Learning, capital-embodied technology and aggregate fluctuations

Business cycles in the U.S. and G-7 economies are asymmetric: recoveries and expansions tend to be long and gradual and busts tend to be short and sharp. Moreover, this type of asymmetry appears more pronounced in the last two cyclical episodes in the G-7. A large body of work views the last two cyclical U.S. episodes, namely, the``new economy" boom in the late 1990s, and the 2000s housing boom-bust as episodes where over-optimistic beliefs have played a significant role. These episodes have revived interest in expectations driven business cycles models. However, previous work in this area has not addressed the important asymmetry feature of business cycles. This paper takes a step towards addressing this limitation of expectations driven business cycle models. We propose a generalization of the Greenwood et al. (1988) model with vintage capital and learning about capital embodied productivity and show it can deliver fluctuations that are asymmetric as in the U.S. data. Learning, calibrated to match the procyclical forecast precision from the Survey of Professional Forecasters, is crucial for the model's ability to generate asymmetries. Forecast errors generated by the model are shown to: (a) amplify fluctuations, and (b) trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession.News shocks, expectations, growth asymmetry, Bayesian learning, business cycles

News and financial intermediation in aggregate fluctuations

An important disconnect in the news view of fluctuations is the lack of consistent evidence suggestive of significant macroeconomic effects of news shocks. Findings from estimated DSGE models that, in theory, allow news shocks to matter quantitatively, suggest they do not. This disconnect can be resolved once we augment a DSGE model with a financial channel that provides amplification to news shocks. Our results suggest news shocks to the future growth prospects of the economy to be significant drivers of U.S. fluctuations, explaining as much as 50% and 37% of the variance in hours worked and output respectively, in cyclical frequencies

Learning, capital-embodied technology and aggregate fluctuations

Business cycles in the U.S. and G-7 economies are asymmetric: recoveries and expansions tend to be long and gradual and busts tend to be short and sharp. Moreover, this type of asymmetry appears more pronounced in the last two cyclical episodes in the G-7. A large body of work views the last two cyclical U.S. episodes, namely, the``new economy" boom in the late 1990s, and the 2000s housing boom-bust as episodes where over-optimistic beliefs have played a significant role. These episodes have revived interest in expectations driven business cycles models. However, previous work in this area has not addressed the important asymmetry feature of business cycles. This paper takes a step towards addressing this limitation of expectations driven business cycle models. We propose a generalization of the Greenwood et al. (1988) model with vintage capital and learning about capital embodied productivity and show it can deliver fluctuations that are asymmetric as in the U.S. data. Learning, calibrated to match the procyclical forecast precision from the Survey of Professional Forecasters, is crucial for the model's ability to generate asymmetries. Forecast errors generated by the model are shown to: (a) amplify fluctuations, and (b) trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession

Learning, capital-embodied technology and aggregate fluctuations

Business cycles in the U.S. and G-7 economies are asymmetric: recoveries and expansions tend to be long and gradual and busts tend to be short and sharp. Moreover, this type of asymmetry appears more pronounced in the last two cyclical episodes in the G-7. A large body of work views the last two cyclical U.S. episodes, namely, the``new economy" boom in the late 1990s, and the 2000s housing boom-bust as episodes where over-optimistic beliefs have played a significant role. These episodes have revived interest in expectations driven business cycles models. However, previous work in this area has not addressed the important asymmetry feature of business cycles. This paper takes a step towards addressing this limitation of expectations driven business cycle models. We propose a generalization of the Greenwood et al. (1988) model with vintage capital and learning about capital embodied productivity and show it can deliver fluctuations that are asymmetric as in the U.S. data. Learning, calibrated to match the procyclical forecast precision from the Survey of Professional Forecasters, is crucial for the model's ability to generate asymmetries. Forecast errors generated by the model are shown to: (a) amplify fluctuations, and (b) trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession

Learning, capital-embodied technology and aggregate fluctuations

Recent cyclical episodes in the U.S. and G-7 economies are asymmetric: recoveries and expansions tend to be long and gradual and busts tend to be short and sharp. A large body of work views the two recent cyclical U.S. episodes, namely, the “new economy” boom in the late 1990s, and the 2000s housing boom-bust as episodes where over-optimistic beliefs have played a significant role. These episodes have revived interest in expectations driven business cycles models. However, previous work in this area has not addressed the important asymmetry feature of business cycles. This paper takes a step towards addressing this limitation of expectations driven business cycle models. We propose a generalization of the Greenwood et al. (1988) model with vintage capital and learning about capital embodied productivity and show it can deliver fluctuations that are asymmetric as in the U.S. data. Learning, calibrated to match the procyclical forecast precision from the Survey of Professional Forecasters, is crucial for the modelʼs ability to generate asymmetries. Forecast errors generated by the model are shown to trigger recessions that mimic in magnitude, duration and depth the typical post WW II U.S. recession

Replication data for: "News and Financial Intermediation in Aggregate Fluctuations"

Gortz, Christoph, and Tsoukalas, John D., (2017) "News and Financial Intermediation in Aggregate Fluctuations." Review of Economics and Statistics 99:3, 514-530