A Class of Time-Varying Vector Moving Average Models: Nonparametric Kernel Estimation and Application

TL;DR

This paper introduces a broad class of multivariate time-varying models with a unified estimation approach, enabling better analysis of dynamic economic relationships, exemplified by studying monetary policy transmission in the U.S.

Contribution

It proposes a general framework for time-varying vector moving average models with a unified estimation method and asymptotic theory, extending existing models like VAR and VARMA.

Findings

Monetary policy shocks had less influence on inflation before and during the Great Moderation.

Inflation has become more anchored in recent years.

The long-run inflation level is below but close to the 2% target after the Great Moderation.

Abstract

Multivariate dynamic time series models are widely encountered in practical studies, e.g., modelling policy transmission mechanism and measuring connectedness between economic agents. To better capture the dynamics, this paper proposes a wide class of multivariate dynamic models with time-varying coefficients, which have a general time-varying vector moving average (VMA) representation, and nest, for instance, time-varying vector autoregression (VAR), time-varying vector autoregression moving-average (VARMA), and so forth as special cases. The paper then develops a unified estimation method for the unknown quantities before an asymptotic theory for the proposed estimators is established. In the empirical study, we investigate the transmission mechanism of monetary policy using U.S. data, and uncover a fall in the volatilities of exogenous shocks. In addition, we find that (i) monetary policy shocks have less influence on inflation before and during the so-called Great Moderation, (ii) inflation is more anchored recently, and (iii) the long-run level of inflation is below, but quite close to the Federal Reserve's target of two percent after the beginning of the Great Moderation period.