{"title":"用MFBVAR模型预测俄罗斯主要宏观经济变量的临近预报","authors":"Nikita Fokin","doi":"10.18288/1994-5124-2023-3-110-135","DOIUrl":null,"url":null,"abstract":"This paper examines the quality of nowcasts and forecasts for Russian GDP and its components (in constant and current prices) using a mixed-frequency Bayesian vector autoregression model (MFBVAR) which is currently one of the most advanced time series forecasting models. It enables use of quarterly and monthly frequency data within a single monthly frequency VAR model in a statespace form while taking into account the intra-quarter dynamics of monthly indicators; this approach improves forecasting accuracy when new monthly data is published. The MFBVAR model’s resistance to the jagged edge problem is especially important for real-time forecasting, and it can incorporate a large number of predictors because of its Bayesian estimation with a Minnesota-type prior distribution. The paper sets up three experiments with differing availability of monthly data in order to test pseudo out-of-sample nowcasting and forecasting. The MFBVAR model exhibits statistically significant outperformance compared to a naïve benchmark, as well as to ARIMA and quarterly BVAR models, in nowcasting and forecasting a few steps ahead for GDP, consumption and foreign trade variables. The test sample is also quite representative and covers two crisis periods, specifically 2015 and 2020. In both crises, the model accurately estimates the scale of the recession and recovery of economic activity. Nevertheless, there was no significant improvement in the quality of forecasts when new available monthly data was introduced.","PeriodicalId":43996,"journal":{"name":"Ekonomicheskaya politika","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nowcasting and Forecasting Key Russian Macroeconomic Variables With the MFBVAR Model\",\"authors\":\"Nikita Fokin\",\"doi\":\"10.18288/1994-5124-2023-3-110-135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper examines the quality of nowcasts and forecasts for Russian GDP and its components (in constant and current prices) using a mixed-frequency Bayesian vector autoregression model (MFBVAR) which is currently one of the most advanced time series forecasting models. It enables use of quarterly and monthly frequency data within a single monthly frequency VAR model in a statespace form while taking into account the intra-quarter dynamics of monthly indicators; this approach improves forecasting accuracy when new monthly data is published. The MFBVAR model’s resistance to the jagged edge problem is especially important for real-time forecasting, and it can incorporate a large number of predictors because of its Bayesian estimation with a Minnesota-type prior distribution. The paper sets up three experiments with differing availability of monthly data in order to test pseudo out-of-sample nowcasting and forecasting. The MFBVAR model exhibits statistically significant outperformance compared to a naïve benchmark, as well as to ARIMA and quarterly BVAR models, in nowcasting and forecasting a few steps ahead for GDP, consumption and foreign trade variables. The test sample is also quite representative and covers two crisis periods, specifically 2015 and 2020. In both crises, the model accurately estimates the scale of the recession and recovery of economic activity. Nevertheless, there was no significant improvement in the quality of forecasts when new available monthly data was introduced.\",\"PeriodicalId\":43996,\"journal\":{\"name\":\"Ekonomicheskaya politika\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ekonomicheskaya politika\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18288/1994-5124-2023-3-110-135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ekonomicheskaya politika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18288/1994-5124-2023-3-110-135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
Nowcasting and Forecasting Key Russian Macroeconomic Variables With the MFBVAR Model
This paper examines the quality of nowcasts and forecasts for Russian GDP and its components (in constant and current prices) using a mixed-frequency Bayesian vector autoregression model (MFBVAR) which is currently one of the most advanced time series forecasting models. It enables use of quarterly and monthly frequency data within a single monthly frequency VAR model in a statespace form while taking into account the intra-quarter dynamics of monthly indicators; this approach improves forecasting accuracy when new monthly data is published. The MFBVAR model’s resistance to the jagged edge problem is especially important for real-time forecasting, and it can incorporate a large number of predictors because of its Bayesian estimation with a Minnesota-type prior distribution. The paper sets up three experiments with differing availability of monthly data in order to test pseudo out-of-sample nowcasting and forecasting. The MFBVAR model exhibits statistically significant outperformance compared to a naïve benchmark, as well as to ARIMA and quarterly BVAR models, in nowcasting and forecasting a few steps ahead for GDP, consumption and foreign trade variables. The test sample is also quite representative and covers two crisis periods, specifically 2015 and 2020. In both crises, the model accurately estimates the scale of the recession and recovery of economic activity. Nevertheless, there was no significant improvement in the quality of forecasts when new available monthly data was introduced.
期刊介绍:
Ekonomicheskaya Politika is a broad-range economic journal devoted primarily to the study of the economic policy of present-day Russia as well as global economic problems. The subject matters of articles includes macroeconomic, fiscal, monetary, industrial, social, regulation and competition policyand more. The journal also publishes theoretical papers in such areas as political economy, general economic theory, welfare economics, law and economics,and institutional economics.. The character and the scope of economic problems studied in many publications require a multidisciplinary approach, consistent with the editorial policy of the journal. While the thematic scope of articles is generally related to Russia, the aim of editorial policy is to cover politico-economic processes in the modern world and international economic relations, as well. In addition, Ekonomicheskaya Politika publishes Russian translations of classical and significant modern works of foreign economists.