Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-66-39-67
D. Sugaipov
This paper examines the impact of terms of trade news shocks on the dynamics of output, consumption, investment, trade balance and exchange rate in Russia. News in recent work are understood as the emergence of information about future changes in the economy. To identify expected shocks, we maximize the forecast error variance share of terms of trade time series over a finite horizon of several quarters. The results indicate that news shocks have a significant effect on the Russian economy and explain almost 60% of the variance of the main indicators.
{"title":"Estimating the impact of terms of trade news shocks on the Russian economy","authors":"D. Sugaipov","doi":"10.22394/1993-7601-2022-66-39-67","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-66-39-67","url":null,"abstract":"This paper examines the impact of terms of trade news shocks on the dynamics of output, consumption, investment, trade balance and exchange rate in Russia. News in recent work are understood as the emergence of information about future changes in the economy. To identify expected shocks, we maximize the forecast error variance share of terms of trade time series over a finite horizon of several quarters. The results indicate that news shocks have a significant effect on the Russian economy and explain almost 60% of the variance of the main indicators.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418213","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-66-118-143
Y. Raskina, O. Podkorytova, R. Kuchakov
We show the reporting heterogeneity bias into all 7 domains of health using the anchoring vignettes approach with the hierarchical ordered probit model. The paper uses the data of the Russian sample of the first wave of the global survey SAGE provided by the World Health Organization Study. The heterogeneity bias correction has changed the significance level of education level, permanent income, rural/ urban living, and marital status.
{"title":"Health determinants and the reporting heterogeneity bias in Russia: Anchoring vignettes approach","authors":"Y. Raskina, O. Podkorytova, R. Kuchakov","doi":"10.22394/1993-7601-2022-66-118-143","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-66-118-143","url":null,"abstract":"We show the reporting heterogeneity bias into all 7 domains of health using the anchoring vignettes approach with the hierarchical ordered probit model. The paper uses the data of the Russian sample of the first wave of the global survey SAGE provided by the World Health Organization Study. The heterogeneity bias correction has changed the significance level of education level, permanent income, rural/ urban living, and marital status.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418550","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-65-29-44
I. Denisova, V. Oksinenko, O. Chudinovskikh
We study the impact of migration policy liberalization under the Eurasian Economic Union (EAEU) on the welfare of households in sending country-members. We use the example of the Republic of Armenia. The Integrated Survey of the Living Standards of Households in Armenia for 2013–2017 is the informational basis of the study. Using the difference-in-differences approach, we identify a statistically significant causal relationship between joining the EAEU and the well-being of households. Armenia’s accession to the EAEU in 2015 made it possible to reduce the risk of poverty of labor migrant households by 2.5 percentage points, and the risk of acute poverty by 4.5 percentage points. The results prompt for further harmonization of the labor migration legislation of the participating countries, and the formation and mutual offset of pension rights of labor migrants within the EAEU in particular.
{"title":"The impact of the EAEU common labor market on the well-being of households of migrants: The case of Armenia","authors":"I. Denisova, V. Oksinenko, O. Chudinovskikh","doi":"10.22394/1993-7601-2022-65-29-44","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-65-29-44","url":null,"abstract":"We study the impact of migration policy liberalization under the Eurasian Economic Union (EAEU) on the welfare of households in sending country-members. We use the example of the Republic of Armenia. The Integrated Survey of the Living Standards of Households in Armenia for 2013–2017 is the informational basis of the study. Using the difference-in-differences approach, we identify a statistically significant causal relationship between joining the EAEU and the well-being of households. Armenia’s accession to the EAEU in 2015 made it possible to reduce the risk of poverty of labor migrant households by 2.5 percentage points, and the risk of acute poverty by 4.5 percentage points. The results prompt for further harmonization of the labor migration legislation of the participating countries, and the formation and mutual offset of pension rights of labor migrants within the EAEU in particular.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68417873","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-66-25-38
Diana Petrova
This article provides assessments of inflation expectations of Russian internet users based on the posts of the social media Vkontakte and Google Trends search queries during 2014–2021. We collect posts from news communities and search queries to measure inflation expectations using inflation‐related regular expressions. Inflation expectations based on internet data are highly correlated with actual inflation and reflect household concerns about price increases during 2014–2021. Granger causality tests have shown both measures of internet user inflation expectations can be potential predictors of actual inflation. An econometric analysis of the New Keynesian Phillips Curve is carried out using VAR from February 2015 to December 2021. The results suggest that an increase in inflation expectations is consistent with an increase in actual inflation and a tightening of monetary policy.
{"title":"Assessment of inflation expectations based on internet data","authors":"Diana Petrova","doi":"10.22394/1993-7601-2022-66-25-38","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-66-25-38","url":null,"abstract":"This article provides assessments of inflation expectations of Russian internet users based on the posts of the social media Vkontakte and Google Trends search queries during 2014–2021. We collect posts from news communities and search queries to measure inflation expectations using inflation‐related regular expressions. Inflation expectations based on internet data are highly correlated with actual inflation and reflect household concerns about price increases during 2014–2021. Granger causality tests have shown both measures of internet user inflation expectations can be potential predictors of actual inflation. An econometric analysis of the New Keynesian Phillips Curve is carried out using VAR from February 2015 to December 2021. The results suggest that an increase in inflation expectations is consistent with an increase in actual inflation and a tightening of monetary policy.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418145","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-67-46-73
I. Kalabikhina, Zarina Kazbekova, G. Klimenko, A. Kolotusha
Based on autoregressive models of distributed lags (ADL models), a sample of publications on maternity (family) capital, using an aggregator Public.ru and the developed list of normative legal acts on maternity capital, the authors assessed the intensity and dynamics of the media reaction to federal legislative initiatives regarding maternal capital, and built demographic rating of Russian regions on the publication activity of electronic media in response to the events of the federal legislative process in the field of maternity capital taking into account the different periodization of the actions of the maternity capital program.
{"title":"Demographic regional rankings by media activity on maternal (family) capital","authors":"I. Kalabikhina, Zarina Kazbekova, G. Klimenko, A. Kolotusha","doi":"10.22394/1993-7601-2022-67-46-73","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-67-46-73","url":null,"abstract":"Based on autoregressive models of distributed lags (ADL models), a sample of publications on maternity (family) capital, using an aggregator Public.ru and the developed list of normative legal acts on maternity capital, the authors assessed the intensity and dynamics of the media reaction to federal legislative initiatives regarding maternal capital, and built demographic rating of Russian regions on the publication activity of electronic media in response to the events of the federal legislative process in the field of maternity capital taking into account the different periodization of the actions of the maternity capital program.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418561","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-68-28-49
Maria Lycheva, A. Mironenkov, A. Kurbatskii, Dean Fantazzini
This paper investigates whether augmenting models with the variance risk premium (VRP) and Google search data improves the quality of the forecasts for real oil prices. We considered a time sample of monthly data from 2007 to 2019 that includes several episodes of high volatility in the oil market. Our evidence shows that penalized regressions provided the best forecasting performances across most of the forecasting horizons. Moreover, we found that models using the VRP as an additional predictor performed best for forecasts up to 6–12 months ahead forecasts, while models using Google data as an additional predictor performed better for longer‐term forecasts up to 12–24 months ahead. However, we found that the differences in forecasting performances were not statistically different for most models, and only the Principal Component Regression (PCR) and the Partial least squares (PLS) regression were consistently excluded from the set of best forecasting models. These results also held after a set of robustness checks that considered model specifications using a wider set of influential variables, a Hierarchical Vector Auto‐Regression model estimated with the LASSO, and a set of forecasting models using a simplified specification for Google Trends data.
{"title":"Forecasting oil prices with penalized regressions, variance risk premia and Google data","authors":"Maria Lycheva, A. Mironenkov, A. Kurbatskii, Dean Fantazzini","doi":"10.22394/1993-7601-2022-68-28-49","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-68-28-49","url":null,"abstract":"This paper investigates whether augmenting models with the variance risk premium (VRP) and Google search data improves the quality of the forecasts for real oil prices. We considered a time sample of monthly data from 2007 to 2019 that includes several episodes of high volatility in the oil market. Our evidence shows that penalized regressions provided the best forecasting performances across most of the forecasting horizons. Moreover, we found that models using the VRP as an additional predictor performed best for forecasts up to 6–12 months ahead forecasts, while models using Google data as an additional predictor performed better for longer‐term forecasts up to 12–24 months ahead. However, we found that the differences in forecasting performances were not statistically different for most models, and only the Principal Component Regression (PCR) and the Partial least squares (PLS) regression were consistently excluded from the set of best forecasting models. These results also held after a set of robustness checks that considered model specifications using a wider set of influential variables, a Hierarchical Vector Auto‐Regression model estimated with the LASSO, and a set of forecasting models using a simplified specification for Google Trends data.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418726","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-67-97-120
L. Serkov, S. Krasnykh
The article evaluates and compares behavioral neo‐Keynesian models obtained with two alternative ways of introducing irrational expectations. It is assumed that, in accordance with their heuristics, agents can be either short‐sighted with a short‐term forecast, or far‐sighted forecasters. Bayesian estimates, as well as a comparison of the second moments for the empirical data of the Russian economy and the variables of the studied models, showed that the behavioral model based on short‐term forecasts is better in agreement with the empirical data than the model based on long‐term forecasts and even compared to the model with rational expectations of agents. Using the Smirnov–Kolmogorov statistics, the parameters responsible for the behavior of the impulse response functions of the studied variables to external shocks, and, therefore, in a more general case, for the behavior of agents, are determined. All the above results confirmed by a posteriori estimate for these parameters.
{"title":"Analysis of the external shocks impact on the behavior of agents with limited expectations: The case of Russian economy","authors":"L. Serkov, S. Krasnykh","doi":"10.22394/1993-7601-2022-67-97-120","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-67-97-120","url":null,"abstract":"The article evaluates and compares behavioral neo‐Keynesian models obtained with two alternative ways of introducing irrational expectations. It is assumed that, in accordance with their heuristics, agents can be either short‐sighted with a short‐term forecast, or far‐sighted forecasters. Bayesian estimates, as well as a comparison of the second moments for the empirical data of the Russian economy and the variables of the studied models, showed that the behavioral model based on short‐term forecasts is better in agreement with the empirical data than the model based on long‐term forecasts and even compared to the model with rational expectations of agents. Using the Smirnov–Kolmogorov statistics, the parameters responsible for the behavior of the impulse response functions of the studied variables to external shocks, and, therefore, in a more general case, for the behavior of agents, are determined. All the above results confirmed by a posteriori estimate for these parameters.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418121","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-66-99-117
Anastasia Dubnovitskaya, K. Furmanov
We use the RLMS‐HSE data of Russian workers to investigate the relationship between pay satisfaction and both absolute and relative wages (relative wage is a ratio of the actual wage to the expected wage of an individual with a given vector of attribute). We found that the contribution of relative wages to pay satisfaction differs greatly for the relatively poor and rich workers (whose wages are lower and higher than the expected level respectively) with a greater effect of relative wages on pay satisfaction for relatively rich people rather than relatively poor ones.
{"title":"Relative wage as a determinant of pay satisfaction in Russia","authors":"Anastasia Dubnovitskaya, K. Furmanov","doi":"10.22394/1993-7601-2022-66-99-117","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-66-99-117","url":null,"abstract":"We use the RLMS‐HSE data of Russian workers to investigate the relationship between pay satisfaction and both absolute and relative wages (relative wage is a ratio of the actual wage to the expected wage of an individual with a given vector of attribute). We found that the contribution of relative wages to pay satisfaction differs greatly for the relatively poor and rich workers (whose wages are lower and higher than the expected level respectively) with a greater effect of relative wages on pay satisfaction for relatively rich people rather than relatively poor ones.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418458","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-68-73-92
I. Denisova, Nikita Varioshkin
In recent years, there have been significant fluctuations in world prices for essential goods, many of which have a significant share in the consumption basket of households. In this study, we analyze how fluctuations in world wheat prices affect the well‐being of Russian households. We provide estimates of average welfare losses and of welfare losses in different parts of welfare distribution. We also identify which socio‐demographic groups bear the brunt of such price shocks. For evaluation, a micro‐ simulation approach to model the expenditure part of household budgets is used. The empirical basis is the data of the Russian Longitudinal Monitoring Survey (RLMS) for 2020. When modeling a 50% rise in prices for bread, flour and pasta, the average losses amounted to 2.9% of total household expenditures, the largest losses, 5.5% of total expenditures, fall on households in the first decile. Those living in rural areas or urban‐type settlements, as well as households headed by a female pensioner, suffer losses at the level of 3% of total household expenses. Families with children are not among the most vulnerable groups with rising prices for bread, flour and pasta.
{"title":"The impact of foreign trade shocks on well-being of Russian households: Microsimulation approach","authors":"I. Denisova, Nikita Varioshkin","doi":"10.22394/1993-7601-2022-68-73-92","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-68-73-92","url":null,"abstract":"In recent years, there have been significant fluctuations in world prices for essential goods, many of which have a significant share in the consumption basket of households. In this study, we analyze how fluctuations in world wheat prices affect the well‐being of Russian households. We provide estimates of average welfare losses and of welfare losses in different parts of welfare distribution. We also identify which socio‐demographic groups bear the brunt of such price shocks. For evaluation, a micro‐ simulation approach to model the expenditure part of household budgets is used. The empirical basis is the data of the Russian Longitudinal Monitoring Survey (RLMS) for 2020. When modeling a 50% rise in prices for bread, flour and pasta, the average losses amounted to 2.9% of total household expenditures, the largest losses, 5.5% of total expenditures, fall on households in the first decile. Those living in rural areas or urban‐type settlements, as well as households headed by a female pensioner, suffer losses at the level of 3% of total household expenses. Families with children are not among the most vulnerable groups with rising prices for bread, flour and pasta.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418584","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}
Pub Date : 2022-01-01DOI: 10.22394/1993-7601-2022-65-65-76
V. Manevich, A. Peresetsky, P. Pogorelova
In the last ten years, cryptocurrencies have developed rapidly, of which bitcoin has the largest capitalization. With the development of the cryptocurrency market, more and more investors include bitcoin in their asset portfolio. In this regard, the question of the relationship between the volatility of the cryptocurrency market and the stock market is of particular interest. This article analyzes the common stochastic component of the realized volatility of bitcoin and e‐mini S&P futures. The assessment of the global stochastic component and its share in the volatility of the S&P 500 futures and bitcoin in the rolling window made it possible to analyze the dynamics of the relationship between the realized volatility of these two assets, as well as put forward a hypothesis about the causes and preconditions for volatility flows between the cryptocurrency market and the stock market.
{"title":"Stock market and cryptocurrency market volatility","authors":"V. Manevich, A. Peresetsky, P. Pogorelova","doi":"10.22394/1993-7601-2022-65-65-76","DOIUrl":"https://doi.org/10.22394/1993-7601-2022-65-65-76","url":null,"abstract":"In the last ten years, cryptocurrencies have developed rapidly, of which bitcoin has the largest capitalization. With the development of the cryptocurrency market, more and more investors include bitcoin in their asset portfolio. In this regard, the question of the relationship between the volatility of the cryptocurrency market and the stock market is of particular interest. This article analyzes the common stochastic component of the realized volatility of bitcoin and e‐mini S&P futures. The assessment of the global stochastic component and its share in the volatility of the S&P 500 futures and bitcoin in the rolling window made it possible to analyze the dynamics of the relationship between the realized volatility of these two assets, as well as put forward a hypothesis about the causes and preconditions for volatility flows between the cryptocurrency market and the stock market.","PeriodicalId":8045,"journal":{"name":"Applied Econometrics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68418342","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}
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