Empirical Macro and Time Series

Paper Session

Saturday, Jan. 7, 2017 8:00 AM – 10:00 AM

Hyatt Regency Chicago, New Orleans
Hosted By: Korea-America Economic Association
  • Chair: Jinill Kim, Korea University

Why You Should Never Use the Hodrick-Prescott Filter

James D. Hamilton
,
University of California-San Diego

Abstract

Here's why. (1) The HP filter can induce spurious predictive relations that have no basis in the underlying data‐generating process. (2) A one‐sided version of the filter reduces but does not eliminate spurious predictability and moreover produces a series that does not have the properties sought by advocates of HP filtering. (3) A statistical formalization of the problem typically produces values for tuning parameters vastly at odds with common practice, e.g., a value for λ far below 1600 for quarterly data. (4) A superior alternative exists. Specifically, the k‐step‐ahead residuals from a sample linear projection on the variable's own lags offer a robust approach that achieves all the objectives of users of the HP filter with none of its drawbacks.

Identifying the Sources of Model Misspecification

Atsushi Inoue
,
Vanderbilt University
Chun-Hung Kuw
,
International University of Japan
Barbara Rossi
,
ICREA-Pompeu Fabra University, Barcelona GSE and CREI

Abstract

The Great Recession has challenged the adequacy of existing models to explain key macroeonomic data, and raised the concern that the models might be misspecified. This paper investigates the importance of misspecification in structural models using a novel approach to detect and identify the source of the misspecification, thus guiding researchers in their quest for improving economic models. Our approach formalizes the common practice of adding "shocks" in the model and identifies potential misspecification via forecast error variance decomposition and marginal likelihood analyses. Simulation results based on a small-scale DSGE model demonstrate that the method can correctly identify the source of misspefication. Our empirical results show that state-of-the-art medium-scale New Keynesian DSGE models remain misspecified, pointing to asset and labor markets as the sources of the misspefication.

N-State Endogenous Markov-Switching Models

Shih-Tang Hwu
,
University of Washington
Chang-Jin Kim
,
University of Washington
Jeremy Piger
,
University of Oregon

Abstract

We develop an N-regime Markov-switching regression model in which the latent state variable driving the regime switching is endogenous. The model admits a wide variety of patterns of correlation between the state variable and the regression disturbance term, while still maintaining computational feasibility. We provide an iterative filter that generates objects of interest, including the model likelihood function and estimated regime probabilities. The parameterization of the model also allows for a simple test of the null hypothesis of exogenous switching. Using simulation experiments we demonstrate that the maximum likelihood estimator performs well in finite samples, and that a likelihood ratio test of exogenous switching has good size and power properties. We provide results from two applications of the endogenous switching model: a three state model of U.S business cycle dynamics and a three-state volatility model of U.S. equity returns. In both cases we find statistically signi ficant evidence in favor of endogenous switching.

Monetary Policy Uncertainty and Economic Fluctuations

Drew D. Creal
,
University of Chicago
Jing Cynthia Wu
,
University of Chicago

Abstract

We investigate the relationship between uncertainty about monetary policy and its transmission mechanism, and economic fluctuations. We propose a new term structure model where the second moments of macroeconomic variables and yields can have a first-order effect on their dynamics. The data favors a model with two unspanned volatility factors that capture uncertainty about monetary policy and the term premium. Uncertainty contributes negatively to economic activity. Two dimensions of uncertainty react in opposite directions to a shock to the real economy, and the response of inflation to uncertainty shocks vary across different historical episodes.

Assessing the Macroeconomic Impact of Bank Intermediation Shocks: A Structural Approach

Kanji Chen
,
Emory University
Tao Zha
,
Federal Reserve Bank of Atlanta

Abstract

We take a structural approach to assessing the empirical importance of shocks to the supply of bank-intermediated credit in aff ecting macroeconomic fluctuations. First, we develop a theoretical model to show how credit supply shocks can be transmitted into disruptions in the production economy. Second, we utilize the unique micro banking data to identify and support the model's key mechanism. Third, we find that the output e ffect of credit supply shocks is not only economically and statistically signi ficant but also consistent with the VAR evidence. Our mode estimation indicates that a negative one-standard-deviation shock to credit supply generates a loss of output by one percent.
JEL Classifications
  • C1 - Econometric and Statistical Methods and Methodology: General
  • E0 - General