This paper investigates the nonlinearity of exchange rate pass-through in the Brazilian economy during the inflation targeting period (2000–2018) using a Markov-switching new Keynesian DSGE model. We find evidence of two distinct regimes for exchange rate pass-through and for the volatility of shocks to inflation. Under the so-called ‘normal’ regime, the long-run pass-through to consumer prices inflation is estimated as almost zero, only 0.00057 of a percentage point given a 1% exchange rate shock. In comprasion, the expected pass-through to inflation under a ‘crisis’ regime is 0.1035 of a percentage point, for the same exchange rate shock. These results allow us to identify four distinct cycles for exchange rate pass-through during the inflation targeting period in Brazil, and suggest that higher central bank credibility and anchored inflation expectations may be related to lower levels of pass-through.
{"title":"Exchange Rate Pass-Through in Brazil: А Markov Switching DSGE Estimation for the Inflation Targeting Period","authors":"F. A. Marodin, M. S. Portugal","doi":"10.31477/RJMF.201901.36","DOIUrl":"https://doi.org/10.31477/RJMF.201901.36","url":null,"abstract":"This paper investigates the nonlinearity of exchange rate pass-through in the Brazilian economy during the inflation targeting period (2000–2018) using a Markov-switching new Keynesian DSGE model. We find evidence of two distinct regimes for exchange rate pass-through and for the volatility of shocks to inflation. Under the so-called ‘normal’ regime, the long-run pass-through to consumer prices inflation is estimated as almost zero, only 0.00057 of a percentage point given a 1% exchange rate shock. In comprasion, the expected pass-through to inflation under a ‘crisis’ regime is 0.1035 of a percentage point, for the same exchange rate shock. These results allow us to identify four distinct cycles for exchange rate pass-through during the inflation targeting period in Brazil, and suggest that higher central bank credibility and anchored inflation expectations may be related to lower levels of pass-through.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116102463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fiscal devaluation, meaning a shift from payroll to indirect taxes, can be beneficial for a small open economy such as the Czech Republic. Using a structural fiscal DSGE model, I show that fiscal devaluation can boost real GDP growth by 0.5 percentage points in the first year, when a budget-neutral tax shift of the magnitude of 1% of GDP occurs from direct taxes to consumption tax. I also calculate fiscal multipliers for several revenue and expenditure categories of the government budget, the largest of which (after the first year) are government consumption (0.6), government investment (0.5), and social security contributions paid by employers (0.4). These results corroborate the hypothesis that the government can easily boost the economy by adjusting fiscal instruments appropriately.
{"title":"Fiscal Devaluation in a Small Open Economy","authors":"Róbert Ambriško, Cerge–Ei","doi":"10.31477/rjmf.201901.67","DOIUrl":"https://doi.org/10.31477/rjmf.201901.67","url":null,"abstract":"Fiscal devaluation, meaning a shift from payroll to indirect taxes, can be beneficial for a small open economy such as the Czech Republic. Using a structural fiscal DSGE model, I show that fiscal devaluation can boost real GDP growth by 0.5 percentage points in the first year, when a budget-neutral tax shift of the magnitude of 1% of GDP occurs from direct taxes to consumption tax. I also calculate fiscal multipliers for several revenue and expenditure categories of the government budget, the largest of which (after the first year) are government consumption (0.6), government investment (0.5), and social security contributions paid by employers (0.4). These results corroborate the hypothesis that the government can easily boost the economy by adjusting fiscal instruments appropriately.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132639643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, I forecast CPI inflation in Russia by the method of Dynamic Model Averaging (Raftery et al., 2010; Koop and Korobilis, 2012) pseudo out-of-sample on historical data. This method can be viewed as an extension of the Bayesian Model Averaging where the identity of a model that generates data and model parameters are allowed to change over time. The DMA is shown not to produce forecasts superior to simpler benchmarks even if a subset of individual predictors is pre-selected “with the benefit of hindsight” on the full sample. The two groups of predictors that feature the highest average values of the posterior inclusion probability are loans to non-financial firms and individuals along with actual and anticipated wages.
在本研究中，我采用动态模型平均的方法预测俄罗斯的CPI通胀(Raftery et al.， 2010;Koop和Korobilis, 2012)历史数据的伪样本外。这种方法可以看作是贝叶斯模型平均的扩展，其中允许生成数据和模型参数的模型的身份随时间变化。事实证明，DMA的预测结果并不优于更简单的基准，即使是在“事后诸明的好处”下，对整个样本预先选择了个别预测指标的子集。后验包容概率平均值最高的两组预测因子是对非金融公司和个人的贷款以及实际和预期工资。
{"title":"Forecasting Inflation in Russia Using Dynamic Model Averaging","authors":"K. Styrin","doi":"10.31477/RJMF.201901.03","DOIUrl":"https://doi.org/10.31477/RJMF.201901.03","url":null,"abstract":"In this study, I forecast CPI inflation in Russia by the method of Dynamic Model Averaging (Raftery et al., 2010; Koop and Korobilis, 2012) pseudo out-of-sample on historical data. This method can be viewed as an extension of the Bayesian Model Averaging where the identity of a model that generates data and model parameters are allowed to change over time. The DMA is shown not to produce forecasts superior to simpler benchmarks even if a subset of individual predictors is pre-selected “with the benefit of hindsight” on the full sample. The two groups of predictors that feature the highest average values of the posterior inclusion probability are loans to non-financial firms and individuals along with actual and anticipated wages.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122647299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of stress-testing is to test the resilience of the banking sector to negative developments on the financial markets and in the real economy. One of the key issues in stress-testing is the translation of various scenarios into bank-level risk parameters and the determination of their impact on banks’ profitability or loss-bearing capacity. This paper has two objectives. The first is to identify key macroeconomic determinants of the loan loss provision ratio and net interest margin. The second is to show how satellite models can be applied in stress-testing exercises to determine the impact of macroeconomic outcomes on banks. We contribute to the empirical literature by defining macroeconomic determinants for credit risk on the basis of three different credit portfolios (consumer, mortgage, and corporate) for banks operating in Poland. Our estimation results suggest that economic growth, the labour market, and market interest rates have a significant influence on the net interest margin and loan loss provision ratio.
{"title":"Forecasting the Net Interest Margin and Loan Loss Provision Ratio of Banks in Various Economic Scenarios: Evidence from Poland","authors":"M. Borsuk","doi":"10.31477/RJMF.201901.89","DOIUrl":"https://doi.org/10.31477/RJMF.201901.89","url":null,"abstract":"The aim of stress-testing is to test the resilience of the banking sector to negative developments on the financial markets and in the real economy. One of the key issues in stress-testing is the translation of various scenarios into bank-level risk parameters and the determination of their impact on banks’ profitability or loss-bearing capacity. This paper has two objectives. The first is to identify key macroeconomic determinants of the loan loss provision ratio and net interest margin. The second is to show how satellite models can be applied in stress-testing exercises to determine the impact of macroeconomic outcomes on banks. We contribute to the empirical literature by defining macroeconomic determinants for credit risk on the basis of three different credit portfolios (consumer, mortgage, and corporate) for banks operating in Poland. Our estimation results suggest that economic growth, the labour market, and market interest rates have a significant influence on the net interest margin and loan loss provision ratio.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115880417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a pseudo real-time out-of-sample forecast exercise for short-term forecasting and nowcasting quarterly Russian GDP growth with mixed-frequency data. We employ a large set of indicators and study their predictive power for different subperiods within the forecast evaluation period 2008–2016. Four indicators consistently figure in the list of top-performing indicators: the Rosstat key sector economic output index, the OECD composite leading indicator for Russia, household banking deposits, and money supply M2. Aside from these indicators, the top indicators in the 2008–2011 evaluation period are traditional real-sector variables, while those in the 2012–2016 evaluation period largely comprise monetary, banking sector and financial market variables. We also compare the forecast accuracy of three different mixed-frequency forecasting model classes (bridge equations, MIDAS models, and U-MIDAS models). Differences between the performance of model classes are generally small, but for the 2008–2011 period MIDAS models and U-MIDAS models outperform bridge equation models.
{"title":"Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data","authors":"H. Mikosch, L. Solanko","doi":"10.31477/RJMF.201901.19","DOIUrl":"https://doi.org/10.31477/RJMF.201901.19","url":null,"abstract":"This paper presents a pseudo real-time out-of-sample forecast exercise for short-term forecasting and nowcasting quarterly Russian GDP growth with mixed-frequency data. We employ a large set of indicators and study their predictive power for different subperiods within the forecast evaluation period 2008–2016. Four indicators consistently figure in the list of top-performing indicators: the Rosstat key sector economic output index, the OECD composite leading indicator for Russia, household banking deposits, and money supply M2. Aside from these indicators, the top indicators in the 2008–2011 evaluation period are traditional real-sector variables, while those in the 2012–2016 evaluation period largely comprise monetary, banking sector and financial market variables. We also compare the forecast accuracy of three different mixed-frequency forecasting model classes (bridge equations, MIDAS models, and U-MIDAS models). Differences between the performance of model classes are generally small, but for the 2008–2011 period MIDAS models and U-MIDAS models outperform bridge equation models.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116968662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In September, the Bank of Russia held a joint workshop with the International Monetary Fund in Moscow on macroprudential stress testing. IMF experts, members of the research community, staff members of central banks, and regulators from 16 countries shared their approaches to and methodologies of macroprudential stress testing and systemic risk analysis. This publication provides a brief review of the workshop and the key findings of studies presented.
{"title":"Review of the Bank of Russia – IMF Workshop \"Recent Developments in Macroprudential Stress Testing\"","authors":"Elizaveta Danilova, E. Rumyantsev, I. Shevchuk","doi":"10.31477/RJMF.201804.60","DOIUrl":"https://doi.org/10.31477/RJMF.201804.60","url":null,"abstract":"In September, the Bank of Russia held a joint workshop with the International Monetary Fund in Moscow on macroprudential stress testing. IMF experts, members of the research community, staff members of central banks, and regulators from 16 countries shared their approaches to and methodologies of macroprudential stress testing and systemic risk analysis. This publication provides a brief review of the workshop and the key findings of studies presented.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121252888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper outlines a methodology for constructing a high-frequency indicator of economic activity in Russia. News stories from internet resources are used as data sources. News data is analyzed using text mining and machine learning methods, which, although developed relatively recently, have quickly found wide application in scientific research, including economic studies. This is because news is not only a key source of information but a way to gauge the sentiment of journalists and survey respondents about the current situation and convert it into quantitative data.
{"title":"Text Mining-based Economic Activity Estimation","authors":"Ksenia Yakovleva","doi":"10.31477/RJMF.201804.26","DOIUrl":"https://doi.org/10.31477/RJMF.201804.26","url":null,"abstract":"This paper outlines a methodology for constructing a high-frequency indicator of economic activity in Russia. News stories from internet resources are used as data sources. News data is analyzed using text mining and machine learning methods, which, although developed relatively recently, have quickly found wide application in scientific research, including economic studies. This is because news is not only a key source of information but a way to gauge the sentiment of journalists and survey respondents about the current situation and convert it into quantitative data.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129747021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines the relationship between inflation and population age structure for emerging market economies. We form an unbalanced panel of data for 21 countries for the period 1950-2017 and include a number of additional control variables – terms of trade, exchange rate regime, debt-to-GDP ratio, broad money supply growth rates, and PPP-adjusted GDP per capita index. After estimating a variety of model specifications and robustness checks we conclude that the elderly group (65+) in these sample of countries is deflationary, the young group (0-19) shows weak signs of being deflationary, and the working group (20-64) is found to be inflationary. The deflationary effect of the elderly has been found in some studies for OECD countries, but the findings regarding the young group being deflationary and the working group being inflationary are new. Therefore, the question about the general empirical relation between inflation and the population age structure remains unsettled, and it is probable that the relation between population age structure and other macroeconomic variables is different for emerging market economies and for advanced countries.
{"title":"Inflation and Population Age Structure: The Case of Emerging Economies","authors":"D. Antonova, Y. Vymyatnina","doi":"10.31477/RJMF.201804.03","DOIUrl":"https://doi.org/10.31477/RJMF.201804.03","url":null,"abstract":"This paper examines the relationship between inflation and population age structure for emerging market economies. We form an unbalanced panel of data for 21 countries for the period 1950-2017 and include a number of additional control variables – terms of trade, exchange rate regime, debt-to-GDP ratio, broad money supply growth rates, and PPP-adjusted GDP per capita index. After estimating a variety of model specifications and robustness checks we conclude that the elderly group (65+) in these sample of countries is deflationary, the young group (0-19) shows weak signs of being deflationary, and the working group (20-64) is found to be inflationary. The deflationary effect of the elderly has been found in some studies for OECD countries, but the findings regarding the young group being deflationary and the working group being inflationary are new. Therefore, the question about the general empirical relation between inflation and the population age structure remains unsettled, and it is probable that the relation between population age structure and other macroeconomic variables is different for emerging market economies and for advanced countries.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121324064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inflation forecasting is an important practical problem. This paper proposes a solution to this problem for Russia using several basic machine learning methods: LASSO, Ridge, Elastic Net, Random Forest, and Boosting. Despite the fact that these methods already existed in the early 2000s, for a long time they remained almost unnoticed in the professional literature related to the forecasting of inflation in general, and Russian inflation in particular. This paper is one of the first attempts to apply machine learning methods to the forecasting of inflation in Russia. The present empirical study demostrates that the Random Forest model and the Boosting model are at least as good at inflation forecasting as more traditional models, such as Random Walk and autoregression. The main result of this paper is the confirmation of the possibility of more accurate forecasting of inflation in Russia using machine learning methods.
通货膨胀预测是一个重要的现实问题。本文提出了俄罗斯使用几种基本机器学习方法的解决方案:LASSO, Ridge, Elastic Net, Random Forest和Boosting。尽管这些方法在21世纪初就已经存在，但在很长一段时间里，它们在与通胀预测相关的专业文献中几乎没有被注意到，尤其是俄罗斯的通胀预测。本文是将机器学习方法应用于俄罗斯通货膨胀预测的首次尝试之一。目前的实证研究表明，随机森林模型和Boosting模型在通胀预测方面至少与Random Walk和自回归等更传统的模型一样好。本文的主要结果是确认了使用机器学习方法更准确地预测俄罗斯通货膨胀的可能性。
{"title":"Inflation Forecasting Using Machine Learning Methods","authors":"I. Baybuza","doi":"10.31477/RJMF.201804.42","DOIUrl":"https://doi.org/10.31477/RJMF.201804.42","url":null,"abstract":"Inflation forecasting is an important practical problem. This paper proposes a solution to this problem for Russia using several basic machine learning methods: LASSO, Ridge, Elastic Net, Random Forest, and Boosting. Despite the fact that these methods already existed in the early 2000s, for a long time they remained almost unnoticed in the professional literature related to the forecasting of inflation in general, and Russian inflation in particular. This paper is one of the first attempts to apply machine learning methods to the forecasting of inflation in Russia. The present empirical study demostrates that the Random Forest model and the Boosting model are at least as good at inflation forecasting as more traditional models, such as Random Walk and autoregression. The main result of this paper is the confirmation of the possibility of more accurate forecasting of inflation in Russia using machine learning methods.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130429611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Isakov, Vtb Capital, Petr Grishin, Oleg Gorlinsky, Vtb
This article is a response to the review of Adrian et al. (2018) by Yudaeva (2018), which summarizes the case of the Bank of Russia against the publication of key rate forecasts, a communication strategy known as conventional forward guidance. We believe that the case in favour of publishing the Bank of Russia’s key rate forecast is at present not stated sufficiently coherently. Our note attempts to fill this gap. Extending the argument put forward by Adrian et al. (2018) we provide a comprehensive review of the working papers, staff notes and leadership comments related to interest rate expectations and monetary policy communication by four central banks that have had practical experience with the application of conventional forward guidance. We conclude with an evaluation of the validity of the commonly voiced concerns regarding its adoption in Russia, based on the reviewed literature.
{"title":"Fear of Forward Guidance","authors":"A. Isakov, Vtb Capital, Petr Grishin, Oleg Gorlinsky, Vtb","doi":"10.31477/RJMF.201804.84","DOIUrl":"https://doi.org/10.31477/RJMF.201804.84","url":null,"abstract":"This article is a response to the review of Adrian et al. (2018) by Yudaeva (2018), which summarizes the case of the Bank of Russia against the publication of key rate forecasts, a communication strategy known as conventional forward guidance. We believe that the case in favour of publishing the Bank of Russia’s key rate forecast is at present not stated sufficiently coherently. Our note attempts to fill this gap. Extending the argument put forward by Adrian et al. (2018) we provide a comprehensive review of the working papers, staff notes and leadership comments related to interest rate expectations and monetary policy communication by four central banks that have had practical experience with the application of conventional forward guidance. We conclude with an evaluation of the validity of the commonly voiced concerns regarding its adoption in Russia, based on the reviewed literature.","PeriodicalId":358692,"journal":{"name":"Russian Journal of Money and Finance","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126193811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：