Academic literature on the topic 'Monthly forecast'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Monthly forecast.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Monthly forecast"
1
Lavaysse, C., J. Vogt, and F. Pappenberger. "Early warning of drought in Europe using the monthly ensemble system from ECMWF." Hydrology and Earth System Sciences 19, no. 7 (July 28, 2015): 3273–86. http://dx.doi.org/10.5194/hess-19-3273-2015.
Full textAbstract:
Abstract. Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI-1) is used to quantify the onset or likely evolution of ongoing droughts for the next month. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40 % of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty.
2
Lepore, Chiara, Michael K. Tippett, and John T. Allen. "CFSv2 Monthly Forecasts of Tornado and Hail Activity." Weather and Forecasting 33, no. 5 (September 24, 2018): 1283–97. http://dx.doi.org/10.1175/waf-d-18-0054.1.
Full textAbstract:
Abstract Climate Forecast System, version 2, predictions of monthly U.S. severe thunderstorm activity are analyzed for the period 1982–2016. Forecasts are based on a tornado environmental index and a hail environmental index, which are functions of monthly averaged storm relative helicity (SRH), convective precipitation (cPrcp), and convective available potential energy (CAPE). Overall, forecast indices reproduce well the annual cycle of tornado and hail events. Forecast index biases are mostly negative and caused by environment values that are low east of the Rockies, although forecast CAPE is higher than the reanalysis values over the High Plains. Skill is diagnosed spatially for the indices and their constituents separately. SRH is more skillfully forecast than cPrcp and CAPE, especially during December–June. The spatial patterns of forecast skill for CAPE and cPrcp are similar, with higher skill for CAPE and less spatial coherence for cPrcp. The indices are forecast with substantially less skill than the environmental parameters. Numbers of tornado and hail events are forecast with modest but statistically significant skill in some NOAA regions and months of the year. Skill tends to be relatively higher for hail events and in climatologically active seasons and regions. Much of the monthly skill appears to be derived from the first 2 weeks of the forecast. El Niño–Southern Oscillation (ENSO) modulates forecasts and, to a lesser extent, forecast skill, during March–May, with more activity and higher skill during cool ENSO conditions.
3
Lavaysse, C., J. Vogt, and F. Pappenberger. "Early warning of drought in Europe using the monthly ensemble system from ECMWF." Hydrology and Earth System Sciences Discussions 12, no. 2 (February 13, 2015): 1973–2009. http://dx.doi.org/10.5194/hessd-12-1973-2015.
Full textAbstract:
Abstract. Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from ECMWF in predicting droughts over the European continent. The Standardized Precipitation Index (SPI) is used to quantify the onset and severity of droughts. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast whilst both outperform climatology. No significant spatial or temporal patterns can be observed but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40% of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty.
4
Lee, Cai Lin, and Dong Mei Wang. "Monthly Runoff Probabilistic Forecast Model Based on Similar Process Derivations." Applied Mechanics and Materials 737 (March 2015): 710–14. http://dx.doi.org/10.4028/www.scientific.net/amm.737.710.
Full textAbstract:
In this paper, a runoff forecast model combining similar process derivation with probabilistic forecasts is proposed. Certain forecast result is computed by similar processes derivations, and on the basis of certain results, a confidence interval under given confidence coefficient is worked out by probabilistic forecast part. The model is simple in structure, easy in establishing and unnecessary to concern for predictor selections. Applying above model in simulation experiments, the results show the forecast model have excellent forecast accuracy and can be used in monthly runoff forecast effectively.
5
Tippett, Michael K., Laurie Trenary, Timothy DelSole, Kathleen Pegion, and Michelle L. L’Heureux. "Sources of Bias in the Monthly CFSv2 Forecast Climatology." Journal of Applied Meteorology and Climatology 57, no. 5 (May 2018): 1111–22. http://dx.doi.org/10.1175/jamc-d-17-0299.1.
Full textAbstract:
AbstractForecast climatologies are used to remove systematic errors from forecasts and to express forecasts as departures from normal. Forecast climatologies are computed from hindcasts by various averaging, smoothing, and interpolation procedures. Here the Climate Forecast System, version 2 (CFSv2), monthly forecast climatology provided by the NCEP Environmental Modeling Center (EMC) is shown to be biased in the sense of systematically differing from the hindcasts that are used to compute it. These biases, which are unexpected, are primarily due to fitting harmonics to hindcast data that have been organized in a particular format, which on careful inspection is seen to introduce discontinuities. Biases in the monthly near-surface temperature forecast climatology reach 2°C over North America for March targets and start times at the end of January. Biases in the monthly Niño-3.4 forecast climatology are also largest for start times near calendar-month boundaries. A further undesirable consequence of this fitting procedure is that the EMC forecast climatology varies discontinuously with lead time for fixed target month. Two alternative methods for computing the forecast climatology are proposed and illustrated. The proposed methods more accurately fit the hindcast data and provide a clearer representation of the CFSv2 model climate drift toward lower Niño-3.4 values for starts in March and April and toward higher Niño-3.4 values for starts in June, July, and August.
6
Aptukov, Valery N., and Victor Yu Mitin. "STATISTICAL MODELS FOR FORECASTING AVERAGE MONTHLY TEMPERATURE AND MONTHLY PRECIPITATION AMOUNT IN PERM." Географический вестник = Geographical bulletin, no. 2(57) (2021): 84–95. http://dx.doi.org/10.17072/2079-7877-2021-2-84-95.
Full textAbstract:
The article proposes an approach to forecasting mean temperature and total precipitation for the upcoming month, based on the study of the regularities of the influence of statistical characteristics of temperature and precipitation of previous periods on them. Among the predictors, along with the basic statistical characteristics, we use the fractality index which is an indicator of the randomness/ determinism of the climate series. Within the framework of this approach, we have developed models of different levels to predict the temperature and total precipitation amount in the upcoming month. The main parameters of these models are described and the possibilities of their variation are indicated. Examples are given to illustrate the forecasting methodology using various types of models and include the results of quality control of the models, calculation of forecast accuracy and dependence of forecast accuracy of average temperature and precipitation on the month (climate season). When tested in 2020, models for forecasting temperature and precipitation for the upcoming month give good results: 9 correct forecasts of temperature anomalies out of 10 (90%) and 7 correct forecasts of precipitation anomalies out of 9 (77,7%).
7
Fundel, F., S. Jörg-Hess, and M. Zappa. "Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices." Hydrology and Earth System Sciences 17, no. 1 (January 29, 2013): 395–407. http://dx.doi.org/10.5194/hess-17-395-2013.
Full textAbstract:
Abstract. Streamflow droughts, characterized by low runoff as consequence of a drought event, affect numerous aspects of life. Economic sectors that are impacted by low streamflow are, e.g., power production, agriculture, tourism, water quality management and shipping. Those sectors could potentially benefit from forecasts of streamflow drought events, even of short events on the monthly time scales or below. Numerical hydrometeorological models have increasingly been used to forecast low streamflow and have become the focus of recent research. Here, we consider daily ensemble runoff forecasts for the river Thur, which has its source in the Swiss Alps. We focus on the evaluation of low streamflow and of the derived indices as duration, severity and magnitude, characterizing streamflow droughts up to a lead time of one month. The ECMWF VarEPS 5-member ensemble reforecast, which covers 18 yr, is used as forcing for the hydrological model PREVAH. A thorough verification reveals that, compared to probabilistic peak-flow forecasts, which show skill up to a lead time of two weeks, forecasts of streamflow droughts are skilful over the entire forecast range of one month. For forecasts at the lower end of the runoff regime, the quality of the initial state seems to be crucial to achieve a good forecast quality in the longer range. It is shown that the states used in this study to initialize forecasts satisfy this requirement. The produced forecasts of streamflow drought indices, derived from the ensemble forecasts, could be beneficially included in a decision-making process. This is valid for probabilistic forecasts of streamflow drought events falling below a daily varying threshold, based on a quantile derived from a runoff climatology. Although the forecasts have a tendency to overpredict streamflow droughts, it is shown that the relative economic value of the ensemble forecasts reaches up to 60%, in case a forecast user is able to take preventive action based on the forecast.
8
Qiao, Guangchao, Mingxiang Yang, and Xiaoling Zeng. "Monthly-scale runoff forecast model based on PSO-SVR." Journal of Physics: Conference Series 2189, no. 1 (February 1, 2022): 012016. http://dx.doi.org/10.1088/1742-6596/2189/1/012016.
Full textAbstract:
Abstract The current methods used in the Lubbog reservoir runoff forecast generally have shortcomings such as low forecast accuracy and low stability. Aiming at these problems, this paper constructs a PSO-SVR mid-and-long term forecast model, and it uses the particle swarm optimization algorithm (PSO) to find the penalty coefficient C, the insensitivity coefficient ε and the gamma parameter of the Gaussian radial basis kernel function of the support vector regression machine (SVR). The results demonstrates that the average relative errors of the PSO-SVR forecast model is relatively small, which are all within a reasonable range; the qualification rates for most monthly forecasts are above 80%. Experimental results indicate that compared with multiple regression analysis, the PSO-SVR model has a higher forecast accuracy, a stronger stability, and a higher credibility. It has a certain practical value and provides a reference for related research.
9
Wang, Ting, and Xi Miao Jia. "Monthly Load Forecasting Based on Optimum Grey Model." Advanced Materials Research 230-232 (May 2011): 1226–30. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.1226.
Full textAbstract:
Due to the variety and the randomicity of its influencing factors, the monthly load forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on improved GM (1, 1).First, the GM (1, 1) is used to forecast the load data, which takes the longitude historical data as original series, the increment trend of load was forecasted and takes the crosswise historical data as original series, the fluctuation trend of load was forecasted. On this basis the optimum method is led in. An optimal integrated forecasting model is built up. The case calculation results show that the proposed method can remarkably improve the accuracy of monthly load forecasting, and decrease the error. The integrated model this paper describes for short-term load forecasting is available and accurate.
10
Milléo, Carla, and Ricardo Carvalho de Almeida. "Application of RBF artificial neural networks to precipitation and temperature forecasting in Paraná, Brazil." Ciência e Natura 43 (March 1, 2021): e40. http://dx.doi.org/10.5902/2179460x43258.
Full textAbstract:
Precipitation and temperature have an impact on various sectors of society, such as agriculture, power generation, water availability, so it is essential to develop accurate monthly forecasts. The objective of this study is to develop an artificial neural network (ANN) model for monthly temperature and precipitation forecasts for the state of Paraná, Brazil. An important step in the ANN model is the selection of input variables, for which the forward stepwise regression method was used. After identifying the predictor variables for the forecasting model, the Radial Basis Function (RBF) ANN was developed with 50 neurons in the hidden layer and one neuron in the output layer. For the precipitation forecasting models, better performances were obtained for forecasting the data smoothed by the three-month moving average, since noisy data, such as monthly precipitation, are more difficult to be simulated by the neural network. For the temperature forecasts, the ANN model performed well both in the monthly temperature forecast and in the 3-month moving average forecast. This study showed the suitability of forecasting precipitation and temperature with the use of RBF ANNs, especially in the forecast of the monthly temperature.
Dissertations / Theses on the topic "Monthly forecast"
1
MENDES, EVANDRO LUIZ. "INTERVENTION MODELS TO FORECAST MONTHLY DEMAND OF ELETRIC ENERGY, CONSIDERING THE RATIONING SCENERY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3336@1.
Full textAbstract:
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICONesta dissertação é desenvolvida uma metodologia para previsão de demanda mensal de energia elétrica considerando cenários de racionamento. A metodologia usada consiste em, a partir das taxas de crescimento da série temporal, identificar e eliminar os efeitos do racionamento de energia elétrica através da aplicação de Modelos Lineares Dinâmicos. São analisadas também estruturas de intervenção nos modelos estatísticos de Box & Jenkins e Holt & Winters. Os modelos são então comparados segundo alguns critérios, basicamente no que tange à sua eficiência preditiva. Conclui-se ao final sobre a eficiência da metodologia proposta, dado a grande dificuldade para solucionar o problema a partir dos modelos estatísticos de Box & Jenkins e Holt & Winters. Esta solução é então proposta como a mais viável para criar cenários de racionamento e pósracionamento de energia para ser utilizado por agentes do sistema elétrico nacional.
In this dissertation, a methodology is developed to forecast monthly demand of electric energy, considering the rationing scenery. The methodology is based on, taking the growth rate from the time series, identify and eliminate the effects of electric energy rationing, using Dynamic Linear Models. It is also analyzed intervention structures in the statistics models of Box & Jenkins and Holt & Winters. The models are compared according to some criterions, mainly forecast accuracy. At the end, we concluded that the methodology proposed is more efficient, due to the difficult to solve the problem using the statistics models with intervention. This solution is proposed as the best among them to create scenery during the energy rationing and after energy rationing, to be used by the national electric system agents.
2
Robertson, Fredrik, and Max Wallin. "Forecasting monthly air passenger flows from Sweden : Evaluating forecast performance using the Airline model as benchmark." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242764.
Full textAbstract:
In this paper two different models for forecasting the number of monthly departing passengers from Sweden to any international destination are developed and compared. The Swedish transport agency produces forecasts on a yearly basis, where net export is the only explanatory variable controlled for in the latest report. More profound studies have shown a relevance of controlling for variables such as unemployment rate, oil price and exchange rates. Due to the high seasonality within passenger flows, these forecasts are based on monthly or quarterly data. This paper shows that a seasonal autoregressive integrated moving average model with exogenous input outperforms the benchmark model forecast in seven out of nine months. Thus, controlling for oil price, the SEK/EUR exchange rate and the occurrence of Easter reduces the mean absolute percentage error of the forecasts from 3,27 to 2,83 % on Swedish data.
3
RENDINA, Cristian. "STUDY OF THE IMPACT OF MODELLING SEA SURFACE TEMPERATURE IN A MONTHLY ATMOSPHERIC ENSEMBLE PREDICTION SYSTEM." Doctoral thesis, Università degli studi di Ferrara, 2012. http://hdl.handle.net/11392/2389449.
Full textAbstract:
Atmospheric monthly forecast is intermediate between medium range forecast, an initial value
problem, and seasonal forecast, a boundary value problem. The influence of sea surface temperature
(SST) on the atmospheric dynamics in the time range of 10-40 days is still not well understood. As a
consequence, there is no common approach for the representation of the SST in a monthly prediction
system. At ISAC-CNR in Bologna a monthly ensemble forecasting system is run experimentally once a
month, based on the GLOBO model. GLOBO is an atmospheric general circulation model developed at
ISAC. The evolution of SST is represented by a simple slab ocean model based on surface flux balance
with a relaxation term to climatological SST. Recently, a new definition of the slab ocean model which
includes a flux correction term has been implemented to improve the SST simulation. It has been tested
in parallel with the operational forecast for some months of 2011. The results show that the globally
averaged root mean square forecast error of the SST simulated with the new model is slightly larger
than the operational one. However, the ensemble spread of the SST predicted with the new model
increases significantly and becomes very similar to the observed SST variability, in particular in the
Northern Hemisphere. The atmospheric field differences between the new and operational forecasts
show that SST has an impact in the second part of the month, especially in the Southern Hemisphere.
The ensemble spread of atmospheric parameters shows a slight increase using the new slab model.
However, its impact on the anomaly forecast fields is small.
4
Aider, Rabah. "Skill of monthly and seasonal forecasts using a Canadian general circulation model." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32296.
Full textAbstract:
An analysis of the co-variability of surface air temperature and precipitation over North America and Pacific SST is conducted using an SVD analysis. The leading SVD mode revealed a strong link between November SST anomalies and winter surface air temperature (SAT) and precipitation anomalies. In summer this relationship is much weaker. In winter GCM3 captured well the Pacific SST forcing and its response, particularly on the SAT pattern, but its response is less accurate in the summer. The monthly and seasonal forecasts using GCM3 are also analyzed. Precipitation forecasts showed little skill, especially in the warm season where the SST forcing is weak. Furthermore, GCM3 has low seasonal predictive skill in forecasting drought events over the Canadian prairies. However, the model has generally good predictive skill for 500 hPa heights and SAT, with higher scores in the winter. The skill is concentrated in the first month of the prediction period and decreases as the lead time is extended to one month.Une analyse de la co-variabilité entre la température de l'air au sol (SAT) ainsi que les précipitations en Amérique du Nord et la température de l'océan Pacifique à la surface (SST), a été faite en utilisant la méthode SVD. Le mode dominant de la SVD a révélé une relation forte entre les anomalies de la SST du mois de novembre et celles de la SAT et des précipitations hivernales. Ce lien est beaucoup plus faible en été. Le modèle GCM3 reproduit assez bien la réponse au forçage de la SST, particulièrement sur les patrons de la SAT, mais sa réponse est beaucoup moins précise en été. Les prévisions mensuelles et saisonnières de GCM3 ont aussi été analysées. Les capacités de GCM3 à prévoir les précipitations sont faibles, surtout en été où le forçage de la SST est aussi faible. De plus, le modèle ne possède pas d'habiletés notables à prédire les sécheresses dans les prairies Canadiennes. Par contre, les capacités prévisionnelles du modèle concernant la SAT et le géopotentiel à 500 hPa sont généralement assez élevées, particulièrement en hivers. Les habiletés de GCM3 sont concentrées dans le premier mois de la période de prévision, puis déclinent lorsque le délai d'émission est prolongé.
5
Kim, Young-Oh. "The value of monthly and seasonal forecasts in Bayesian stochastic dynamic programming /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/10142.
Full text
6
Maitaria, Kazungu. "ENABLING HYDROLOGICAL INTERPRETATION OF MONTHLY TO SEASONAL PRECIPITATION FORECASTS IN THE CORE NORTH AMERICAN MONSOON REGION." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/193926.
Full textAbstract:
The aim of the research undertaken in this dissertation was to use medium-range to seasonal precipitation forecasts for hydrologic applications for catchments in the core North American Monsoon (NAM) region. To this end, it was necessary to develop a better understanding of the physical and statistical relationships between runoff processes and the temporal statistics of rainfall. To achieve this goal, development of statistically downscaled estimates of warm season precipitation over the core region of the North American Monsoon Experiment (NAME) were developed. Currently, NAM precipitation is poorly predicted on local and regional scales by Global Circulation Models (GCMs). The downscaling technique used here, the K-Nearest Neighbor (KNN) model, combines information from retrospective GCM forecasts with simultaneous historical observations to infer statistical relationships between the low-resolution GCM fields and the locally-observed precipitation records. The stochastic nature of monsoon rainfall presents significant challenges for downscaling efforts and, therefore, necessitate a regionalization and an ensemble or probabilistic-based approach to quantitative precipitation forecasting. It was found that regionalization of the precipitation climatology prior to downscaling using KNN offered significant advantages in terms of improved skill scores.Selected output variables from retrospective ensemble runs of the National Centers for Environmental Predictions medium-range forecast (MRF) model were fed into the KNN downscaling model. The quality of the downscaled precipitation forecasts was evaluated in terms of a standard suite of ensemble verification metrics. This study represents the first time the KNN model has been successfully applied within a warm season convective climate regime and shown to produce skillful and reliable ensemble forecasts of daily precipitation out to a lead time of four to six days, depending on the forecast month.Knowledge of the behavior of the regional hydrologic systems in NAM was transferred into a modeling framework aimed at improving intra-seasonal hydrologic predictions. To this end, a robust lumped-parameter computational model of intermediate conceptual complexity was calibrated and applied to generate streamflow in three unregulated test basins in the core region of the NAM. The modeled response to different time-accumulated KNN-generated precipitation forcing was investigated. Although the model had some difficulty in accurately simulating hydrologic fluxes on the basis of Hortonian runoff principles only, the preliminary results achieved from this study are encouraging. The primary and most novel finding from this study is an improved predictability of the NAM system using state-of-the-art ensemble forecasting systems. Additionally, this research significantly enhanced the utility of the MRF ensemble forecasts and made them reliable for regional hydrologic applications. Finally, monthly streamflow simulations (from an ensemble-based approach) have been demonstrated. Estimated ensemble forecasts provide quantitative estimates of uncertainty associated with our model forecasts.
7
Tennant, Warren James. "A monthly forecast strategy for Southern Africa." Thesis, 1998. https://hdl.handle.net/10539/26794.
Full textAbstract:
Dissertation submitted to the Faculty of Science,
University of the Witwatersrand, Johannesburg
for the Degree of Master of ScienceVarious techniques and procedures suited to monthly forecasting are investigated and tested. These include using the products generated by atmospheric general circulation models during a 17-year hindcast experiment, and downscaling the forecast circulation to regional rainfall in South Africa using circulation indices and canonical correlation analysis. The downscaling methods are evaluated using the cross-validation technique. Various model forecast bias-correction methods and skill-enhancing ensemble techniques are employed to improve the 30-day prognosis of the model. Forecasts from the general circulation model and each technique are evaluated. Those demonstrating reasonable skill over the southern Africa region, and which are feasible when considering available resources, are adopted into a strategy which can be used operationally to produce monthly outlooks. Various practical issues regarding the operational aspects of long-term forecasting are also discussed.
Andrew Chakane 2019
8
Pei-MinZhao and 趙培珉. "Dry Season Monthly Rainfall Forecast for Tseng-Wen Reservoir Catchment." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/17441733305543664412.
Full text
9
Tseng, Pin-Han, and 曾品涵. "A development of statistic forecast system for pentad to monthly scales prediction." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28490115029542569801.
Full textAbstract:
碩士國立中央大學
大氣物理研究所
98
ABSTRACT The main purpose of this research is to develop a statistic forecast system for pentad to monthly scales prediction. The basic structure of this system was built by the persistence neutralization method and the linear regressive model. The persistence neutralization method filtered out the persistence of variables to distinguish the relationship between lead time and lag time. It had better performance than the persistence forecast. At first, the persistence neutralization method was used to transform the variables of predictand for neutralizing the persistence effect in climate data. Then, the predictive predictors were picked out by using the linear regressive model to develop a statistic forecast system for pentad to monthly scales prediction. 60 climate variables were used, including the outgoing longwave radiation (OLR), sea surface temperature (SST), estimated precipitation version1 (Precip), and mean sea level pressure (mslp), etc. Because each variable had different seasonal influence, the annual data were divided into six periods to construct the prediction system. First, we used the persistence neutralization method and the linear regressive model to neutralize and filter out of the persistence effect in 60 kinds of climate variables. The OLR field was used to be predictand and all 60 climate variables were used to be predictors. Each predictors had different predictive skill in different periods. We calculated the correlation coefficient and root mean square errors between OLR (predictand) and all climate variables. The spacial distribution of correlation coefficient between 40oS and 40oN was exhibited the relationship between predictand and predictors. 11 variables were selected in January and February. The correlation coefficient was more than 0.8 over the tropical Eastern Pacific and exceeded 0.6 in the north of Australia, Indonesia, Philippine, and South China Sea. In March and April, the correlation coefficient was more than 0.8 from the date line to 70oW on tropical Eastern Pacific and was about 0.6 near 120oE from 10oN to the Equator. In May and June, the correlation coefficient was 0.7 near 120oW on tropical Pacific Ocean, from 160oE to 70oW in Pacific Ocean, South America, and Australia. There was more than 0.8 in South Africa. High correlation exited from 0oE to 60oE and 40oN to 20oS in July and August. In September and October, the correlation coefficient was more than 0.7 from 120oE to 0oE and 40oN to 20oS and was 0.6 near 20oS in South America. The correlation coefficient in November and December were similar to September and October. But the atmos column precipitation water and absolute vorticity on 850hPa showed the best predictive skill to predict OLR. The high correlation areas between predictand and each predictor were dissimilar in different periods, but displayed consistency in same period.
10
Lai, Chia Liang, and 賴佳良. "Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/23171218166774438081.
Full textAbstract:
碩士國立清華大學
工業工程與工程管理學系
104
The information from electricity demand forecasting helps energy generation enterprises develop an electricity supply system. This study aims to develop a monthly electricity forecasting model to predict the electricity demand for energy management. Given that the influence of weather factors, such as temperature and humidity, is diluted in the overall monthly electricity demand, the forecasting model uses historical electricity consumption data as an integrated factor to obtain future prediction. The proposed approach is applied to a monthly electricity demand time series forecasting model that includes trend and fluctuation series, of which the former describes the trend of the electricity demand series and the latter describes the periodic fluctuation imbedded in the trend. An integrated genetic algorithm and neural network model (GANN) is then trained to forecast the trend series. Given that the fluctuation series demonstrates an oscillatory behavior, we apply Fourier series to fit the fluctuation series. The complete demand model is named GANN–Fourier series. U.S. electricity demand data are used to evaluate the proposed model and to compare the results of applying this model with those of using conventional neural networks.
Books on the topic "Monthly forecast"
1
Wehelie, Yassin Jeyte. Maize price seasonality: An analysis of monthly retail maize prices in Mogadishu from January 1979 to December 1986 (with 1987 monthly maize forecast prices). [Mogadishu]: Ministry of Agriculture, Planning Directorate, 1987.
Find full text
2
Fielder, Lonnie L. Analysis, forecasts, and seasonal patterns of monthly prices and quantities, Louisiana farm products. Baton Rouge, La: Dept. of Agricultural Economics and Agribusiness, Louisiana Agricultural Experiment Station, 1985.
Find full text
3
plc, Waste Recycling Group. Merger with Yorkshire Environmental Global Waste Management: Interim results for the six months ended 30 June 1998 : profit forecast. Norwich: Waste Recycling Group, 1998.
Find full text
4
Staff, Insignia Accounts. Monthly Cash Forecast Template. Independently Published, 2017.
Find full text
5
Staff, Insignia Accounts. Monthly Template Cash Flow Forecast. Independently Published, 2017.
Find full text
6
Staff, Insignia Accounts. Monthly Cash Flow Forecast Template. Independently Published, 2017.
Find full text
7
Staff, Insignia Accounts. Monthly Cash Flow Forecast Spreadsheet. Independently Published, 2017.
Find full text
8
Staff, Insignia Accounts. Monthly Cash Flow Forecast Format. Independently Published, 2017.
Find full text
9
Staff, Insignia Accounts. Monthly Template for Cash Flow Forecast. Independently Published, 2017.
Find full text
10
Lazuli, Lisa. Aquarius Horoscope 2019: Yearly and Monthly Astrology Forecast. Independently Published, 2019.
Find full textBook chapters on the topic "Monthly forecast"
1
Mounter, William, Huda Dawood, and Nashwan Dawood. "The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression." In Springer Proceedings in Energy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63916-7_9.
Full textAbstract:
AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.
2
Araujo, Ruben, Meuser Valenca, and Sergio Fernandes. "A New Approach of Fuzzy Neural Networks in Monthly Forecast of Water Flow." In Advances in Computational Intelligence, 576–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19258-1_47.
Full text
3
Wu, Shanshan, Xiang Wang, and Hengyue Hou. "Monthly Power Consumption Forecast of the Whole Society Based on Mixed Data Sampling Model." In Lecture Notes in Electrical Engineering, 662–68. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8052-6_82.
Full text
4
Akeh, Ugbah Paul, Steve Woolnough, and Olumide A. Olaniyan. "ECMWF Subseasonal to Seasonal Precipitation Forecast for Use as a Climate Adaptation Tool Over Nigeria." In African Handbook of Climate Change Adaptation, 1613–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_97.
Full textAbstract:
AbstractFarmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seasonal (S2S) precipitation forecast to ascertain its usefulness as a climate change adaptation tool over Nigeria. Observed daily and monthly CHIRPS reanalysis precipitation amount and the ECMWF subseasonal weekly precipitation forecast data for the period 1995–2015 was used. The forecast and observed precipitation were analyzed from May to September while El Nino and La Nina years were identified using the Oceanic Nino Index. Skill of the forecast was determined from standard metrics: Bias, Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC).The Bias, RMSE, and ACC scores reveal that the ECMWF model is capable of predicting precipitation over Southern Nigeria, with the best skill at one week lead time and poorest skills at lead time of 4 weeks. Results also show that the model is more reliable during El Nino years than La-Nina. However, some improvement in the model by ECMWF can give better results and make this tool a more dependable tool for disaster risk preparedness, reduction and prevention of possible damages and losses from extreme rainfall during the wet season, thus enhancing climate change adaptation.
5
Martinho, A. D., T. L. Fonseca, and L. Goliatt. "Automated Extreme Learning Machine to Forecast the Monthly Flows: A Case Study at Zambezi River." In Advances in Intelligent Systems and Computing, 1314–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71187-0_122.
Full text
6
Amoo, Oseni Taiwo, Hammed Olabode Ojugbele, Abdultaofeek Abayomi, and Pushpendra Kumar Singh. "Hydrological Dynamics Assessment of Basin Upstream–Downstream Linkages Under Seasonal Climate Variability." In African Handbook of Climate Change Adaptation, 2005–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_116.
Full textAbstract:
AbstractThe impacts of climate change are already being felt, not only in terms of increase in temperature but also in respect of inadequate water availability. The Mkomazi River Basins (MRB) of the KwaZulu-Natal region, South Africa serves as major source of water and thus a mainstay of livelihood for millions of people living downstream. It is in this context that the study investigates water flows abstraction from headwaters to floodplains and how the water resources are been impacted by seasonal climate variability. Artificial Neural Network (ANN) pattern classifier was utilized for the seasonal classification and subsequence hydrological flow regime prediction between the upstream–downstream anomalies. The ANN input hydroclimatic data analysis results covering the period 2008–2015 provides a likelihood forecast of high, near-median, or low streamflow. The results show that monthly mean water yield range is 28.6–36.0 m3/s over the Basin with a coefficient of correlation (CC) values of 0.75 at the validation stage. The yearly flow regime exhibits considerable changes with different magnitudes and patterns of increase and decrease in the climatic variables. No doubt, added activities and processes such as land-use change and managerial policies in upstream areas affect the spatial and temporal distribution of available water resources to downstream regions. The study has evolved an artificial neuron system thinking from conjunctive streamflow prediction toward sustainable water allocation planning for medium- and long-term purposes.
7
Arndt, Channing, and Kenneth Foster. "Forecasts of Monthly U.S. Wheat Prices: A Spatial Market Analysis." In Applications of Computer Aided Time Series Modeling, 91–105. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2252-1_4.
Full text
8
Douglas, James W., and Ringa Raudla. "CBO Updated Forecasts: Do a Few Months Matter?" In The Palgrave Handbook of Government Budget Forecasting, 133–52. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18195-6_7.
Full text
9
Barbaglia, Luca, Sergio Consoli, and Sebastiano Manzan. "Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting." In Mining Data for Financial Applications, 135–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66981-2_11.
Full textAbstract:
AbstractForecasting economic and financial variables is a challenging task for several reasons, such as the low signal-to-noise ratio, regime changes, and the effect of volatility among others. A recent trend is to extract information from news as an additional source to forecast economic activity and financial variables. The goal is to evaluate if news can improve forecasts from standard methods that usually are not well-specified and have poor out-of-sample performance. In a currently on-going project, our goal is to combine a richer information set that includes news with a state-of-the-art machine learning model. In particular, we leverage on two recent advances in Data Science, specifically on Word Embedding and Deep Learning models, which have recently attracted extensive attention in many scientific fields. We believe that by combining the two methodologies, effective solutions can be built to improve the prediction accuracy for economic and financial time series. In this preliminary contribution, we provide an overview of the methodology under development and some initial empirical findings. The forecasting model is based on DeepAR, an auto-regressive probabilistic Recurrent Neural Network model, that is combined with GloVe Word Embeddings extracted from economic news. The target variable is the spread between the US 10-Year Treasury Constant Maturity and the 3-Month Treasury Constant Maturity (T10Y3M). The DeepAR model is trained on a large number of related GloVe Word Embedding time series, and employed to produce point and density forecasts.
10
Howrey, E. Philip. "New Methods for Using Monthly Data to Improve Forecast Accuracy." In Comparative Performance of U.S. Econometric Models, 227–49. Oxford University Press, 1991. http://dx.doi.org/10.1093/acprof:oso/9780195057720.003.0008.
Full textConference papers on the topic "Monthly forecast"
1
Guo, Huifang, Zengchuan Dong, Xin Chen, Xixia Ma, and Peiyan Zhang. "WANN Model for Monthly Runoff Forecast." In 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop (KAM 2008 Workshop). IEEE, 2008. http://dx.doi.org/10.1109/kamw.2008.4810682.
Full text
2
Sharma, Ashutosh, and Manish Kumar Goyal. "Bayesian network model for monthly rainfall forecast." In 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2015. http://dx.doi.org/10.1109/icrcicn.2015.7434243.
Full text
3
Berriel, Rodrigo F., Andre Teixeira Lopes, Alexandre Rodrigues, Flavio Miguel Varejao, and Thiago Oliveira-Santos. "Monthly energy consumption forecast: A deep learning approach." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966398.
Full text
4
Cotrina-Teatino, Marco Antonio, Jairo Jhonatan Marquina Araujo, Eduardo Manuel Noriega Vidal, Juan Antonio Vega Gonzalez, Solio Marino Arango Retamozo, Hans Roger Portilla Rodriguez, and Aldo Roger Castillo Chung. "Copper Monthly Price Forecast with Time Series Models." In 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 2022): “Exponential Technologies and Global Challenges: Moving toward a new culture of entrepreneurship and innovation for sustainable development”. Latin American and Caribbean Consortium of Engineering Institutions, 2022. http://dx.doi.org/10.18687/leird2022.1.1.6.
Full text
5
Qifeng, Xu, Wang Qiang, Yao Zhilin, and Liu Shufen. "The Monthly Electricity Load Forecast Based on Composite Model." In 2015 7th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2015. http://dx.doi.org/10.1109/itme.2015.57.
Full text
6
Beiraghi, M., and A. M. Ranjbar. "Discrete Fourier Transform Based Approach to Forecast Monthly Peak Load." In 2011 Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2011. http://dx.doi.org/10.1109/appeec.2011.5748585.
Full text
7
Chia-Liang Lai and Hsiao-Fan Wang. "Application of soft computing techniques to forecast monthly electricity demand." In 2015 International Conference on Industrial Engineering and Operations Management (IEOM). IEEE, 2015. http://dx.doi.org/10.1109/ieom.2015.7093922.
Full text
8
Dilini, W. M. N., Dilhari Attygalle, Liwan Liyanage Hansen, and K. D. W. Nandalal. "Ensemble Forecast for monthly Reservoir Inflow; A Dynamic Neural Network Approach." In Annual International Conference on Operations Research and Statistics ( ORS 2016 ). Global Science & Technology Forum ( GSTF ), 2016. http://dx.doi.org/10.5176/2251-1938_ors16.22.
Full text
9
Shabri, Ani, and Ruhaidah Samsudin. "Application of Improved GM(1,1) Models in Seasonal Monthly Tourism Demand Forecast." In 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2019. http://dx.doi.org/10.1109/aidas47888.2019.8970945.
Full text
10
Nan, Linjiang, Mingxiang Yang, Jianqiu Li, Ningpeng Dong, and Hejia Wang. "Monthly Precipitation Forecast of Jiulong River Basin Based on Association Rule Analysis." In 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE, 2021. http://dx.doi.org/10.1109/icitbs53129.2021.00210.
Full textReports on the topic "Monthly forecast"
1
Wendy, Disch. ESRI Nowcast October 2022. ESRI, October 2022. http://dx.doi.org/10.26504/ir1.
Full textAbstract:
Using the ESRI Nowcasting model currently employed to support the forecasting exercise in the Quarterly Economic Commentary, we will now update our forecast of modified domestic demand (MDD) on a monthly basis. Our nowcast estimates that MDD is expected to grow by 4.1 per cent in Q3 2022 on an annual basis, indicating some moderation from its growth of 10.2 per cent per annum in Q2 2022. On a monthly basis, MDD is estimated to be 4.0 per cent above its level from August 2021. Strong tax receipts, continued strength in the labour market and growth in industrial production are all contributing to growth. However, continued uncertainty in the global economy and elevated inflation rates have contributed to a decline in survey indicators, such as business and consumer sentiment, as well as slowdowns in retail sales. While growth of 4.1 per cent is strong on a historical basis, it is clear that MDD is moderating considerably from its peak of 14 per cent in February 2022.
2
De Castro-Valderrama, Marcela, Santiago Forero-Alvarado, Nicolás Moreno-Arias, and Sara Naranjo-Saldarriaga. Unraveling the Exogenous Forces Behind Analysts' Macroeconomic Forecasts. Banco de la República, December 2021. http://dx.doi.org/10.32468/be.1184.
Full textAbstract:
Modern macroeconomics focuses on the identification of the primitive exogenous forces generating business cycles. This is at odds with macroeconomic forecasts collected through surveys, which are about endogenous variables. To address this divorce, our paper uses a general equilibrium model as a multivariate filter to infer the shocks behind market analysts' forecasts and thus, unravel their implicit macroeconomic stories. By interpreting all analysts' forecasts through the same lenses, it is possible to understand the differences between projected endogenous variables as differences in the types and magnitudes of shocks. It also allows to explain market's uncertainty about the future in terms of analysts' disagreement about these shocks. The usefulness of the approach is illustrated by adapting the canonical SOE semi-structural model in Carabenciov et al. (2008a) to Colombia and then using it to filter forecasts of its Central Bank's Monthly Expectations Survey during the COVID-19 crisis.
3
Venäläinen, Ari, Sanna Luhtala, Mikko Laapas, Otto Hyvärinen, Hilppa Gregow, Mikko Strahlendorff, Mikko Peltoniemi, et al. Sää- ja ilmastotiedot sekä uudet palvelut auttavat metsäbiotaloutta sopeutumaan ilmastonmuutokseen. Finnish Meteorological Institute, January 2021. http://dx.doi.org/10.35614/isbn.9789523361317.
Full textAbstract:
Climate change will increase weather induced risks to forests, and thus effective adaptation measures are needed. In Säätyö project funded by the Ministry of Agriculture and Forestry, we have summarized the data that facilitate adaptation measures, developed weather and climate services that benefit forestry, and mapped what kind of new weather and climate services are needed in forestry. In addition, we have recorded key further development needs to promote adaptation. The Säätyö project developed a service product describing the harvesting conditions of trees based on the soil moisture assessment. The output includes an analysis of the current situation and a 10-day forecast. In the project we also tested the usefulness of long forecasts beyond three months. The weather forecasting service is sidelined and supplemented by another co-operation project between the Finnish Meteorological Institute and Metsäteho called HarvesterSeasons (https://harvesterseasons.com/). The HarvesterSeasons service utilizes long-term forecasts of up to 6 months to assess terrain bearing conditions. A test version of a wind damage risk tool was developed in cooperation with the Department of Forest Sciences of the University of Eastern Finland and the Finnish Meteorological Institute. It can be used to calculate the wind speeds required in a forest area for wind damage (falling trees). It is currently only suitable for researcher use. In the Säätyö project the possibility of locating the most severe wind damage areas immediately after a storm was also tested. The method is based on the spatial interpolation of wind observations. The method was used to analyze storms that caused forest damages in the summer and fall of 2020. The produced maps were considered illustrative and useful to those responsible for compiling the situational picture. The accumulation of snow on tree branches, can be modeled using weather data such as rainfall, temperature, air humidity, and wind speed. In the Säätyö project, the snow damage risk assessment model was further developed in such a way that, in addition to the accumulated snow load amount, the characteristics of the stand and the variations in terrain height were also taken into account. According to the verification performed, the importance of abiotic factors increased under extreme snow load conditions (winter 2017-2018). In ordinary winters, the importance of biotic factors was emphasized. According to the comparison, the actual snow damage could be explained well with the tested model. In the interviews and workshop, the uses of information products, their benefits, the conditions for their introduction and development opportunities were mapped. According to the results, diverse uses and benefits of information products and services were seen. Information products would make it possible to develop proactive forest management, which would reduce the economic costs caused by wind and snow damages. A more up-to-date understanding of harvesting conditions, enabled by information products, would enhance the implementation of harvesting and harvesting operations and the management of timber stocks, as well as reduce terrain, trunk and root damage. According to the study, the introduction of information is particularly affected by the availability of timeliness. Although the interviewees were not currently willing to pay for the information products developed in the project, the interviews highlighted several suggestions for the development of information products, which could make it possible to commercialize them.
4
Monetary Policy Report - April 2022. Banco de la República, June 2022. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr2-2022.
Full textAbstract:
Macroeconomic summary Annual inflation continued to rise in the first quarter (8.5%) and again outpaced both market expectations and the technical staff’s projections. Inflation in major consumer price index (CPI) baskets has accelerated year-to-date, rising in March at an annual rate above 3%. Food prices (25.4%) continued to contribute most to rising inflation, mainly affected by a deterioration in external supply and rising costs of agricultural inputs. Increases in transportation prices and in some utility rates (energy and gas) can explain the acceleration in regulated items prices (8.3%). For its part, the increase in inflation excluding food and regulated items (4.5%) would be the result of shocks in supply and external costs that have been more persistent than expected, the effects of indexation, accumulated inflationary pressures from the exchange rate, and a faster-than-anticipated tightening of excess productive capacity. Within the basket excluding food and regulated items, external inflationary pressures have meaningfully impacted on goods prices (6.4%), which have been accelerating since the last quarter of 2021. Annual growth in services prices (3.8%) above the target rate is due primarily to food away from home (14.1%), which was affected by significant increases in food and utilities prices and by a rise in the legal monthly minimum wage. Housing rentals and other services prices also increased, though at rates below 3%. Forecast and expected inflation have increased and remain above the target rate, partly due to external pressures (prices and costs) that have been more persistent than projected in the January report (Graphs 1.1 and 1.2). Russia’s invasion of Ukraine accentuated inflationary pressures, particularly on international prices for certain agricultural goods and inputs, energy, and oil. The current inflation projection assumes international food prices will increase through the middle of this year, then remain high and relatively stable for the remainder of 2022. Recovery in the perishable food supply is forecast to be less dynamic than previously anticipated due to high agricultural input prices. Oil prices should begin to recede starting in the second half of the year, but from higher levels than those presented in the previous report. Given the above, higher forecast inflation could accentuate indexation effects and increase inflation expectations. The reversion of a rebate on value-added tax (VAT) applied to cleaning and hygiene products, alongside the end of Colombia’s COVID-19 health emergency, could increase the prices of those goods. The elimination of excess productive capacity on the forecast horizon, with an output gap close to zero and somewhat higher than projected in January, is another factor to consider. As a consequence, annual inflation is expected to remain at high levels through June. Inflation should then decline, though at a slower pace than projected in the previous report. The adjustment process of the monetary policy rate wouldcontribute to pushing inflation and its expectations toward the target on the forecast horizon. Year-end inflation for 2022 is expected to be around 7.1%, declining to 4.8% in 2023. Economic activity again outperformed expectations. The technical staff’s growth forecast for 2022 has been revised upward from 4.3% to 5% (Graph 1.3). Output increased more than expected in annual terms in the fourth quarter of 2021 (10.7%), driven by domestic demand that came primarily because of private consumption above pre-pandemic levels. Investment also registered a significant recovery without returning to 2019 levels and with mixed performance by component. The trade deficit increased, with significant growth in imports similar to that for exports. The economic tracking indicator (ISE) for January and February suggested that firstquarter output would be higher than previously expected and that the positive demand shock observed at the end of 2021 could be fading slower than anticipated. Imports in consumer goods, retail sales figures, real restaurant and hotel income, and credit card purchases suggest that household spending continues to be dynamic, with levels similar to those registered at the end of 2021. Project launch and housing starts figures and capital goods import data suggest that investment also continues to recover but would remain below pre-pandemic levels. Consumption growth is expected to decelerate over the year from high levels reached over the last two quarters. This would come amid tighter domestic and external financial conditions, the exhaustion of suppressed demand, and a deterioration of available household income due to increased inflation. Investment is expected to continue to recover, while the trade deficit should tighten alongside high oil and other export commodity prices. Given all of the above, first-quarter economic growth is now expected to be 7.2% (previously 5.2%) and 5.0% for 2022 as a whole (previously 4.3%). Output growth would continue to moderate in 2023 (2.9%, previously 3.1%), converging similar to long-term rates. The technical staff’s revised projections suggest that the output gap would remain at levels close to zero on the forecast horizon but be tighter than forecast in January (Graph 1.4). These estimates continue to be affected by significant uncertainty associated with geopolitical tensions, external financial conditions, Colombia’s electoral cycle, and the COVID-19 pandemic. External demand is now projected to grow at a slower pace than previously expected amid increased global inflationary pressures, high oil prices, and tighter international financial conditions than forecast in January. The Russian invasion of Ukraine and its inflationary effects on prices for oil and certain agricultural goods and inputs accentuated existing global inflationary pressures originating in supply restrictions and increased international costs. A decline in the supply of Russian oil, low inventory levels, and continued production limits on behalf of the Organization of Petroleum Exporting Countries and its allies (OPEC+) can explain increased projected oil prices for 2022 (USD 100.8/barrel, previously USD 75.3) and 2023 (USD 86.8/barrel, previously USD 71.2). The forecast trajectory for the U.S. Federal Reserve (Fed) interest rate has increased for this and next year to reflect higher real and expected inflation and positive performance in the labormarket and economic activity. The normalization of monetary policy in various developed and emerging market economies, more persistent supply and cost shocks, and outbreaks of COVID-19 in some Asian countries contributed to a reduction in the average growth outlook for Colombia’s trade partners for 2022 (2.8%, previously 3.3%) and 2023 (2.4%, previously 2.6%). In this context, the projected path for Colombia’s risk premium increased, partly due to increased geopolitical global tensions, less expansionary monetary policy in the United States, an increase in perceived risk for emerging markets, and domestic factors such as accumulated macroeconomic imbalances and political uncertainty. Given all the above, external financial conditions are tighter than projected in January report. External forecasts and their impact on Colombia’s macroeconomic scenario continue to be affected by considerable uncertainty, given the unpredictability of both the conflict between Russia and Ukraine and the pandemic. The current macroeconomic scenario, characterized by high real inflation levels, forecast and expected inflation above 3%, and an output gap close to zero, suggests an increased risk of inflation expectations becoming unanchored. This scenario offers very limited space for expansionary monetary policy. Domestic demand has been more dynamic than projected in the January report and excess productive capacity would have tightened more quickly than anticipated. Headline and core inflation rose above expectations, reflecting more persistent and important external shocks on supply and costs. The Russian invasion of Ukraine accentuated supply restrictions and pressures on international costs. This partly explains the increase in the inflation forecast trajectory to levels above the target in the next two years. Inflation expectations increased again and are above 3%. All of this increased the risk of inflation expectations becoming unanchored and could generate indexation effects that move inflation still further from the target rate. This macroeconomic context also implies reduced space for expansionary monetary policy. 1.2 Monetary policy decision Banco de la República’s board of directors (BDBR) continues to adjust its monetary policy. In its meetings both in March and April of 2022, it decided by majority to increase the monetary policy rate by 100 basis points, bringing it to 6.0% (Graph 1.5).
5
Monetary Policy Report - July de 2021. Banco de la República, October 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr3-2021.
Full textAbstract:
Macroeconomic summary The Colombian economy sustained numerous shocks in the second quarter, pri¬marily related to costs and supply. The majority of these shocks were unantic¬ipated or proved more persistent than expected, interrupting the recovery in economic activity observed at the beginning of the year and pushing overall inflation above the target. Core inflation (excluding food and regulated items) increased but remained low, in line with the technical staff’s expectations. A third wave of the pandemic, which became more severe and prolonged than the previous outbreak, began in early April. This had both a high cost in terms of human life and a negative impact on Colombia's economic recovery. Between May and mid-June roadblocks and other disruptions to public order had a sig¬nificant negative effect on economic activity and inflation. The combination and magnitude of these two shocks likely led to a decline in gross domestic product (GDP) compared to the first quarter. Roadblocks also led to a significant in¬crease in food prices. The accumulated effects of global disruptions to certain value chains and increased international freight transportation prices, which since the end of 2020 have restricted supply and increased costs, also affected Colombia’s economy. The factors described above, which primarily affected the consumer price index (CPI) for goods and foods, explain to a significant degree the technical staff’s forecast errors and the increase in overall inflation above the 3% target. By contrast, increases in core inflation and in prices for regulated items were in line with the technical staff’s expectations, and can be explained largely by the elimination of various price relief measures put in place last year. An increase in perceived sovereign risk and the upward pressures that this im¬plies on international financing costs and the exchange rate were further con¬siderations. Despite significant negative shocks, economic growth in the first half of the year (9.1%) is now expected to be significantly higher than projected in the April re¬port (7.1%), a sign of a more dynamic economy that could recover more quickly than previously forecast. Diverse economic activity figures have indicated high¬er-than-expected growth since the end of 2020. This suggests that the negative effects on output from recurring waves of COVID-19 have grown weaker and less long-lasting with subsequent outbreaks. Nevertheless, the third wave of the coro¬navirus, and to an even greater degree the previously mentioned roadblocks and disruptions to public order, likely led to a decline in GDP in the second quar¬ter compared to the first. Despite this, data from the monthly economic tracking indicator (ISE) for April and May surpassed expectations, and new sector-level measures of economic activity suggest that the negative impact of the pandemic on output continues to moderate, amid reduced restrictions on mobility and im¬provements in the pace of vaccination programs. Freight transportation registers (June) and unregulated energy demand (July), among other indicators, suggest a significant recovery following the roadblocks in May. Given the above, annual GDP growth in the second quarter is expected to have been around 17.3% (previously 15.8%), explained in large part by a low basis of comparison. The technical staff revised its growth projection for 2021 upward from 6% to 7.5%. This forecast, which comes with an unusually high degree of uncertain¬ty, assumes no additional disruptions to public order and that any new waves of COVID-19 will not have significant additional negative effects on economic activity. Recovery in international demand, price levels for some of Colombia’s export com¬modities, and remittances from workers abroad have all performed better than projected in the previous report. This dynamic is expected to continue to drive recovery in the national income over the rest of the year. Continued ample international liquidity, an acceleration in vacci¬nation programs, and low interest rates can also be ex¬pected to favor economic activity. Improved performance in the second quarter, which led to an upward growth revision for all components of spending, is expected to continue, with the economy returning to 2019 production levels at the end of 2021, earlier than estimated in the April report. This forecast continues to account for the short-term effects on aggregate demand of a tax reform package along the lines of what is currently being pro-posed by the national government. Given the above, the central forecast scenario in this report projects growth in 2021 of 7.5% and in 2022 of 3.1% (Graph 1.1). In this scenar¬io, economic activity would nonetheless remain below potential. The noted improvement in these projections comes with a high degree of uncertainty. Annual inflation increased more than expected in June (3.63%) as a result of changes in food prices, while growth in core inflation (1.87%) was similar to projections.
6
Monetary Policy Report - July 2022. Banco de la República, October 2022. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr3-2022.
Full textAbstract:
In the second quarter, annual inflation (9.67%), the technical staff’s projections and its expectations continued to increase, remaining above the target. International cost shocks, accentuated by Russia's invasion of Ukraine, have been more persistent than projected, thus contributing to higher inflation. The effects of indexation, higher than estimated excess demand, a tighter labor market, inflation expectations that continue to rise and currently exceed 3%, and the exchange rate pressures add to those described above. High core inflation measures as well as in the producer price index (PPI) across all baskets confirm a significant spread in price increases. Compared to estimates presented in April, the new forecast trajectory for headline and core inflation increased. This was partly the result of greater exchange rate pressure on prices, and a larger output gap, which is expected to remain positive for the remainder of 2022 and which is estimated to close towards yearend 2023. In addition, these trends take into account higher inflation rate indexation, more persistent above-target inflation expectations, a quickening of domestic fuel price increases due to the correction of lags versus the parity price and higher international oil price forecasts. The forecast supposes a good domestic supply of perishable foods, although it also considers that international prices of processed foods will remain high. In terms of the goods sub-basket, the end of the national health emergency implies a reversal of the value-added tax (VAT) refund applied to health and personal hygiene products, resulting in increases in the prices of these goods. Alternatively, the monetary policy adjustment process and the moderation of external shocks would help inflation and its expectations to begin to decrease over time and resume their alignment with the target. Thus, the new projection suggests that inflation could remain high for the second half of 2022, closing at 9.7%. However, it would begin to fall during 2023, closing the year at 5.7%. These forecasts are subject to significant uncertainty, especially regarding the future behavior of external cost shocks, the degree of indexation of nominal contracts and decisions made regarding the domestic price of fuels. Economic activity continues to outperform expectations, and the technical staff’s growth projections for 2022 have been revised upwards from 5% to 6.9%. The new forecasts suggest higher output levels that would continue to exceed the economy’s productive capacity for the remainder of 2022. Economic growth during the first quarter was above that estimated in April, while economic activity indicators for the second quarter suggest that the GDP could be expected to remain high, potentially above that of the first quarter. Domestic demand is expected to maintain a positive dynamic, in particular, due to the household consumption quarterly growth, as suggested by vehicle registrations, retail sales, credit card purchases and consumer loan disbursement figures. A slowdown in the machinery and equipment imports from the levels observed in March contrasts with the positive performance of sales and housing construction licenses, which indicates an investment level similar to that registered for the first three months of the year. International trade data suggests the trade deficit would be reduced as a consequence of import levels that would be lesser than those observed in the first quarter, and stable export levels. For the remainder of the year and 2023, a deceleration in consumption is expected from the high levels seen during the first half of the year, partially as a result of lower repressed demand, tighter domestic financial conditions and household available income deterioration due to increased inflation. Investment is expected to continue its slow recovery while remaining below pre-pandemic levels. The trade deficit is expected to tighten due to projected lower domestic demand dynamics, and high prices of oil and other basic goods exported by the country. Given the above, economic growth in the second quarter of 2022 would be 11.5%, and for 2022 and 2023 an annual growth of 6.9% and 1.1% is expected, respectively. Currently, and for the remainder of 2022, the output gap would be positive and greater than that estimated in April, and prices would be affected by demand pressures. These projections continue to be affected by significant uncertainty associated with global political tensions, the expected adjustment of monetary policy in developed countries, external demand behavior, changes in country risk outlook, and the future developments in domestic fiscal policy, among others. The high inflation levels and respective expectations, which exceed the target of the world's main central banks, largely explain the observed and anticipated increase in their monetary policy interest rates. This environment has tempered the growth forecast for external demand. Disruptions in value chains, rising international food and energy prices, and expansionary monetary and fiscal policies have contributed to the rise in inflation and above-target expectations seen by several of Colombia’s main trading partners. These cost and price shocks, heightened by the effects of Russia's invasion of Ukraine, have been more prevalent than expected and have taken place within a set of output and employment recovery, variables that in some countries currently equal or exceed their projected long-term levels. In response, the U.S. Federal Reserve accelerated the pace of the benchmark interest rate increase and rapidly reduced liquidity levels in the money market. Financial market actors expect this behavior to continue and, consequently, significantly increase their expectations of the average path of the Fed's benchmark interest rate. In this setting, the U.S. dollar appreciated versus the peso in the second quarter and emerging market risk measures increased, a behavior that intensified for Colombia. Given the aforementioned, for the remainder of 2022 and 2023, the Bank's technical staff increased the forecast trajectory for the Fed's interest rate and reduced the country's external demand growth forecast. The projected oil price was revised upward over the forecast horizon, specifically due to greater supply restrictions and the interruption of hydrocarbon trade between the European Union and Russia. Global geopolitical tensions, a tightening of monetary policy in developed economies, the increase in risk perception for emerging markets and the macroeconomic imbalances in the country explain the increase in the projected trajectory of the risk premium, its trend level and the neutral real interest rate1. Uncertainty about external forecasts and their consequent impact on the country's macroeconomic scenario remains high, given the unpredictable evolution of the conflict between Russia and Ukraine, geopolitical tensions, the degree of the global economic slowdown and the effect the response to recent outbreaks of the pandemic in some Asian countries may have on the world economy. This macroeconomic scenario that includes high inflation, inflation forecasts, and expectations above 3% and a positive output gap suggests the need for a contractionary monetary policy that mitigates the risk of the persistent unanchoring of inflation expectations. In contrast to the forecasts of the April report, the increase in the risk premium trend implies a higher neutral real interest rate and a greater prevailing monetary stimulus than previously estimated. For its part, domestic demand has been more dynamic, with a higher observed and expected output level that exceeds the economy’s productive capacity. The surprising accelerations in the headline and core inflation reflect stronger and more persistent external shocks, which, in combination with the strength of aggregate demand, indexation, higher inflation expectations and exchange rate pressures, explain the upward projected inflation trajectory at levels that exceed the target over the next two years. This is corroborated by the inflation expectations of economic analysts and those derived from the public debt market, which continued to climb and currently exceed 3%. All of the above increase the risk of unanchoring inflation expectations and could generate widespread indexation processes that may push inflation away from the target for longer. This new macroeconomic scenario suggests that the interest rate adjustment should continue towards a contractionary monetary policy landscape. 1.2. Monetary policy decision Banco de la República’s Board of Directors (BDBR), at its meetings in June and July 2022, decided to continue adjusting its monetary policy. At its June meeting, the BDBR decided to increase the monetary policy rate by 150 basis points (b.p.) and its July meeting by majority vote, on a 150 b.p. increase thereof at its July meeting. Consequently, the monetary policy interest rate currently stands at 9.0% . 1 The neutral real interest rate refers to the real interest rate level that is neither stimulative nor contractionary for aggregate demand and, therefore, does not generate pressures that lead to the close of the output gap. In a small, open economy like Colombia, this rate depends on the external neutral real interest rate, medium-term components of the country risk premium, and expected depreciation. Box 1: A Weekly Indicator of Economic Activity for Colombia Juan Pablo Cote Carlos Daniel Rojas Nicol Rodriguez Box 2: Common Inflationary Trends in Colombia Carlos D. Rojas-Martínez Nicolás Martínez-Cortés Franky Juliano Galeano-Ramírez Box 3: Shock Decomposition of 2021 Forecast Errors Nicolás Moreno Arias
7
Monetary Policy Report - January 2022. Banco de la República, March 2022. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr1-2022.
Full textAbstract:
Macroeconomic summary Several factors contributed to an increase in projected inflation on the forecast horizon, keeping it above the target rate. These included inflation in December that surpassed expectations (5.62%), indexation to higher inflation rates for various baskets in the consumer price index (CPI), a significant real increase in the legal minimum wage, persistent external and domestic inflationary supply shocks, and heightened exchange rate pressures. The CPI for foods was affected by the persistence of external and domestic supply shocks and was the most significant contributor to unexpectedly high inflation in the fourth quarter. Price adjustments for fuels and certain utilities can explain the acceleration in inflation for regulated items, which was more significant than anticipated. Prices in the CPI for goods excluding food and regulated items also rose more than expected. This was partly due to a smaller effect on prices from the national government’s VAT-free day than anticipated by the technical staff and more persistent external pressures, including via peso depreciation. By contrast, the CPI for services excluding food and regulated items accelerated less than expected, partly reflecting strong competition in the communications sector. This was the only major CPI basket for which prices increased below the target inflation rate. The technical staff revised its inflation forecast upward in response to certain external shocks (prices, costs, and depreciation) and domestic shocks (e.g., on meat products) that were stronger and more persistent than anticipated in the previous report. Observed inflation and a real increase in the legal minimum wage also exceeded expectations, which would boost inflation by affecting price indexation, labor costs, and inflation expectations. The technical staff now expects year-end headline inflation of 4.3% in 2022 and 3.4% in 2023; core inflation is projected to be 4.5% and 3.6%, respectively. These forecasts consider the lapse of certain price relief measures associated with the COVID-19 health emergency, which would contribute to temporarily keeping inflation above the target on the forecast horizon. It is important to note that these estimates continue to contain a significant degree of uncertainty, mainly related to the development of external and domestic supply shocks and their ultimate effects on prices. Other contributing factors include high price volatility and measurement uncertainty related to the extension of Colombia’s health emergency and tax relief measures (such as the VAT-free days) associated with the Social Investment Law (Ley de Inversión Social). The as-yet uncertain magnitude of the effects of a recent real increase in the legal minimum wage (that was high by historical standards) and high observed and expected inflation, are additional factors weighing on the overall uncertainty of the estimates in this report. The size of excess productive capacity remaining in the economy and the degree to which it is closing are also uncertain, as the evolution of the pandemic continues to represent a significant forecast risk. margin, could be less dynamic than expected. And the normalization of monetary policy in the United States could come more quickly than projected in this report, which could negatively affect international financing costs. Finally, there remains a significant degree of uncertainty related to the duration of supply chocks and the degree to which macroeconomic and political conditions could negatively affect the recovery in investment. The technical staff revised its GDP growth projection for 2022 from 4.7% to 4.3% (Graph 1.3). This revision accounts for the likelihood that a larger portion of the recent positive dynamic in private consumption would be transitory than previously expected. This estimate also contemplates less dynamic investment behavior than forecast in the previous report amid less favorable financial conditions and a highly uncertain investment environment. Third-quarter GDP growth (12.9%), which was similar to projections from the October report, and the fourth-quarter growth forecast (8.7%) reflect a positive consumption trend, which has been revised upward. This dynamic has been driven by both public and private spending. Investment growth, meanwhile, has been weaker than forecast. Available fourth-quarter data suggest that consumption spending for the period would have exceeded estimates from October, thanks to three consecutive months that included VAT-free days, a relatively low COVID-19 caseload, and mobility indicators similar to their pre-pandemic levels. By contrast, the most recently available figures on new housing developments and machinery and equipment imports suggest that investment, while continuing to rise, is growing at a slower rate than anticipated in the previous report. The trade deficit is expected to have widened, as imports would have grown at a high level and outpaced exports. Given the above, the technical staff now expects fourth-quarter economic growth of 8.7%, with overall growth for 2021 of 9.9%. Several factors should continue to contribute to output recovery in 2022, though some of these may be less significant than previously forecast. International financial conditions are expected to be less favorable, though external demand should continue to recover and terms of trade continue to increase amid higher projected oil prices. Lower unemployment rates and subsequent positive effects on household income, despite increased inflation, would also boost output recovery, as would progress in the national vaccination campaign. The technical staff expects that the conditions that have favored recent high levels of consumption would be, in large part, transitory. Consumption spending is expected to grow at a slower rate in 2022. Gross fixed capital formation (GFCF) would continue to recover, approaching its pre-pandemic level, though at a slower rate than anticipated in the previous report. This would be due to lower observed GFCF levels and the potential impact of political and fiscal uncertainty. Meanwhile, the policy interest rate would be less expansionary as the process of monetary policy normalization continues. Given the above, growth in 2022 is forecast to decelerate to 4.3% (previously 4.7%). In 2023, that figure (3.1%) is projected to converge to levels closer to the potential growth rate. In this case, excess productive capacity would be expected to tighten at a similar rate as projected in the previous report. The trade deficit would tighten more than previously projected on the forecast horizon, due to expectations of an improved export dynamic and moderation in imports. The growth forecast for 2022 considers a low basis of comparison from the first half of 2021. However, there remain significant downside risks to this forecast. The current projection does not, for example, account for any additional effects on economic activity resulting from further waves of COVID-19. High private consumption levels, which have already surpassed pre-pandemic levels by a large margin, could be less dynamic than expected. And the normalization of monetary policy in the United States could come more quickly than projected in this report, which could negatively affect international financing costs. Finally, there remains a significant degree of uncertainty related to the duration of supply chocks and the degree to which macroeconomic and political conditions could negatively affect the recovery in investment. External demand for Colombian goods and services should continue to recover amid significant global inflation pressures, high oil prices, and less favorable international financial conditions than those estimated in October. Economic activity among Colombia’s major trade partners recovered in 2021 amid countries reopening and ample international liquidity. However, that growth has been somewhat restricted by global supply chain disruptions and new outbreaks of COVID-19. The technical staff has revised its growth forecast for Colombia’s main trade partners from 6.3% to 6.9% for 2021, and from 3.4% to 3.3% for 2022; trade partner economies are expected to grow 2.6% in 2023. Colombia’s annual terms of trade increased in 2021, largely on higher oil, coffee, and coal prices. This improvement came despite increased prices for goods and services imports. The expected oil price trajectory has been revised upward, partly to supply restrictions and lagging investment in the sector that would offset reduced growth forecasts in some major economies. Elevated freight and raw materials costs and supply chain disruptions continue to affect global goods production, and have led to increases in global prices. Coupled with the recovery in global demand, this has put upward pressure on external inflation. Several emerging market economies have continued to normalize monetary policy in this context. Meanwhile, in the United States, the Federal Reserve has anticipated an end to its asset buying program. U.S. inflation in December (7.0%) was again surprisingly high and market average inflation forecasts for 2022 have increased. The Fed is expected to increase its policy rate during the first quarter of 2022, with quarterly increases anticipated over the rest of the year. For its part, Colombia’s sovereign risk premium has increased and is forecast to remain on a higher path, to levels above the 15-year-average, on the forecast horizon. This would be partly due to the effects of a less expansionary monetary policy in the United States and the accumulation of macroeconomic imbalances in Colombia. Given the above, international financial conditions are projected to be less favorable than anticipated in the October report. The increase in Colombia’s external financing costs could be more significant if upward pressures on inflation in the United States persist and monetary policy is normalized more quickly than contemplated in this report. As detailed in Section 2.3, uncertainty surrounding international financial conditions continues to be unusually high. Along with other considerations, recent concerns over the potential effects of new COVID-19 variants, the persistence of global supply chain disruptions, energy crises in certain countries, growing geopolitical tensions, and a more significant deceleration in China are all factors underlying this uncertainty. The changing macroeconomic environment toward greater inflation and unanchoring risks on inflation expectations imply a reduction in the space available for monetary policy stimulus. Recovery in domestic demand and a reduction in excess productive capacity have come in line with the technical staff’s expectations from the October report. Some upside risks to inflation have materialized, while medium-term inflation expectations have increased and are above the 3% target. Monetary policy remains expansionary. Significant global inflationary pressures and the unexpected increase in the CPI in December point to more persistent effects from recent supply shocks. Core inflation is trending upward, but remains below the 3% target. Headline and core inflation projections have increased on the forecast horizon and are above the target rate through the end of 2023. Meanwhile, the expected dynamism of domestic demand would be in line with low levels of excess productive capacity. An accumulation of macroeconomic imbalances in Colombia and the increased likelihood of a faster normalization of monetary policy in the United States would put upward pressure on sovereign risk perceptions in a more persistent manner, with implications for the exchange rate and the natural rate of interest. Persistent disruptions to international supply chains, a high real increase in the legal minimum wage, and the indexation of various baskets in the CPI to higher inflation rates could affect price expectations and push inflation above the target more persistently. These factors suggest that the space to maintain monetary stimulus has continued to diminish, though monetary policy remains expansionary. 1.2 Monetary policy decision Banco de la República’s board of directors (BDBR) in its meetings in December 2021 and January 2022 voted to continue normalizing monetary policy. The BDBR voted by a majority in these two meetings to increase the benchmark interest rate by 50 and 100 basis points, respectively, bringing the policy rate to 4.0%.
8
Financial Stability Report - Second Semester of 2020. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/rept-estab-fin.sem2.eng-2020.
Full textAbstract:
The Colombian financial system has not suffered major structural disruptions during these months of deep economic contraction and has continued to carry out its basic functions as usual, thus facilitating the economy's response to extreme conditions. This is the result of the soundness of financial institutions at the beginning of the crisis, which was reflected in high liquidity and capital adequacy indicators as well as in the timely response of various authorities. Banco de la República lowered its policy interest rates 250 points to 1.75%, the lowest level since the creation of the new independent bank in 1991, and provided ample temporary and permanent liquidity in both pesos and foreign currency. The Office of the Financial Superintendent of Colombia, in turn, adopted prudential measures to facilitate changes in the conditions for loans in effect and temporary rules for rating and loan-loss provisions. Finally, the national government expanded the transfers as well as the guaranteed credit programs for the economy. The supply of real credit (i.e. discounting inflation) in the economy is 4% higher today than it was 12 months ago with especially marked growth in the housing (5.6%) and commercial (4.7%) loan portfolios (2.3% in consumer and -0.1% in microloans), but there have been significant changes over time. During the first few months of the quarantine, firms increased their demands for liquidity sharply while consumers reduced theirs. Since then, the growth of credit to firms has tended to slow down, while consumer and housing credit has grown. The financial system has responded satisfactorily to the changes in the respective demands of each group or sector and loans may grow at high rates in 2021 if GDP grows at rates close to 4.6% as the technical staff at the Bank expects; but the forecasts are highly uncertain. After the strict quarantine implemented by authorities in Colombia, the turmoil seen in March and early April, which was evident in the sudden reddening of macroeconomic variables on the risk heatmap in Graph A,[1] and the drop in crude oil and coal prices (note the high volatility registered in market risk for the region on Graph A) the local financial markets stabilized relatively quickly. Banco de la República’s credible and sustained policy response played a decisive role in this stabilization in terms of liquidity provision through a sharp expansion of repo operations (and changes in amounts, terms, counterparties, and eligible instruments), the purchases of public and private debt, and the reduction in bank reserve requirements. In this respect, there is now abundant aggregate liquidity and significant improvements in the liquidity position of investment funds. In this context, the main vulnerability factor for financial stability in the short term is still the high degree of uncertainty surrounding loan quality. First, the future trajectory of the number of people infected and deceased by the virus and the possible need for additional health measures is uncertain. For that reason, there is also uncertainty about the path for economic recovery in the short and medium term. Second, the degree to which the current shock will be reflected in loan quality once the risk materializes in banks’ financial statements is uncertain. For the time being, the credit risk heatmap (Graph B) indicates that non-performing and risky loans have not shown major deterioration, but past experience indicates that periods of sharp economic slowdown eventually tend to coincide with rises in non-performing loans: the calculations included in this report suggest that the impact of the recession on credit quality could be significant in the short term. This is particularly worrying since the profitability of credit establishments has been declining in recent months, and this could affect their ability to provide credit to the real sector of the economy. In order to adopt a forward-looking approach to this vulnerability, this Report presents several stress tests that evaluate the resilience of the liquidity and capital adequacy of credit institutions and investment funds in the event of a hypothetical scenario that seeks to simulate an extreme version of current macroeconomic conditions. The results suggest that even though there could be strong impacts on the credit institutions’ volume of credit and profitability under such scenarios, aggregate indicators of total and core capital adequacy will probably remain at levels that are above the regulatory limits over the horizon of a year. At the same time, the exercises highlight the high capacity of the system's liquidity to face adverse scenarios. In compliance with its constitutional objectives and in coordination with the financial system's security network, Banco de la República will continue to closely monitor the outlook for financial stability at this juncture and will make the decisions that are necessary to ensure the proper functioning of the economy, facilitate the flow of sufficient credit and liquidity resources, and further the smooth operation of the payment systems. Juan José Echavarría Governor