Monetary Policy Analysis Based on Lasso-Assisted Vector Autoregression (Lavar)

Abstract

Measuring the quantitative effects of monetary policy on the economy has been playing a central role in promoting economic growth and stability. However, in the presence of numerous macroeconomic variables, traditional vector autoregression (VAR) could only accommodate a few data series, and thus may ignore the information set which is actually observed by central banks and financial market participants. In this paper, we propose a novel VAR model with the aid of new developments in high-dimensional statistical inference. Our approach could handle hundreds of observed data series simultaneously, and increase the prediction accuracy as well as the robustness of monetary policy analysis in a data-rich environment. It has been shown that our model outperforms factor-augmented VAR in terms of in-sample-fit and out-of-sample forecasts. Moreover, impulse responses are observed for all macroeconomic variables, where “price puzzle”, a commonly observed empirical anomaly, is resolved.