We use information in higher-order moments to identify aggregate supply and aggregate demand shocks for the U.S. economy. Traditional methods based on sign restrictions and/or second-order moments yield only “set” or “interval” identification but higher-order moments are shown to considerably aid identification. Aggregate supply shocks dominated recessions in the 1970s and early 1980s, while aggregate demand shocks dominated most later recessions. The Great Recession of 2008-2009 and the pandemic-induced recession of 2020 exhibited large components due to both negative aggregate demand and negative aggregate supply shocks.
{"title":"Identifying Aggregate Demand and Supply Shocks Using Sign Restrictions and Higher-Order Moments","authors":"G. Bekaert, Eric C. Engstrom, Andrey Ermolov","doi":"10.2139/ssrn.3857959","DOIUrl":"https://doi.org/10.2139/ssrn.3857959","url":null,"abstract":"We use information in higher-order moments to identify aggregate supply and aggregate demand shocks for the U.S. economy. Traditional methods based on sign restrictions and/or second-order moments yield only “set” or “interval” identification but higher-order moments are shown to considerably aid identification. Aggregate supply shocks dominated recessions in the 1970s and early 1980s, while aggregate demand shocks dominated most later recessions. The Great Recession of 2008-2009 and the pandemic-induced recession of 2020 exhibited large components due to both negative aggregate demand and negative aggregate supply shocks.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122146054","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}
F. D’Amuri, Marta de Philippis, Elisa Guglielminetti, Salvatore Lo Bello
Motivated by the magnitude and cyclicality of transitions into and out of the labour force, we jointly estimate natural unemployment and participation rates through a forward-looking Phillips curve informed by structural labour market flows and demographic trends. We find that the estimated reaction of inflation to the participation gap is twice as large as that to the unemployment gap, and that the participation margin accounts for a significant share of total slack. Moreover, by exploiting a far-reaching and unexpected pension reform, we study the effects of a sudden expansion in labour supply that was not directly related to unemployment. The reform triggered a marked reduction in the employment to inactivity transitions of the elderly, determining an increase in natural participation (stronger than that in observed participation) but not in natural unemployment. Thus, the trends in activity explain in part why inflation has been so low in the recent years.
{"title":"Natural Unemployment and Activity Rates: Flow-Based Determinants and Implications for Price Dynamics","authors":"F. D’Amuri, Marta de Philippis, Elisa Guglielminetti, Salvatore Lo Bello","doi":"10.2139/ssrn.3827520","DOIUrl":"https://doi.org/10.2139/ssrn.3827520","url":null,"abstract":"Motivated by the magnitude and cyclicality of transitions into and out of the labour force, we jointly estimate natural unemployment and participation rates through a forward-looking Phillips curve informed by structural labour market flows and demographic trends. We find that the estimated reaction of inflation to the participation gap is twice as large as that to the unemployment gap, and that the participation margin accounts for a significant share of total slack. Moreover, by exploiting a far-reaching and unexpected pension reform, we study the effects of a sudden expansion in labour supply that was not directly related to unemployment. The reform triggered a marked reduction in the employment to inactivity transitions of the elderly, determining an increase in natural participation (stronger than that in observed participation) but not in natural unemployment. Thus, the trends in activity explain in part why inflation has been so low in the recent years.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000073","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}
Ten years ago we presented a modified version of Okun’s law for the biggest developed economies and reported its excellent predictive power. In this study, we revisit the original models using the estimates of real GDP per capita and unemployment rate between 2010 and 2019. The initial results show that the change in unemployment rate can be accurately predicted by variations in the rate of real economic growth. There is a discrete version of the model which is represented by a piecewise linear dependence of the annual increment in unemployment rate on the annual rate of change in real GDP per capita. The lengths of the country-dependent time segments are defined by breaks in the GDP measurement units associated with definitional revisions to the nominal GDP and GDP deflator (dGDP). The difference between the CPI and dGDP indices since the beginning of measurements reveals the years of such breaks. Statistically, the link between the studied variables in the revised models is characterized by the coefficient of determination in the range from R2=0.866 (Australia) to R2=0.977 (France). The residual errors can be likely associated with the measurement errors, e.g. the estimates of real GDP per capita from various sources differ by tens of percent. The obtained results confirm the original finding on the absence of structural unemployment in the studied developed countries.
{"title":"The Link between Unemployment and Real Economic Growth in Developed Countries","authors":"I. Kitov","doi":"10.2139/ssrn.3776796","DOIUrl":"https://doi.org/10.2139/ssrn.3776796","url":null,"abstract":"Ten years ago we presented a modified version of Okun’s law for the biggest developed economies and reported its excellent predictive power. In this study, we revisit the original models using the estimates of real GDP per capita and unemployment rate between 2010 and 2019. The initial results show that the change in unemployment rate can be accurately predicted by variations in the rate of real economic growth. There is a discrete version of the model which is represented by a piecewise linear dependence of the annual increment in unemployment rate on the annual rate of change in real GDP per capita. The lengths of the country-dependent time segments are defined by breaks in the GDP measurement units associated with definitional revisions to the nominal GDP and GDP deflator (dGDP). The difference between the CPI and dGDP indices since the beginning of measurements reveals the years of such breaks. Statistically, the link between the studied variables in the revised models is characterized by the coefficient of determination in the range from R2=0.866 (Australia) to R2=0.977 (France). The residual errors can be likely associated with the measurement errors, e.g. the estimates of real GDP per capita from various sources differ by tens of percent. The obtained results confirm the original finding on the absence of structural unemployment in the studied developed countries.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130301830","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}
We analyze the information content of alternative inflation expectations measures, including those from consumers, firms, experts and financial markets, in the context of open economy Phillips curves. We adopt a thick modeling approach with rolling regressions and we assess the results of an out-of sample conditional forecasting exercise by means of meta regressions. The information content varies substantially across inflation expectations measures. In particular, we find that those from consumers and firms are better at predicting inflation if compared to those from experts and, especially, those from financial markets.
{"title":"Inflation Expectations in Euro Area Phillips Curves","authors":"L. J. Álvarez, M. Correa‐López","doi":"10.2139/ssrn.3654114","DOIUrl":"https://doi.org/10.2139/ssrn.3654114","url":null,"abstract":"We analyze the information content of alternative inflation expectations measures, including those from consumers, firms, experts and financial markets, in the context of open economy Phillips curves. We adopt a thick modeling approach with rolling regressions and we assess the results of an out-of sample conditional forecasting exercise by means of meta regressions. The information content varies substantially across inflation expectations measures. In particular, we find that those from consumers and firms are better at predicting inflation if compared to those from experts and, especially, those from financial markets.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132881217","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 structural model to account for a country's business cycle fluctuations. Our model is a two-sector open economy dynamic stochastic general equilibrium model in which production structure is classified by the intensity levels of primary energy (oil) use by firms in each sector. We estimate this model on unfiltered data by Indirect Inference, which is a simulation-based econometric approach. The results establish the fit of our model to the observed data. The estimated model is then scrutinized concerning the three epochs in US postwar economic activity, as we ask: Of the twenty-two structural shocks admitted into the model, which were the prime drivers of the Great Inflation, the Great Moderation, and the Great Recession?
{"title":"Postwar Business Cycles: What Are the Prime Drivers?","authors":"David Meenagh, P. Minford, Ọ. Oyèkọ́lá","doi":"10.2139/ssrn.3547761","DOIUrl":"https://doi.org/10.2139/ssrn.3547761","url":null,"abstract":"This paper presents a structural model to account for a country's business cycle fluctuations. Our model is a two-sector open economy dynamic stochastic general equilibrium model in which production structure is classified by the intensity levels of primary energy (oil) use by firms in each sector. We estimate this model on unfiltered data by Indirect Inference, which is a simulation-based econometric approach. The results establish the fit of our model to the observed data. The estimated model is then scrutinized concerning the three epochs in US postwar economic activity, as we ask: Of the twenty-two structural shocks admitted into the model, which were the prime drivers of the Great Inflation, the Great Moderation, and the Great Recession?","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115569930","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 article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007–08, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth, and inflation. In the context of unstable environments, I discuss how to assess models’ forecasting ability; how to robustify models’ estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models’ parameters are neither necessary nor sufficient to generate time variation in models’ forecasting performance: thus, one should not test for breaks in models’ parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models’ forecasting performance are more appropriate than traditional, average measures. (JEL C51, C53, E31, E32, E37, F37)
{"title":"Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them","authors":"B. Rossi","doi":"10.1257/jel.20201479","DOIUrl":"https://doi.org/10.1257/jel.20201479","url":null,"abstract":"This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007–08, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth, and inflation. In the context of unstable environments, I discuss how to assess models’ forecasting ability; how to robustify models’ estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models’ parameters are neither necessary nor sufficient to generate time variation in models’ forecasting performance: thus, one should not test for breaks in models’ parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models’ forecasting performance are more appropriate than traditional, average measures. (JEL C51, C53, E31, E32, E37, F37)","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128881569","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}
Michael T. Owyang, Daniel Soques, Neville R. Francis
We study the comovement of international business cycles in a time series clustering model with regime-switching. We extend the framework of Hamilton and Owyang (2012) to include time-varying transition probabilities to determine what drives similarities in business cycle turning points. We find four groups, or ?clusters?, of countries which experience idiosyncratic recessions relative to the global cycle. Additionally, we find the primary indicators of international recessions to be fluctuations in equity markets and geopolitical uncertainty. In out-of-sample forecasting exercises, we find that our model is an improvement over standard benchmark models for forecasting both aggregate output growth and country-level recessions.
{"title":"Business Cycles Across Space and Time","authors":"Michael T. Owyang, Daniel Soques, Neville R. Francis","doi":"10.20955/wp.2019.010","DOIUrl":"https://doi.org/10.20955/wp.2019.010","url":null,"abstract":"We study the comovement of international business cycles in a time series clustering model with regime-switching. We extend the framework of Hamilton and Owyang (2012) to include time-varying transition probabilities to determine what drives similarities in business cycle turning points. We find four groups, or ?clusters?, of countries which experience idiosyncratic recessions relative to the global cycle. Additionally, we find the primary indicators of international recessions to be fluctuations in equity markets and geopolitical uncertainty. In out-of-sample forecasting exercises, we find that our model is an improvement over standard benchmark models for forecasting both aggregate output growth and country-level recessions.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129036367","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}
We analyze the transmission of producer price in inflation shocks across the U.S. manufacturing industries from 1947 to 2018 using the Diebold-Yilmaz Connectedness Index framework, which fully utilizes the information in generalized variance decompositions from vector autoregressions. The results show that the system-wide connectedness of the input-output network Granger-causes the producer price inflation connectedness across industries. The input-output network and the inflation connectedness nexus is stronger during periods of major supply-side shocks, such as the global oil and metal price hikes, and weaker during periods of aggregate demand shocks, such as the Volcker disinflation of 1981-84 and the Great Recession of 2008. These findings are consistent with Acemoglu et al. (2016)'s conjecture that supply shocks are transmitted downstream, whereas demand shocks are transmitted upstream. Finally, preliminary results show that Trump tariffs caused an increase in the system-wide inflation connectedness in the first half of 2018, due to shocks mostly transmitted from tariff-targeted industries, namely, basic metals, fabricated metals and machinery.
{"title":"Producer Price Inflation Connectedness and Input-Output Networks","authors":"Nuriye Melisa Bilgin, K. Yilmaz","doi":"10.2139/ssrn.3244645","DOIUrl":"https://doi.org/10.2139/ssrn.3244645","url":null,"abstract":"We analyze the transmission of producer price in inflation shocks across the U.S. manufacturing industries from 1947 to 2018 using the Diebold-Yilmaz Connectedness Index framework, which fully utilizes the information in generalized variance decompositions from vector autoregressions. The results show that the system-wide connectedness of the input-output network Granger-causes the producer price inflation connectedness across industries. The input-output network and the inflation connectedness nexus is stronger during periods of major supply-side shocks, such as the global oil and metal price hikes, and weaker during periods of aggregate demand shocks, such as the Volcker disinflation of 1981-84 and the Great Recession of 2008. These findings are consistent with Acemoglu et al. (2016)'s conjecture that supply shocks are transmitted downstream, whereas demand shocks are transmitted upstream. Finally, preliminary results show that Trump tariffs caused an increase in the system-wide inflation connectedness in the first half of 2018, due to shocks mostly transmitted from tariff-targeted industries, namely, basic metals, fabricated metals and machinery.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124027422","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 constructs a monthly real-time oil price dataset using backcasting and compares the forecast performance of alternative models of constant and timevarying volatility based on the accuracy of point and density forecasts of real oil prices of both real-time and ex-post revised data. The paper considers Bayesian autoregressive and autoregressive moving average models with respectively, constant volatility and two forms of time-varying volatility: GARCH and stochastic volatility. In addition to the standard time-varying models, more flexible models with volatility in mean and moving average innovations are used to forecast the real price of oil. The results show that timevarying volatility models dominate their counterparts with constant volatility in terms of point forecasting at longer horizons and density forecasting at all horizons. The inclusion of a moving average component provides a substantial improvement in the point and density forecasting performance for both types of time-varying models while stochastic volatility in mean is superfluous for forecasting oil prices.
{"title":"Forecasting the Real Price of Oil Under Alternative Specifications of Constant and Time-Varying Volatility","authors":"Beili Zhu","doi":"10.2139/ssrn.3069990","DOIUrl":"https://doi.org/10.2139/ssrn.3069990","url":null,"abstract":"This paper constructs a monthly real-time oil price dataset using backcasting and compares the forecast performance of alternative models of constant and timevarying volatility based on the accuracy of point and density forecasts of real oil prices of both real-time and ex-post revised data. The paper considers Bayesian autoregressive and autoregressive moving average models with respectively, constant volatility and two forms of time-varying volatility: GARCH and stochastic volatility. In addition to the standard time-varying models, more flexible models with volatility in mean and moving average innovations are used to forecast the real price of oil. The results show that timevarying volatility models dominate their counterparts with constant volatility in terms of point forecasting at longer horizons and density forecasting at all horizons. The inclusion of a moving average component provides a substantial improvement in the point and density forecasting performance for both types of time-varying models while stochastic volatility in mean is superfluous for forecasting oil prices.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116996736","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 expectations play a key role in determining future economic outcomes. The associated uncertainty provides a direct gauge of how well‐anchored the inflation expectations are. We construct a model‐based measure of inflation expectations uncertainty by augmenting a standard unobserved components model of inflation with information from noisy and possibly biased measures of inflation expectations obtained from financial markets. This new model‐based measure of inflation expectations uncertainty is more accurately estimated and can provide valuable information for policymakers. Using U.S. data, we find significant changes in inflation expectations uncertainty during the Great Recession.
{"title":"Measuring Inflation Expectations Uncertainty Using High-Frequency Data","authors":"J. Chan, Yong Song","doi":"10.2139/ssrn.3054252","DOIUrl":"https://doi.org/10.2139/ssrn.3054252","url":null,"abstract":"Inflation expectations play a key role in determining future economic outcomes. The associated uncertainty provides a direct gauge of how well‐anchored the inflation expectations are. We construct a model‐based measure of inflation expectations uncertainty by augmenting a standard unobserved components model of inflation with information from noisy and possibly biased measures of inflation expectations obtained from financial markets. This new model‐based measure of inflation expectations uncertainty is more accurately estimated and can provide valuable information for policymakers. Using U.S. data, we find significant changes in inflation expectations uncertainty during the Great Recession.","PeriodicalId":445951,"journal":{"name":"ERN: Forecasting & Simulation (Prices) (Topic)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126036749","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：