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Developments in Macro and Econometrics
Friday, Jan. 5, 2024
2:30 PM - 4:30 PM (CST)
Society for Nonlinear Dynamics and Econometrics
Texas A&M University
Cross-Sectional Dynamics under Network Structure: Theory and Macroeconomic Applications
Many environments in economics feature a cross-section of agents or units linked by a network of bilateral ties. I develop a framework to study dynamics in these cases. It consists of a vector autoregression in which innovations transmit cross-sectionally via bilateral links and which can accommodate general patterns of how network effects of higher order accumulate over time. In a first application, I take the supply chain network of the US economy as given and document how it drives the dynamics of sectoral prices. By estimating the time profile of network effects, the model allows me to go beyond steady state comparisons and study transition dynamics induced by granular shocks. As a result of different positions in the input-output network, sectors differ in both the strength and the timing of their impact on aggregates. In a second application, I discuss how to approximate cross-sectional processes by assuming that dynamics are driven by a network and in turn estimating the latter. The proposed framework offers a sparse, yet flexible and interpretable method for doing so, owing to networks' ability to summarize complex relations among units by relatively few non-zero bilateral links. Modeling industrial production growth across 44 countries, I obtain reductions in out-of-sample mean squared errors of up to 20% relative to a principal components factor model.
The 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
This 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
I 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.
University of Pittsburgh
Texas A&M University
European Central Bank
C1 - Econometric and Statistical Methods and Methodology: General
E3 - Prices, Business Fluctuations, and Cycles