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Artykuły w czasopismach na temat „GDP forecast”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Chang, Andrew C., and Trace J. Levinson. "Raiders of the Lost High-Frequency Forecasts: New Data and Evidence on the Efficiency of the Fed's Forecasting." Finance and Economics Discussion Series 2020, no. 089 (2020): 1–56. http://dx.doi.org/10.17016/feds.2020.090.

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We introduce a new dataset of real gross domestic product (GDP) growth and core personal consumption expenditures (PCE) inflation forecasts produced by the staff of the Board of Governors of the Federal Reserve System. In contrast to the eight Greenbook forecasts a year the staff produces for Federal Open Market Committee (FOMC) meetings, our dataset has roughly weekly forecasts. We use these new data to study whether the staff forecasts efficiently and whether efficiency, or lack thereof, is time-varying. Prespecified regressions of forecast errors on forecast revisions show that the staff's

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2

Yang, Biao, Yingcheng Li, Haokun Wei, and Huan Lu. "Is Urbanisation Rate a Feasible Supplemental Parameter in Forecasting Electricity Consumption in China?" Journal of Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/2465248.

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Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the l

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3

Krupkina, A. S., O. S. Vinogradova, E. A. Orlova, and E. N. Ershova. "Forecasting Russia’s GDP through the production method." Moscow University Economics Bulletin, no. 5 (October 3, 2022): 62–81. http://dx.doi.org/10.38050/01300105202254.

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The paper analyses the variables covering real, financial and external sectors of the economy alongside various sectoral, price and survey indicators. We have obtained forecast values of gross value added by industry and an aggregated estimate of Russia’s productionbased GDP. Drawing on dynamic factor model (DFM) as the main approach, we obtained quarterly point forecasts for production-based Russian GDP and for individual sectors for 2011-21. The forecast accuracy is compared to Bayesian vector autoregression (BVAR),s imple benchmarks based on aggregated and disaggregated GDP modeling and con

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4

Subian, Alwan Rahmana, Drajat Ali Mulkan, Haidar Hilmy Ahmady, and Fitri Kartiasih. "Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia." SISTEMASI 13, no. 1 (2024): 149. http://dx.doi.org/10.32520/stmsi.v13i1.3445.

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The success of an economy can bring significant benefits to a country and its society. One way to measure economic growth is by looking at the value of gross domestic product (GDP). The value of a country's GDP is influenced by many factors, including inflation, exports, and imports. Therefore, predicting future economic growth requires forecasting the value of GDP. GDP forecasts are crucial as they provide information about the economic development of a country over a specific period of time. By forecasting GDP, governments and investors can make informed decisions to optimize profits or mini

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5

Tura-Gawron, Karolina. "The forecasts-based instrument rule and decision making. How closely interlinked? The case of Sweden." Equilibrium 12, no. 2 (2017): 295. http://dx.doi.org/10.24136/eq.v12i2.16.

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Research background: The Central Bank of Sweden declared in years 1999–2006 the implementation of the Svensson’s concept of inflation forecast targeting (IFT). It means that the repo rate decision-making process depends on the inflation fore-casts. The concept evolved from the strict IFT with the decision-making algorithm called ‘the rule of thumb’ to the flexible IFT.Purpose of the article: The aim of the article is to: (1) analyze the influence of the inflation rate and GDP growth rate on the repo rate decisions, (2) analyze the influence of the inflation rate and GDP growth rate forecasts (

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6

Franses, Philip Hans, and Max Welz. "Does More Expert Adjustment Associate with Less Accurate Professional Forecasts?" Journal of Risk and Financial Management 13, no. 3 (2020): 44. http://dx.doi.org/10.3390/jrfm13030044.

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Professional forecasters can rely on an econometric model to create their forecasts. It is usually unknown to what extent they adjust an econometric model-based forecast. In this paper we show, while making just two simple assumptions, that it is possible to estimate the persistence and variance of the deviation of their forecasts from forecasts from an econometric model. A key feature of the data that facilitates our estimates is that we have forecast updates for the same forecast target. An illustration to consensus forecasters who give forecasts for GDP growth, inflation and unemployment fo

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7

Barrell, Ray, and Robert Metz. "An assessment of NIESR forecast accuracy." National Institute Economic Review 196 (April 1, 2006): 36–39. http://dx.doi.org/10.1177/0027950106067042.

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The Institute periodically reviews the accuracy of its macroeconomic forecasts. Pain et al. (2001) compare the performance of NIESR output forecasts to a naïve forecast that uses a simple rule to predict growth next year. They find that between 1980 and 2000 the National Institute forecast performed better than a naïve, or random walk, forecast in two years out of three. Poulizac et al. (1996) consider a sequence of quarterly economic forecasts published by NIESR between 1982 and 1995 (beginning with that produced in February for the growth of GDP and inflation in the following year and finish

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8

Gupta, Monika, and Mohammad Haris Minai. "An Empirical Analysis of Forecast Performance of the GDP Growth in India." Global Business Review 20, no. 2 (2019): 368–86. http://dx.doi.org/10.1177/0972150918825207.

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This article evaluates the accuracy of a forecast based on the properties of the forecast error. To measure how close the predictions of GDP growth are to the actual outcome in India, we have calculated three measures of forecast accuracy: mean absolute error (MAE), root mean square error (RMSE) and Theil’s U statistic. To evaluate the performance of the forecasts, we have compared them with naive forecast and common rules of thumb, using moving averages (MAs) as rules of thumb. The results are inconclusive regarding biasedness and also inefficient. Further, the forecasts have a high degree of

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9

Kiriakidis, Michail, and Antonios Kargas. "Greek GDP forecast estimates." Applied Economics Letters 20, no. 8 (2013): 767–72. http://dx.doi.org/10.1080/13504851.2012.744128.

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10

Fortin, Ines, Sebastian P. Koch, and Klaus Weyerstrass. "Evaluation of economic forecasts for Austria." Empirical Economics 58, no. 1 (2019): 107–37. http://dx.doi.org/10.1007/s00181-019-01814-1.

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AbstractIn this paper, we evaluate macroeconomic forecasts for Austria and analyze the effects of external assumptions on forecast errors. We consider the growth rates of real GDP and the demand components as well as the inflation rate and the unemployment rate. The analyses are based on univariate measures like RMSE and Theil’s inequality coefficient and also on the Mahalanobis distance, a multivariate measure that takes the variances of and the correlations between the variables into account. We compare forecasts generated by the two leading Austrian economic research institutes, the Institu

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11

Korhonen, Iikka, and Maria Ritola. "An Empirical Note on the Success of Forecasting Economic Developments in Major Emerging Markets." Asian Economic Papers 13, no. 1 (2014): 131–54. http://dx.doi.org/10.1162/asep_a_00260.

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In this paper we assess the rationality and goodness-of-fit of macroeconomic forecasts for 21 large emerging market countries during the past two decades. We find that in some countries forecasts have not been rational in the sense that they seem to have a systematic bias. We also find that for both GDP growth and inflation the forecast errors are larger for more volatile economies. For some countries GDP and inflation forecast errors have positive correlation, which is consistent with New Keynesian macromodels, whereas for other countries this is not the case. Our results suggest that relying

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12

Melliss, Chris, and Rod Whittaker. "The Treasury Forecasting Record: Some New Results." National Institute Economic Review 164 (April 1998): 65–79. http://dx.doi.org/10.1177/002795019816400110.

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We examine the forecasting record of HM Treasury for GDP and the RPI from 1971 to the present. As well as presenting the usual statistical measures of performance, such as Root Mean Squared Errors, and regression tests of forecast efficiency and bias, we test for any relationship between the errors in GDP and RPI forecasts. Confidence intervals are constructed using a classical statistical approach based on past forecast errors, which is similar to that employed in this Review to describe forecast uncertainty.

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13

Liu, Yiqun, Xiao Chen, Qiqi Wang, Lipeng Zhu, and Zejiong Zhou. "Forecast and Analysis of National GDP in China Based on ARIMA Model." Academic Journal of Science and Technology 3, no. 2 (2022): 78–83. http://dx.doi.org/10.54097/ajst.v3i2.2096.

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Gross domestic product (GDP) is an important indicator to measure the economic situation and development level of a country or region, which is of great significance in promoting steady economic development. Therefore, this paper selects the national GDP data from 1978 to 2019, uses Eviews 9.0 software to build a model for the selected time series, and finally determines ARIMA (1, 1, 1) as the optimal model, and forecasts and analyzes the national GDP in 2020 and 2021. The prediction results show that the gap between the predicted GDP in 2020 and 2021 and the actual value is small. Therefore,

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14

COSIMO, MAGAZZINO. "Economic growth, CO2 emissions, and energy use in Israel." International Journal of Sustainable Development and World Ecology 22, no. 1 (2021): 89–97. https://doi.org/10.1080/13504509.2014.991365.

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This paper analyses the relationship among economic growth, energy use and carbon dioxide (CO<sub>2</sub>) emissions in Israel over the period 1971–2006. Results of unit root tests show that all variables are integrated of order one. Causality results suggest that real gross domestic product (GDP) drives both energy use and CO<sub>2</sub> emissions. Forecast error variance decompositions (FEVDs) evidence that the errors in real per capita GDP are mainly due to uncertainty in GDP itself, while the errors in predicting the energy consumption and the CO<sub>2</sub> emissions are s

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15

Heenan, Geoffrey, Karras Lui, Ian Nield, et al. "Nowcasting Real GDP in Samoa." IMF Working Papers 2025, no. 092 (2025): 1. https://doi.org/10.5089/9798229009782.001.

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This paper describes the recent work to strengthen the nowcasting capacity at the Central Bank of Samoa (CBS). It compiles available high-frequency datasets such as tourism receipts, agriculture market survey, remittances, among others, to nowcast real GDP in Samoa. Nowcasting enables the estimation of the present and near-term forecast. It employs standard nowcasting methods such as Bridge, Mixed Data Sampling (MIDAS), and Unrestricted MIDAS (U-MIDAS). All methods significantly outperform the naive forecasts. Our analysis show that forecast combination of the three methods minimizes the root

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16

Gómez-Zamudio, Luis M., and Raúl Ibarra. "Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico." Economía 17, no. 2 (2017): 173–203. http://dx.doi.org/10.31389/eco.70.

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This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. The model uses both quarterly and daily sampling frequencies while remaining parsimonious. In particular, the mixed data sampling (MIDAS) regression model is employed to deal with the multi-frequency problem. To preserve parsimony, factor analysis and forecast combination techniques are used to summarize the information contained in a data set containing 392 daily financial series. Our findings suggest that the MIDAS model incorporating daily financial data

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17

Lehmann, Robert, and Klaus Wohlrabe. "Forecasting GDP at the Regional Level with Many Predictors." German Economic Review 16, no. 2 (2015): 226–54. http://dx.doi.org/10.1111/geer.12042.

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Abstract In this study, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden-Württemberg) and Eastern Germany. We overcome the problem of a ‘data-poor environment’ at the sub-national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single-indicator, multi-indicator, pooled and factor forecasts in a ‘pseudo-real-time’ setting. Our results show that we can signific

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18

Barrell, Ray, Simon Kirby, and Rebecca Riley. "UK Economy Forecast." National Institute Economic Review 198 (October 2006): 40–58. http://dx.doi.org/10.1177/0027950106074037.

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GDP growth accelerated from 1.6 per cent in the year to the second quarter of 2005 to 2.6 per cent in the year to the second quarter of 2006. At the time of writing the official preliminary estimates suggest economic growth was 0.7 per cent in the third quarter of this year. This is a little stronger than implied by our forecast, which is based on monthly estimates of GDP. But, taking on board the preliminary estimate in our forecast would have only a small effect on the annual growth figure for this year. Looking further forward we expect relatively stable GDP growth at around the economy's t

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19

Guérin, Pierre, Laurent Maurin, and Matthias Mohr. "TREND-CYCLE DECOMPOSITION OF OUTPUT AND EURO AREA INFLATION FORECASTS: A REAL-TIME APPROACH BASED ON MODEL COMBINATION." Macroeconomic Dynamics 19, no. 2 (2013): 363–93. http://dx.doi.org/10.1017/s1365100513000461.

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This paper estimates univariate and multivariate trend-cycle decomposition models of GDP and considers the novel possibility of regime switches in the growth of potential output. We compute both ex post and real-time estimates of the output gap to check the stability of our estimates to GDP data revisions. We find some evidence of regime changes in the growth of potential output during the recessions experienced by the euro area. We also run a forecasting experiment to evaluate the predictive power of the output gap for inflation. The benchmark autoregressive model tends to obtain the best for

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20

Boero, Gianna, Jeremy Smith, and Kenneth F. Wallis. "Here is the news: forecast revisions in the Bank of England survey of external forecasters." National Institute Economic Review 203 (January 2008): 68–77. http://dx.doi.org/10.1177/0027950108089679.

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This article analyses the forecasts of inflation and GDP growth supplied by the individual respondents to the Bank of England's quarterly Survey of External Forecasters, 1996-2005, using a recently released dataset. This comprises a conventional incomplete panel dataset, with an additional dimension arising from the collection of repeated forecasts for the last quarter of each year. This fixed-event forecast structure allows study of the forecast revision process, its weak and strong efficiency, and its relation to macroeconomic news. The collection of density forecasts as well as point foreca

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21

Rathanayke, G. I. "Cross–Temporal Coherent Forecasts for Gross Domestic Product." Staff Studies 51, no. 1 (2021): 1–35. https://doi.org/10.4038/ss.v51i1.4727.

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Timely and accurate forecasts aligning different views of economic agents are of utmost importance in macroeconomic forecasting to facilitate effective policy decisions. Thus, this study investigates the ability of a reconciliation approach to align different viewpoints regarding forecasts and thereby increasing the forecast performance specifically related to GDP forecasting. The proposed methodology is based on forecasting hierarchical time series which is a collection of time series that follow an inherent aggregation structure. The aggregation constraints can be cross-sectional or temporal

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22

Lörinc, Peter. "Short-Term Forecasting of Slovak GDP Based on High-Frequency Data." Ekonomické rozhľady – Economic Review 54, no. 2 (2025): 132–63. https://doi.org/10.53465/er.2644-7185.2025.2.132-163.

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The paper compares two forecasts of Slovak GDP, the first with high-frequency data and the second without them. We utilize the last observation from the economic activity index acting as a short-term GDP forecast. We use data from 2000 to 2024 in weekly frequencies and have 27 variables related to different sectors such as: real activity, energy, households, labour, expectations, transport, financial data. We address the problem of Nowcasting of the growth rate of Slovak real GDP using dynamic factor models by incorporating ragged edges in the data. The outcome of the paper is that the model w

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23

Lyu, Gaoyue, Xiulin Lyu, Nansong Sun, and Xinrui Zhao. "Application of ARIMA Model and Exponential Smoothing Method in GDP Prediction of the United States." BCP Business & Management 38 (March 2, 2023): 1305–14. http://dx.doi.org/10.54691/bcpbm.v38i.3887.

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The GDP of the United States always attracts the global attention. It represents the strength of the world's largest economy and the per capita living standard of developed countries. Therefore, many scholars try to use various methods to predict it. On the basis of previous research, this paper collects the Fred’s quarterly U.S. GDP data, uses ARIMA model and exponential smoothing to model the US's GDP from 1950 to 2021. Then, the authors compare the actual values with the predicted values to select a better model. After that, the established model is used to forecast the US's GDP. The result

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24

Allen, William A., and Terence Mills. "How Forecasts Evolve - The Growth Forecasts of the Federal Reserve and the Bank of England." National Institute Economic Review 193 (July 2005): 53–59. http://dx.doi.org/10.1177/0027950105058553.

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We investigate how central bank forecasts of GDP growth evolve through time, and how they are adapted in the light of official estimates of actual GDP growth. Using data for 1988–2005, we find that the Federal Open Market Committee (FOMC) has typically adjusted its forecast for growth over the coming four quarters by about a third of the unexpected component of estimated growth in the four quarters most recently ended. We were unable to find any clear signs of systematic errors in the FOMC's forecasts. UK data for 1998–2005 suggest that the Bank of England Monetary Policy Committee (MPC) did n

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25

Bolshakov, D., G. Kozlov, and V. Menshikov. "Forecast of US and China GDP Dynamics." World Economy and International Relations, no. 4 (2011): 105–7. http://dx.doi.org/10.20542/0131-2227-2011-4-105-107.

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Two models of dynamics of economic systems are considered. On their basis the authors carry out an analysis of the prospects of the economic competition between the USA and China. The statistical information on the annual values of the US and Chinese GDP for the last 20 years is treated. The received results allow authors to propose a forecast of the maximum value of China’s GDP and the break-even point in growth of the US GDP.

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26

Shapovalenko, Nadiia. "A BVAR Model for Forecasting Ukrainian Inflation and GDP." Visnyk of the National Bank of Ukraine, no. 251 (June 30, 2021): 14–36. http://dx.doi.org/10.26531/vnbu2021.251.02.

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In this paper, I examine the forecasting performance of a Bayesian Vector Autoregression (BVAR) model with a steady-state prior and compare the accuracy of the forecasts against the QPM and official NBU forecasts during the Q1 2016–Q1 2020 period. My findings suggest that inflation forecasts produced by the BVAR model are more accurate than those of the QPM model for two quarters ahead and are competitive for a longer time horizon. The BVAR forecasts for GDP growth also outperform those of the QPM but for the whole forecast horizon. Moreover, it is revealed that the BVAR model demonstrates a b

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27

Kalinauskas, Žilvinas. "Econometric modelling of the Lithuanian economic indicators." Lietuvos matematikos rinkinys, no. III (December 17, 1999): 376–83. http://dx.doi.org/10.15388/lmd.1999.35664.

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The paper is devoted to model relations between Lithuanian indicators of production, foreign trade, income and prices and to present short-term forecasts. The join behaviour of Lithuanian GDP, exports and imports of goods and services, money, salaries and prices is examined by the structural vector auto-regression models (SVAR). Striving for the larger accuracy, apart the aggregated indicators their components are analysed as well.Economic literature and experience of practical work show that there is relation between GDP, foreign trade, money and income indicators and unemployment. It was ref

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28

Muchisha, Nadya Dwi, Novian Tamara, Andriansyah Andriansyah, and Agus M. Soleh. "Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms." Indonesian Journal of Statistics and Its Applications 5, no. 2 (2021): 355–68. http://dx.doi.org/10.29244/ijsa.v5i2p355-368.

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GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecas

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29

Kurihara, Yutaka, and Akio Fukushima. "AR Model or Machine Learning for Forecasting GDP and Consumer Price for G7 Countries." Applied Economics and Finance 6, no. 3 (2019): 1. http://dx.doi.org/10.11114/aef.v6i3.4126.

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This paper examines the validity of forecasting economic variables by using machine learning. AI (artificial intelligence) has been improved and entering our society rapidly, and the economic forecast is no exception. In the real business world, AI has been used for economic forecasts, but not so many studies focus on machine learning. Machine learning is focused in this paper and a traditional statistical model, the autoregressive (AR) model is also used for comparison. A comparison of using an AR model and machine learning (LSTM) to forecast GDP and consumer price is conducted using recent c

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30

Yong, Qi Dong, Yang Chen, and Jun Li. "Study on City Energy Plan and Control." Applied Mechanics and Materials 291-294 (February 2013): 1227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.1227.

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In the article, the CD city energy consume is taken as the study object, the formula of energy consume amount forecast is deduced by the method of coefficient of elasticity, energy control model is put forward, the growth amount of the GDP and the energy consume amount of the CD city are forecasted based on the model, and then the energy control object is brought out. Through the forecast model and control model of the CD city energy consume, the energy consume structure of the CD city will be optimized during "twelve five-year" period, unit GDP energy consume amount will be decreased, and the

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31

Jahangir, S. M. Rashed, and Betul Yuce Dural. "Crude oil, natural gas, and economic growth: impact and causality analysis in Caspian Sea region." International Journal of Management and Economics 54, no. 3 (2018): 169–84. http://dx.doi.org/10.2478/ijme-2018-0019.

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Abstract The main objective of this study was to investigate the impact and causality of crude oil and natural gas on economic growth in the Caspian Sea region. Here, the study applies ordinary least square (OLS) method and Granger causality test using time series data from 1997 to 2015 to ascertain the impact and causality of crude oil and natural gas on economic growth. The results, according to the OLS method, evince that crude oil and natural gas have a significant impact on economic growth of the region. Alongside, considering causality test, gross domestic product (GDP) does Granger caus

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32

Wu, Junyi. "Shenzhen's GDP Forecast: Challenges, Opportunities, and Future Prospects." Advances in Economics, Management and Political Sciences 191, no. 1 (2025): None. https://doi.org/10.54254/2754-1169/2025.bj24364.

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Urban GDP serves as a vital barometer of economic advancement, playing a pivotal role in evaluating city competitiveness, shaping policy frameworks, and fostering regional sustainable progress. This research delves into the data-model forecasting of Shenzhen's GDP trajectory, its driving forces, and its impact on urban evolution. Employing qualitative analysis and extensive literature review, the study examines existing research and case studies to unravel the intricacies of Shenzhen's GDP growth mechanisms. The findings underscore that Shenzhen's annual GDP displays a quadratic growth pattern

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33

Franses, Philip Hans. "Modeling Judgment in Macroeconomic Forecasts." Journal of Quantitative Economics 19, S1 (2021): 401–17. http://dx.doi.org/10.1007/s40953-021-00277-5.

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AbstractMany macroeconomic forecasts are the outcome of a judgmental adjustment to a forecast from an econometric model. The size, direction, and motivation of the adjustment are often unknown as usually only the final forecast is available. This is problematic in case an analyst wishes to learn from forecast errors, which could lead to improving the model, the judgment or both. This paper therefore proposes a formal method to include judgment, which makes the combined forecast reproducible. As an illustration, a forecast from a benchmark simple time series model is only modified when the valu

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34

Zhemkov, Michael. "Nowcasting Russian GDP using forecast combination approach." International Economics 168 (December 2021): 10–24. http://dx.doi.org/10.1016/j.inteco.2021.07.006.

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Govallo, M. O., D. D. Lobanova, and L. A. Davletshina. "Analysis and forecasting of the dynamics of the gross domestic product per capita by purchasing power parity in Russia." Vestnik Universiteta, no. 11 (January 1, 2025): 99–108. https://doi.org/10.26425/1816-4277-2024-11-99-108.

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This article provides an extensive analysis of the dynamics of gross domestic product (hereinafter referred to as GDP) per capita by purchasing power parity in the Russian Federation (hereinafter referred to as RF, Russia) for the period from 1995 to 2022. Using exponential smoothing and an adaptive forecasting method, the indicator forecast is built for the period from 2023 to 2027. The characteristic and description of the mathematical and statistical models used are given. The reasons for choosing the model are determined and justified. Based on the results of the calculations, the paper ev

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36

Kharlamova, Ganna, Andriy Stavytskyy, Oleksandr Chernyak, Vincentas Giedraitis, and Olena Komendant. "Economic modeling of the GDP gap in Ukraine and worldwide." Problems and Perspectives in Management 17, no. 2 (2019): 493–509. http://dx.doi.org/10.21511/ppm.17(2).2019.38.

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ActualityThe concept of output gap plays an important role in traditional macroeconomic theory, applied research and monetary policy. GoalThe paper reveals analyses of the potential economic development in Ukraine and in some countries of the world under limited information. Thus, the practical goal is to consider the best modelling approach for the possibility to regulate GDP in Ukraine, as it has been experienced in other countries of the world. MethodThe research is realized with the help of economic-mathematical modelling of GDP gap based on the analysis of the production function, statist

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Raupp, Edward R. "Forecasting Tanzania GDP per Capita, 2013 – 2021." Caucasus Journal of Social Sciences 8, no. 1 (2023): 91–114. http://dx.doi.org/10.62343/cjss.2015.146.

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This study forecasts standard of living in Tanzania over the next ten years as measured by the ratio of two rates: (1) economic growth, using real gross domestic product (GDP), to (2) population growth. GDP alone is an insufficient measure of a nation’s well-being. China and India, for example, have high levels of GDP, but the pie is sliced very thin to be shared among the billions of people who live in those two countries. Real GDP per capita measures not only the level of economic activity but also the number of people who must share in the results of that activity. This study uses historica

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Ashiya, Masahiro. "Testing the rationality of Japanese GDP forecasts: the sign of forecast revision matters." Journal of Economic Behavior & Organization 50, no. 2 (2003): 263–69. http://dx.doi.org/10.1016/s0167-2681(02)00051-3.

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Rana, Surya Bahadur. "Forecasting GDP Movements in Nepal Using Autoregressive Integrated Moving Average (ARIMA) Modelling Process." Journal of Business and Social Sciences Research 4, no. 2 (2019): 1–20. http://dx.doi.org/10.3126/jbssr.v4i2.29480.

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This study attempts to test the ARIMA model and forecast annual time series of GDP in Nepal from mid-July, 1960 to mid-July, 2018. The annual time series on GDP used in this study consists of total 59 observations. Out of them, three years’ data from mid-July 2016 to mid-July 2018 have been used for in-sample forecasting and evaluation. The study uses univariate Box-Jenkins ARIMA modelling process to identify the best fitted model that describes the sample data set. The study examines a number of ARIMA family models and recommends ARIMA (0,1,2) as the most appropriate model that best describes

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Thomson, Daniel, and Gary Van Vuuren. "Forecasting The South African Business Cycle Using Fourier Analysis." International Business & Economics Research Journal (IBER) 15, no. 4 (2016): 175–92. http://dx.doi.org/10.19030/iber.v15i4.9755.

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A Fourier transform analysis is proposed to determine the duration of the South African business cycle, measured using log changes in nominal gross domestic product (GDP). The most prominent cycle (two smaller, but significant, cycles are also present in the time series) is found to be 7.1 years, confirmed using Empirical Mode Decomposition. The three dominant cycles are used to estimate a 3.5 year forecast of log monthly nominal GDP and these forecasts compared to observed (historical) data. Promising forecast potential is found with this significantly-reduced number of cycle components than

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Poulizac, David, Martin Weale, and Garry Young. "The Performance of National Institute Economic Forecasts." National Institute Economic Review 156 (May 1996): 55–62. http://dx.doi.org/10.1177/002795019615600104.

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Economic forecasts are usually presented as point estimates, despite the margin of uncertainty which surrounds them. In November 1995 the National Institute began to present estimates of the probability of the government's inflation target being met and of there being a fall in GDP. This article describes the methods that we use for calculating these probabilities. We show, by studying eight successive forecasts of the same event, how forecast reliability improves as the forecast horizon approaches and demonstrate that this can be explained in terms of the accumulation of information about the

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PÂRȚACHI, Ion, and Simion MIJA. "MOLDOVA GDP FORECASTING USING BAYESIAN MULTIVARIATE MODELS." Revista Economica 76, no. 1 (2025): 85–93. https://doi.org/10.56043/reveco-2024-0008.

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Building a multivariate GDP forecasting model based on relevant macroeconomic indicators selected through a proper selection process. This paper assesses whether alternative specifications of the Bayesian model can provide higher forecast accuracy compared to a standard VECM (Vector Error Correction Model). To achieve this, a Bayesian VAR (Vector Autoregressive) model is estimated using the Litterman precedent (1979). Compare the result based on the Bayesian VAR (Vector Autoregressive) model with the DFM (Dynamic Factor Model). The out-of-sample forecast performance of the models is then evalu

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Partachi, Ion, and Simion Mija. "MOLDOVA GDP FORECASTING USING BAYESIAN MULTIVARIATE MODELS." Revista Economica 76, no. 1 (2024): 85–93. https://doi.org/10.56043/reveco-2024-0008.

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Building a multivariate GDP forecasting model based on relevant macroeconomic indicators selected through a proper selection process. This paper assesses whether alternative specifications of the Bayesian model can provide higher forecast accuracy compared to a standard VECM (Vector Error Correction Model). To achieve this, a Bayesian VAR (Vector Autoregressive) model is estimated using the Litterman precedent (1979). Compare the result based on the Bayesian VAR (Vector Autoregressive) model with the DFM (Dynamic Factor Model). The out-of-sample forecast performance of the models is then evalu

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McCloskey, PJ, and Rodrigo Malheiros Remor. "Comparative Analysis of ARIMA, VAR, and Linear Regression Models for UAE GDP Forecasting." Emirati Journal of Business, Economics, & Social Studies 4, no. 1 (2025): 23–33. https://doi.org/10.54878/gh57cx16.

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Forecasting GDP is crucial for economic planning and policymaking. This study compares the performance of three widely-used econometric models—ARIMA, VAR, and Linear Regression—using GDP data from the UAE. Employing a rolling forecast approach, we analyze the models’ accuracy over different time horizons. Results indicate ARIMA’s robust long-term forecasting capability, LR models perform better with short-term predictions, particularly when exogenous variable forecasts are accurate. These insights provide a valuable foundation for selecting forecasting models in the UAE’s evolving economy, sug

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Marcel, Uche Ezeh, U. Ubeku Emmanuel, and O. Otuagoma Smith. "Long-Term Load Forecasting Incorporating GDP and Population Dynamics." Engineering and Technology Journal 10, no. 02 (2024): 3942–52. https://doi.org/10.5281/zenodo.14988962.

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This research paper seeks to create a dependable long-term load forecasting (LTLF) model for Nigeria that includes socio-economic indicators like Gross Domestic Product (GDP) and population trends. Accurate electricity demand forecasts help policymakers plan infrastructure expansion to meet demand increases due to population growth and socio-economic development. A multi-model approach integrating Linear Regression with Support Vector Machines and Artificial Neural Networks was used to reach this objective. The performance of these models was assessed through implementation and comparison on M

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Hua, Siqi. "Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis." Wireless Communications and Mobile Computing 2022 (January 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/1967607.

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GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a

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COSIMO, MAGAZZINO. "Electricity Demand, GDP and Employment: Evidence from Italy." Frontiers in Energy 8, no. 1 (2021): 31–40. https://doi.org/10.1007/s11708-014-0296-8.

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This paper applies time series methodologies to examine the causal relationship among electricity demand, real per capita GDP and total labor force for Italy from 1970 to 2009. After a brief introduction, a survey of the economic literature on this issue is reported, before discussing the data and introducing the econometric techniques used. The results of estimation indicate that one cointegrating relationship exists among these variables. This equilibrium relation implies that, in the long-run, GDP and labor force are correlated negatively, as well as GDP and electricity. Moreover, there is

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Ju, Jin Yan, Rong Xin Zhu, and Lei Geng. "Forecasting and Analysis the Demand of Agricultural Mechanization for Economic Development." Advanced Materials Research 694-697 (May 2013): 3512–15. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.3512.

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The development of agricultural mechanization not only has to consider its development speed, but also should coordinate with economic development. Therefore, taking economic development as the independent variable, and agricultural mechanization development as the dependent variable, the nonlinear relationship model was established. Then, on the basis of forecasting GDP which on behalf of the economic development level, the demands of agricultural mechanization for economic development was predicted. Given the limitations of single forecast model, the nonlinear combination forecast models bas

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Rocha, Francisco J. S., Marcos R. V. Magalhaes, and Átila Amaral Brilhante. "A BVAR Analysis on Channels of Monetary Policy Transmission in Brazil." International Journal of Economics and Finance 14, no. 3 (2022): 19. http://dx.doi.org/10.5539/ijef.v14n3p19.

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This article measures the responses of GDP and inflation to a positive shock of the variables that make up the channels of transmission of monetary policy. The results of impulse-response functions of the estimated Bayesian VAR (BVAR) were: an increase in the short-term interest rate (SELIC) leads to a long-term interest rate increasing and consequently a reduction in GDP. Free credit does not have a significant impact on Brazilian GDP, given the low free credit/GDP ratio (Bogdanski et al., 2000). A shock in inflation expectations result in a decreasing trajectory of GDP, a fact consistent wit

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Nguyen, V. H. M., K. T. P. Nguyen, C. V. Vo, and B. T. T. Phan. "Forecast on 2030 Vietnam Electricity Consumption." Engineering, Technology & Applied Science Research 8, no. 3 (2018): 2869–74. http://dx.doi.org/10.48084/etasr.2037.

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The first but very significant step in electricity system planning is to make an accurate long-term forecast on electricity consumption. This article aims to forecast the consumption for the Vietnam electricity system (GWH) up to 2030. An econometric model with the Cobb Douglas production function is used. The five variables proposed in the forecasting function are GDP, income, population, proportion of industry and service in GDP, and number of households. The forecasting equation is tested in terms of stationary and co-integration to choose meaningful variables and to eliminate the minor one

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