Relevant bibliographies by topics / GDP forecast

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'GDP forecast'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "GDP forecast"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "GDP forecast"

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Lundberg, Otto. "GDP forecasting and nowcasting : Utilizing a system for averaging models to improve GDP predictions for six countries around the world." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-131718.

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This study was issued by Swedbank because they wanted too improve their GDP growth forecast capabilites.  A program was developed and tested on six countries; USA, Sweden, Germany, UK, Brazil and Norway. In this paper I investigate if I can reduce forecasting error for GDP growth by taking a smart average from a variety of models compared to both the best individual models and a random walk. I combine the forecasts from four model groups: Vector autoregression, principal component analysis, machine learning and random walk. The smart average is given by a system that give more weight to the pr
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Adriansson, Nils, and Ingrid Mattsson. "Forecasting GDP Growth, or How Can Random Forests Improve Predictions in Economics?" Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243028.

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GDP is used to measure the economic state of a country and accurate forecasts of it is therefore important. Using the Economic Tendency Survey we investigate forecasting quarterly GDP growth using the data mining technique Random Forest. Comparisons are made with a benchmark AR(1) and an ad hoc linear model built on the most important variables suggested by the Random Forest. Evaluation by forecasting shows that the Random Forest makes the most accurate forecast supporting the theory that there are benefits to using Random Forests on economic time series.
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Spånberg, Erik. "Out of Sample Forecast of Swedish GDP Growth by the Economic Sentiment Indicator in the Euro Area : A Bayesian Approach." Thesis, Uppsala universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256761.

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In this paper, the predictive capabilities of the Economic Sentiment Indicator (ESI), based on business and consumer surveys in the Euro area, are evaluated by out-of-sample forecasts of Swedish GDP growth. A steady state Bayesian VAR-model is applied to quarterly data from 1996 to 2014. The results show that the inclusion of the ESI improves the forecasting performance, both in the point predictive measurement Root Mean Square Errors and in the forecast density sharpness measurement Log Predictive Density Scores. These findings suggest that international confidence indicators may prove useful
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Eklund, Jana. "Essays on forecasting and Bayesian model averaging." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-490.

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This thesis, which consists of four chapters, focuses on forecasting in a data-rich environment and related computational issues. Chapter 1, “An embarrassment of riches: Forecasting using large panels” explores the idea of combining forecasts from various indicator models by using Bayesian model averaging (BMA) and compares the predictive performance of BMA with predictive performance of factor models. The combination of these two methods is also implemented, together with a benchmark, a simple autoregressive model. The forecast comparison is conducted in a pseudo out-of-sample framework for t
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Zuanazzi, Pedro Tonon. "Previsões para o crescimento do PIB trimestral brasileiro com séries financeiras e econômicas mensais : uma aplicação de midas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/72807.

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A previsão do PIB é um dos principais balizadores para as decisões produtivas de agentes econômicos. Com o objetivo de realizar previsões para o crescimento do PIB trimestral brasileiro, são utilizadas 16 séries mensais financeiras e econômicas como potenciais preditores, abrangendo o período do segundo trimestre de 1996 ao quarto trimestre de 2012. Para isso, aplicou-se as abordagens MIDAS (Mixed Data Sampling) e UMIDAS (Unrestricted Mixed Data Sampling), confrontando seus resultados de previsão fora da amostra com o benchmark ARMA. Foram encontrados erros de previsão menores nessas abordagen
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Sinzato, Arthur Yukio. "Estudo comparativo do poder preditivo do PIB brasileiro por variáveis macroeconômicas em relação ao conjunto de variáveis da estrutura de taxa de juros." reponame:Repositório Institucional do FGV, 2012. http://hdl.handle.net/10438/10089.

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Submitted by Arthur Yukio Sinzato (arthursinzato@gmail.com) on 2012-10-03T16:53:57Z No. of bitstreams: 1 Dissertacao - Arthur Y Sinzato.pdf: 1540863 bytes, checksum: 58f233b710218ecced2ad0bea5cbe63a (MD5)<br>Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2012-10-03T17:09:30Z (GMT) No. of bitstreams: 1 Dissertacao - Arthur Y Sinzato.pdf: 1540863 bytes, checksum: 58f233b710218ecced2ad0bea5cbe63a (MD5)<br>Made available in DSpace on 2012-10-03T17:12:04Z (GMT). No. of bitstreams: 1 Dissertacao - Arthur Y Sinzato.pdf: 1540863 bytes, checksum: 58f233b71
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Singh, Shiu Raj. "Dynamics of macroeconomic variables in Fiji : a cointegrated VAR analysis." Diss., Lincoln University, 2008. http://hdl.handle.net/10182/774.

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Abstract of thesis submitted in partial fulfilment of the requirements for the Degree of Master of Commerce and Management Dynamics of macroeconomic variables in Fiji : a cointegrated VAR analysis By Shiu Raj Singh The objective of this study is to examine how macroeconomic variables of Fiji inter-relate with aggregate demand and co-determine one another using a vector autoregression (VAR) approach. This study did not use a prior theoretical framework but instead used economic justification for selection of variables. It was found that fiscal policy, which is generally used as a stabili
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Duarte, Cláudia Filipa Pires. "Essays on mixed-frequency data : forecasting and unit root testing." Doctoral thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/11662.

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Doutoramento em Economia<br>Nas últimas décadas, OS investigadores têm tido acesso a bases de dados cada vez mais abrangentes, que incluem séries com frequências temporais mais elevadas e que são divulgadas mais atempadamente. Em contraste, algumas variáveis, nomeadamente alguns dos principais indicadores macroeconómicos, são divulgados com urn desfasamento temporal significativo e com baixa frequência. Esta situação levanta questões sobre como lidar com séries com frequências temporais diferentes, mistas. Ao longo do tempo, várias técnicas têm sido propostas. Esta tese debruça-se sobre uma t
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DANTAS, Leydson Galvíncio. "Aplicação de modelos para séries temporais e pluviométricas no estado da Paraíba." Universidade Federal de Campina Grande, 2016. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1390.

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Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-08-08T20:51:33Z No. of bitstreams: 1 LEYDSON GALVÍNCIO DANTAS – DISSERTAÇÃO (PPGMET) 2016.pdf: 2738181 bytes, checksum: db3cee5c7f083124e00eb4eb3220492c (MD5)<br>Made available in DSpace on 2018-08-08T20:51:33Z (GMT). No. of bitstreams: 1 LEYDSON GALVÍNCIO DANTAS – DISSERTAÇÃO (PPGMET) 2016.pdf: 2738181 bytes, checksum: db3cee5c7f083124e00eb4eb3220492c (MD5) Previous issue date: 2016-02-19<br>CNPq<br>A variabilidade espacial e temporal das precipitações no estado da Paraíba, proporciona irregularidades na distribuição da mesm
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Lopes, João Daniel Barosa. "A relação entre a procura interna e a produção em Portugal : uma análise com recurso ao modelo vetorial autorregressivo." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/20732.

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Mestrado em Econometria Aplicada e Previsão<br>A Procura Interna representa um papel essencial na economia como motor de crescimento do nível de produção de bens e serviços de qualquer país. Para Portugal, dada a forte dimensão do Consumo Privado, essa influência na produção é ainda mais notória. Dessa forma, para melhor descrever a economia nacional é fundamental estabelecer as relações inter-dinâmicas entre Consumo, Investimento e PIB, para que os agentes económicos sejam capazes de antecipar mudanças e dessa forma ajustar as suas decisões. Como tal, com recurso a dados trimestrais compreen
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Books on the topic "GDP forecast"

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Aruoba, S. Boragan. Improving gdp measurement: A forecast combination perspective. National Bureau of Economic Research, 2011.

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Grasmann, Peter. An indicator-based short-term forecast for quarterly GDP in the euro area. Directorate-General for Economic and Financial Affairs, Commission of the European Communities, 2001.

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Lindén, Staffan. Assessment of GDP forecasts uncertainty. European Commission, Directorate-General for Economic and Financial Affairs, 2003.

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Patterson, K. D. Efficient forecasts or measurement errors?: Some evidence for revisions to United Kingdom GDP. University of Reading. Department of Economics, 1990.

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Orphanides, Athanasios. The reliability of inflation forecasts based on output gap estimates in real time. Federal Reserve Board, 2004.

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Makgetla, Neva. Background information for wage bargaining: Economic and inflation forecasts and the apartheid wage gap. NALEDI, 1995.

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Gatti, Roberta, Daniel Lederman, Asif M. Islam, Ha Nguyen, Rana Lotfi, and Mennatallah Emam Mousa. Data Transparency and GDP Growth Forecast Errors. World Bank, Washington, DC, 2023. http://dx.doi.org/10.1596/1813-9450-10406.

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Mendez-Ramos, Fabian. Variance and Skewness in Density Predictions: A World GDP Growth Forecast Assessment. World Bank, Washington, DC, 2017. http://dx.doi.org/10.1596/33116.

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The Widening rural-urban income gap: Past trend or-- forecast for the 1990s? The Center, 1991.

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Gajewski, Zbigniew. Prognozowanie wystąpień faz fenologicznych pierwiosnki omączonej Primula farinosa L. (Primulaceae) – krytycznie zagrożonego gatunku - w odniesieniu do fenologii innych składników lokalnej flory i panujących warunków termicznych. Publishing House of the University of Agriculture in Krakow, 2018. http://dx.doi.org/10.15576/978-83-66602-32-8.

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In Poland, the bird’s-eye primrose (P. farinosa) is a rare and critically endangered species. Currently, it occurs only in one location in the area of the Jaworki village in Radziejowa Range (of Beskid Sądecki mountains). This is the last of the nine previously existing locations, and the only one occurring in the mountains. To maintain the species, as well as the moutain fen on which it grows, a multiannual conservation program has been implemented, including, among other measures, conducting environmental monitoring and performing active protection procedures. In 2012–2014, studies were carr
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Book chapters on the topic "GDP forecast"

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Aruoba, S. Borağan, Francis X. Diebold, Jeremy Nalewaik, Frank Schorfheide, and Dongho Song. "Improving U.S. GDP Measurement: A Forecast Combination Perspective." In Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1653-1_1.

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Lyu, Siyi, and Ziqin Zhou. "Analysis of Green GDP and Global Temperature Forecast Based on Time Series Model." In Atlantis Highlights in Intelligent Systems. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-200-2_116.

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de Souza, Leonardo Lourenço, Vinicius Antonio Montgomery de Miranda, Carlos Eduardo Sanches da Silva, Juliana Helena Daroz Gaudencio, and Pedro Paulo Balestrassi. "Forecast of Brazilian GDP Growth Through Consumption of Diesel Oil and Electrical Energy." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80785-5_15.

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Zhang, Xiaoxi, Machiko Shinozuka, Yuriko Tanaka, Yuko Kanamori, and Toshihiko Masui. "Forecast of Future Impacts of Using ICT Services on GHG Emissions Reduction and GDP Growth in Japan." In Sustainable Production, Life Cycle Engineering and Management. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6775-9_14.

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Li, Yang, and Xiaojing Zhang. "Pension Gap and Implicit Debt Forecast." In China's National Balance Sheet. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4385-7_15.

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Cilliers, Jakkie. "The Growing Gap." In The Future of Africa. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-46590-2_1.

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AbstractIn this chapter Cilliers introduces the growing divergence in income and other indices of well-being between Africa and the Rest of the World. He touches on various aspects such as extreme poverty and Africa’s marginal role in the global economy and illustrates the challenge by comparing the divergent experiences of South Korea and Ghana in demographics and income, before moving to introduce matters relating to productivity, digitisation, agriculture and manufacturing. The chapter presents key characteristics of Africa compared to other countries and regions to 2040, defines some of th
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Decheng, Fan, and Xiong Wei. "Forecast of Income Gap Between Urban and Rural Residents in Central China." In Proceedings of the Tenth International Forum of Decision Sciences. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9741-9_27.

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Nguyen, Tien Nhat, Thi Thanh Xuan Pham, and Thi Canh Nguyen. "Forecasts of GDP Growth and Inflation Under the Influence of the Covid-19 Pandemic: The Case of Vietnam." In Prediction and Causality in Econometrics and Related Topics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77094-5_38.

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Hendriks, Carlijn, Renske Timmermans, Martijn de Ruyter de Wildt, et al. "PASODOBLE AIRSHEDS: Regional Operational Air Quality Forecasts to Bridge the Gap Between Continental Scale and Local/Urban Scale Services." In Air Pollution Modeling and its Application XXII. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5577-2_31.

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Kundu, Amit, and Barendra Nath Chakraborty. "Temporal Dynamics of Capital Inflows, Currency Valuation, and Economic Performance in the Indian Economy." In Sustainable Partnership and Investment Strategies for Startups and SMEs. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2197-3.ch013.

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This chapter investigates the association between FDI inflow, exchange rate and GDP in India for the period 1990-2021. How changes in exchange rate affected the openness measures of India such as FDI and vice versa are the matter of study of this chapter. Cointegration among the three (FDI, exchange rate and GDP) is the prime focus of this investigation. Unit root test, Johansen cointegration test, Granger causality, VAR and variance decomposition are adopted to investigate the relation. Key findings include that, variables are not cointegrated. VAR shows that FDI positively affects GDP from 2
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Conference papers on the topic "GDP forecast"

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Malakhov, Vladimir, and Natalya Leokumovich. "Rising Energy Domestic Prices and Russia's Forecast GDP Rates." In 2024 17th International Conference on Management of Large-Scale System Development (MLSD). IEEE, 2024. http://dx.doi.org/10.1109/mlsd61779.2024.10739493.

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Adrian, Sandu, and Răcănel Carmen. "Key indicators affecting the evolution of road traffic in Romania." In 8th International Conference on Road and Rail Infrastructure. University of Zagreb Faculty of Civil Engineering, 2024. http://dx.doi.org/10.5592/co/cetra.2024.1666.

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As with all forecasts, in estimating road traffic for large periods of time (20 or 30 years) there is uncertainty regarding the outcome of key input variables, such as future GDP growth, fuel prices, and population. The forecasts presented should be read as projected trends for traffic, considering the most likely evolution of the input variables. The main indicators affecting the evolution of road traffic are: population and employment, Gross Domestic Product, average net wage gain, vehicle fleet and motorization rate, Average Daily Travel, and road capacity and network utilization rate. To a
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Deka, Angshuman, Nima Hamta, Behzad Esmaeilian, and Sara Behdad. "Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47474.

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Effective energy planning and governmental decision making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two Artificial Neural Network (ANN) models, two regression analysis models and one autoregressive integrated moving average (ARIMA) model are developed based on historical data from 1950–2013. While ANN model 1 and regression model 1 use Gross Domestic Product (GDP), Gross National Product (GNP) and per capita personal inc
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Ge, Li, and Bo Cui. "Research on forecast of GDP based on process neural network." In 2011 Seventh International Conference on Natural Computation (ICNC). IEEE, 2011. http://dx.doi.org/10.1109/icnc.2011.6022203.

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Geraskina, A. A., and L. N. Yudaeva. "APPLICATION OF CORRELATION AND REGRESSION ANALYSIS TO FORECAST RUSSIA'S GDP." In Modern Technologies in Science and Education MTSE-2020. Ryazan State Radio Engineering University, 2020. http://dx.doi.org/10.21667/978-5-6044782-6-4-29-33.

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Koswaththa, N. B. K. S. M., G. A. G. D. Gaganathara, A. S. M. S. Fernando, M. D. T. G. Dissanayake, and M. L. Guruge. "A Model Comparison to Forecast Gross Domestic Product (GDP) in China." In SLIIT International Conference on Advancements in Sciences and Humanities 2023. Faculty of Humanities and Sciences, SLIIT, 2023. http://dx.doi.org/10.54389/afoy5964.

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Gross Domestic Product (GDP) is an accurate indicator to measure the size of the economic performance of a country and its growth rate. This study focuses on finding a suitable model to forecast GDP in China, which is one of the world’s largest and most rapidly developing economies. A simple linear regression model with AR(1) error structure and Autoregressive Integrated Moving Average (ARIMA) model were developed and compared for the purpose. A secondary data set which includes GDP in China from 1952 to 2020 was used for this study and the sample size was 69. Residual diagnostics tests were c
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Wang, Jiasheng. "Forecast GDP with Autoregressive Distributed Lag Model and Dynamic Factor Model." In ICCIR 2021: 2021 International Conference on Control and Intelligent Robotics. ACM, 2021. http://dx.doi.org/10.1145/3473714.3473783.

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Huang, Hao. "Analysis and Forecast of Resource-based City GDP Based on ARIMA Model." In Proceedings of the 2019 4th International Conference on Financial Innovation and Economic Development (ICFIED 2019). Atlantis Press, 2019. http://dx.doi.org/10.2991/icfied-19.2019.39.

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Wijesinghe, W. R. A. N. M., K. A. S. Umayangi, H. D. K. De Silva, S. D. M. Mundigalage, and T. S. G. Peiris. "Forecasting of Constant GDP per capita of Sri Lanka using ARIMA model." In SLIIT International Conference on Advancements in Sciences and Humanities 2023. Faculty of Humanities and Sciences, SLIIT, 2023. http://dx.doi.org/10.54389/shpe7237.

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GDP per capita is a global measurement for assessing the economic prosperity of nations. Constant (Real) GDP per capita eliminates the effects of inflation which allows for a more accurate comparison of GDP per capita over time. However, no statistical models have been developed to predict annual constant GDP per capita (CGDPC) in Sri Lanka. In this study, ARIMA (1,1,0) model was developed using past data from 1961 to 2018 to forecast CGDPC. The best-fitted model was identified based on three possible models using sample ACF and sample PACF of the stationary series and comparing statistics suc
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Zhao, Zihan. "Research on GDP Forecast of the Inner Mongolia Autonomous Region Based on ARIMA Model." In 2021 3rd International Conference on Economic Management and Cultural Industry (ICEMCI 2021). Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.211209.430.

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Reports on the topic "GDP forecast"

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Aruoba, S. Boragan, Francis Diebold, Jeremy Nalewaik, Frank Schorfheide, and Dongho Song. Improving GDP Measurement: A Forecast Combination Perspective. National Bureau of Economic Research, 2011. http://dx.doi.org/10.3386/w17421.

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Clark, Todd E., Gergely Ganics, and Elmar Mertens. What is the predictive value of SPF point and density forecasts? Federal Reserve Bank of Cleveland, 2022. http://dx.doi.org/10.26509/frbc-wp-202237.

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This paper presents a new approach to combining the information in point and density forecasts from the Survey of Professional Forecasters (SPF) and assesses the incremental value of the density forecasts. Our starting point is a model, developed in companion work, that constructs quarterly term structures of expectations and uncertainty from SPF point forecasts for quarterly fixed horizons and annual fixed events. We then employ entropic tilting to bring the density forecast information contained in the SPF’s probability bins to bear on the model estimates. In a novel application of entropic
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Mitchell, James, Aubrey Poon, and Dan Zhu. Constructing density forecasts from quantile regressions: multimodality in macro-financial dynamics. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202212r.

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Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the "data speak." Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regre
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Chernis, Tony, Niko Hauzenberger, Florian Huber, Gary Koop, and James Mitchell. Predictive Density Combination Using a Tree-Based Synthesis Function. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202330.

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Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a “synthesis” function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from th
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Ghirelli, Corinna, Javier J. Pérez, and Daniel Santabárbara. Inflation and growth forecast errors and the sacrifice ratio of monetary policy in the euro area. Banco de España, 2025. https://doi.org/10.53479/39441.

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This paper investigates the relationship between inflation and GDP growth forecast errors and the expected monetary policy stance in the euro area during the monetary policy cycle of 2022-2024, when inflation was well above the ECB’s target. Under rational expectations, forecasts of monetary contractions should be unrelated to subsequent inflation and growth forecast errors. On the contrary, we find that expected monetary policy tightening has been associated with higher than projected GDP growth, suggesting a lower monetary policy effect than that factored in by (ECB/Eurosystem and IMF) forec
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Iyer, Ananth V., Thomas Brady, Steven R. Dunlop, et al. Forecasting Freight Logistic Needs and INDOT Plans. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317372.

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This project focused on forecasting freight logistics needs and developing and analyzing capacity plans for INDOT to consider. The forecast timeframe ranges from the 2020 to 2045; the commodities considered are those used in the FHWA framework. We considered five SSP (Shared Socio-Economic Pathways) scenarios that are in sync with those used by the IPCC (International Protocol for Climate Change). We also use the IPCC forecasts of world GDP and FHWA forecasts to develop county-level freight forecasts by commodity. A survey of industry participants, primarily in manufacturing, suggests that Ind
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Villemot, Sébastien, and Daniel Cohen. Self-Fulfilling Debt Crises in Theory and Practice. Inter-American Development Bank, 2006. http://dx.doi.org/10.18235/0010972.

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This paper analyzes econometrically how a country's post-crisis debt ratio could be forecast, in the aftermath of a debt crisis, from the previous debt-to-GDP ratio. A critical parameter is simply the debt-to-PPP-GDP ratio, where PPP-GDP is, in current international dollars, the Summers-Heston value. In this formulation, this paper shows that the Latin American paradox disappears. This then leads to a simple conclusion: debt crises are more frequent in Latin American countries because they have more damaging consequences on the market value of GDP. This itself appears to be closely related to
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Cooper, Julian. Another budget for a country at war: Military expenditure in Russia’s federal budget for 2024 and beyond. Stockholm International Peace Research Institute, 2023. http://dx.doi.org/10.55163/xroi1465.

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Russia’s war against Ukraine has been waged for over 650 days and has had a significant impact on the country’s economy and society. In April 2023 work started on a new three-year federal budget, which was signed into law at the end of November. It is based on a relatively optimistic economic forecast and is striking for the sharp increase in military spending planned for 2024. Spending under the budget chapter ‘national defence’ is expected to account for 29 per cent of total budget expenditure. This means that total military expenditure will increase to 7.1 per cent of GDP in 2024. Military
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Bonilla-González, Ricardo, Olga Lucía Acosta-Navarro, Roberto Steiner-Sampedro, et al. Report of the Board of Directors to the Congress of Colombia, March 2024. Banco de la República, 2024. http://dx.doi.org/10.32468/inf-jun-dir-con-rep-eng.03-2024.

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In 2023, the Colombian economy made progress in the macroeconomic adjustment required to achieve growth compatible with its productive capacity and external and price stability. This adjustment was reflected in the beginning of the convergence of inflation towards the target, which closed the year at 9.3%. This adjustment is an important step forward in the Board of Directors’ (BDBR) intention to drive inflation toward its target by mid-2025. Net foreign reserves increased and at the end of 2023 reached USD 59,608.3 million, and Banco de la República’s (the Central Bank of Colombia, Banrep) pr
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Semerikov, Serhiy, Hanna Kucherova, Vita Los, and Dmytro Ocheretin. Neural Network Analytics and Forecasting the Country's Business Climate in Conditions of the Coronavirus Disease (COVID-19). CEUR Workshop Proceedings, 2021. http://dx.doi.org/10.31812//123456789/4364.

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The prospects for doing business in countries are also determined by the business confidence index. The purpose of the article is to model trends in indicators that determine the state of the business climate of countries, in particular, the period of influence of the consequences of COVID-19 is of scientific interest. The approach is based on the preliminary results of substantiating a set of indicators and applying the taxonomy method to substantiate an alternative indicator of the business climate, the advantage of which is its advanced nature. The most significant factors influencing the b
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