{"title":"在不确定情况下预报俄罗斯经济的宏观经济指标:考虑新闻情绪是否有用?","authors":"N. M. Makeeva, I. P. Stankevich, N. S. Lyubaykin","doi":"10.32609/0042-8736-2024-3-120-142","DOIUrl":null,"url":null,"abstract":"In this paper the following models are compared: restricted and unrestricted MIDAS-models (mixed data sampling models), MFBVAR-model (mixed frequency Bayesian vector autoregression), Linear model with regularization (MIDAS_L1-, MIDAS_L2and MIDAS_PC-model) and dynamic factor model. The results are compared with classical autoregression as a benchmark. Production indices for different industries and indicators characterizing Russian GDP and its components, energy prices and PMI of Russia and its main trading partners, as well as indicators derived from the analysis of sentiment of news articles published by a number of major media and blogs are used as explanatory variables. The paper also proposes a method of rapid assessment of the current state of the economy based on data for the first or first two months of the quarter in question only. The use of this approach in combination with news sentiment analysis allows to draw conclusions about current economic situation extremely rapidly. Models’ accuracy is assessed by cross-validation for periods before and after the Q2 2022, the significance of the effect of adding news variables is assessed using the Diebold—Mariano test. When testing during the crisis period (starting from the Q1 2022), the addition of news variables leads to an increase in accuracy for 45% of the models considered, and the average improvement (reduction in the average absolute error) was 1.39 points (the reduction in MAE for the science-based GDP growth rates of Russia is 0.64 p.p.). At the same time, in a calmer (pre-sanction) period, the advantage of news is less noticeable: an increase in accuracy was recorded in 30% of cases with an average decrease in error of 1.54 points (the decrease in MAE for Russia’s GDP growth rate is 0.26 p.p.), and the change accuracy of science data when adding variables reflecting the news background turns out to be statistically insignificant. Thus, the use of news sentiment is not a “silver bullet” in the task of nowcasting Russian GDP, but in times of crisis it can serve as a good and, importantly, very operative indicator of the state of the economy and can be used in conjunction with more traditional explanatory variables.","PeriodicalId":45534,"journal":{"name":"Voprosy Ekonomiki","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nowcasting the Russian economy macroeconomic indicators under uncertainty: Does taking into account the news sentiment help?\",\"authors\":\"N. M. Makeeva, I. P. Stankevich, N. S. 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The use of this approach in combination with news sentiment analysis allows to draw conclusions about current economic situation extremely rapidly. Models’ accuracy is assessed by cross-validation for periods before and after the Q2 2022, the significance of the effect of adding news variables is assessed using the Diebold—Mariano test. When testing during the crisis period (starting from the Q1 2022), the addition of news variables leads to an increase in accuracy for 45% of the models considered, and the average improvement (reduction in the average absolute error) was 1.39 points (the reduction in MAE for the science-based GDP growth rates of Russia is 0.64 p.p.). At the same time, in a calmer (pre-sanction) period, the advantage of news is less noticeable: an increase in accuracy was recorded in 30% of cases with an average decrease in error of 1.54 points (the decrease in MAE for Russia’s GDP growth rate is 0.26 p.p.), and the change accuracy of science data when adding variables reflecting the news background turns out to be statistically insignificant. 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引用次数: 0
摘要
本文比较了以下模型:有限制和无限制的 MIDAS 模型(混合数据采样模型)、MFBVAR 模型(混频贝叶斯向量自回归)、带正则化的线性模型(MIDAS_L1-、MIDAS_L2 和 MIDAS_PC-模型)以及动态因子模型。结果与作为基准的经典自回归进行了比较。不同行业的生产指数、反映俄罗斯国内生产总值及其组成部分特征的指标、俄罗斯及其主要贸易伙伴的能源价格和采购经理指数,以及通过分析一些主要媒体和博客发布的新闻文章的情绪得出的指标,都被用作解释变量。本文还提出了一种仅根据有关季度的头一个月或头两个月的数据快速评估经济现状的方法。将这种方法与新闻情感分析相结合,可以极其迅速地得出当前经济形势的结论。模型的准确性通过 2022 年第二季度之前和之后的交叉验证进行评估,加入新闻变量的效果的显著性通过 Diebold-Mariano 检验进行评估。在危机期间(从 2022 年第一季度开始)进行测试时,添加新闻变量导致 45% 的模型准确性提高,平均提高(平均绝对误差减少)1.39 个点(俄罗斯基于科学的 GDP 增长率的 MAE 减少了 0.64 个百分点)。同时,在较为平静的时期(制裁前),新闻的优势就不那么明显了:30%的情况下准确率有所提高,平均误差减少了 1.54 点(俄罗斯国内生产总值增长率的 MAE 减少了 0.26 个百分点)。因此,在预测俄罗斯国内生产总值的任务中,使用新闻情绪并不是 "灵丹妙药",但在危机时期,它可以作为经济状况的良好指标,而且重要的是,具有很强的可操作性,并可与更多传统解释变量结合使用。
Nowcasting the Russian economy macroeconomic indicators under uncertainty: Does taking into account the news sentiment help?
In this paper the following models are compared: restricted and unrestricted MIDAS-models (mixed data sampling models), MFBVAR-model (mixed frequency Bayesian vector autoregression), Linear model with regularization (MIDAS_L1-, MIDAS_L2and MIDAS_PC-model) and dynamic factor model. The results are compared with classical autoregression as a benchmark. Production indices for different industries and indicators characterizing Russian GDP and its components, energy prices and PMI of Russia and its main trading partners, as well as indicators derived from the analysis of sentiment of news articles published by a number of major media and blogs are used as explanatory variables. The paper also proposes a method of rapid assessment of the current state of the economy based on data for the first or first two months of the quarter in question only. The use of this approach in combination with news sentiment analysis allows to draw conclusions about current economic situation extremely rapidly. Models’ accuracy is assessed by cross-validation for periods before and after the Q2 2022, the significance of the effect of adding news variables is assessed using the Diebold—Mariano test. When testing during the crisis period (starting from the Q1 2022), the addition of news variables leads to an increase in accuracy for 45% of the models considered, and the average improvement (reduction in the average absolute error) was 1.39 points (the reduction in MAE for the science-based GDP growth rates of Russia is 0.64 p.p.). At the same time, in a calmer (pre-sanction) period, the advantage of news is less noticeable: an increase in accuracy was recorded in 30% of cases with an average decrease in error of 1.54 points (the decrease in MAE for Russia’s GDP growth rate is 0.26 p.p.), and the change accuracy of science data when adding variables reflecting the news background turns out to be statistically insignificant. Thus, the use of news sentiment is not a “silver bullet” in the task of nowcasting Russian GDP, but in times of crisis it can serve as a good and, importantly, very operative indicator of the state of the economy and can be used in conjunction with more traditional explanatory variables.