Developments in Macro and Econometrics
Friday, Jan. 5, 2024 2:30 PM - 4:30 PM (CST)
- Chair: Tatevik Sekhposyan, Texas A&M University
AbstractThe onset of the COVID-19 and the great lockdown caused macroeconomic variables to display complex patterns that hardly follow any historical behavior. In the context of Bayesian VARs, an off-the-shelf exercise demonstrates how a very low number of extreme pandemic observations distorts the estimated persistence of the variables, affecting forecasts and giving a myopic view of the economic effects after a structural shock. I propose an easy and straightforward solution to deal with these extreme episodes, as an extension of the Minnesota Prior by allowing for time dummies. The method is flexible enough to let the econometrician optimally define the level of shrinkage, or arbitrarily choose how much signal to take from these extreme observations, nesting the boundary cases of an uninformative prior that soaks all the variance and a traditional Minnesota Prior. The Pandemic Priors succeed in recovering historical relationships and the proper identification and propagation of structural shocks.
Jointly Estimating Macroeconomic News and Surprise Shocks
AbstractThis paper clarifies the conditions under which the state-of-the-art approach to identifying TFP news shocks in Kurmann and Sims (2021, KS) identifies not only news shocks but also surprise shocks. We examine the ability of the KS procedure to recover responses to these shocks from data generated by a conventional New Keynesian DSGE model. Our analysis shows that the KS response estimator tends to be strongly biased even in the absence of measurement error. This bias worsens in realistically small samples, and the estimator becomes highly variable. Incorporating a direct measure of TFP news into the model and adapting the identification strategy accordingly removes this asymptotic bias and greatly reduces the RMSE when TFP news are correctly measured. However, the high variability of this alternative estimator in small samples suggests caution in interpreting empirical estimates. We examine to what extent empirical estimates of the responses to news and surprise shocks from a range of VAR models based on alternative measures of TFP news are economically plausible.
The Sentiment Channel of Monetary Policy
AbstractI study the role of sentiments in the transmission of monetary policy to different measures of economic activity. I present a simple theoretical model of diagnostic expectations that motivates my empirical analysis of a sentiment channel of monetary policy. In the theoretical model, I show that belief distortions interact with monetary policy surprises to generate a sentiment channel of transmission that co-exists with the usually studied direct effects of monetary policy. I empirically test the existence and strength of this interaction effect between sentiments and monetary policy surprises and document that effects attributed to the interaction between sentiment and monetary policy surprises are quantitatively important and operate over and above the usual channels examined in earlier studies. I thus find that the direct effects of monetary policy can be amplified or diluted by the state of sentiments in the economy and provide an explanation for why the effectiveness of monetary policy varies over the business cycle.
- C1 - Econometric and Statistical Methods and Methodology: General
- E3 - Prices, Business Fluctuations, and Cycles