ESTIMATION OF THE VECTOR MOVING AVERAGE MODEL BY VECTOR AUTOREGRESSION

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

Analysis of causal effects between continuous-valued variables typically uses either autoregressive models or structural equation models with instantaneous effects. Estimation of Gaussian, linear structural equation models poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the non-Gaussian instantaneous model wi...

Lecture 6 : Vector Autoregression ∗

A Bayesian Poisson Vector Autoregression Model

Multivariate count models are rare in political science, despite the presence of many count time series. This article develops a new Bayesian Poisson vector autoregression (BaP-VAR) model that can characterize endogenous dynamic counts with no restrictions on the contemporaneous correlations. Impulse responses, decomposition of the forecast errors, and dynamic multiplier methods for the effects...

Testing for Fundamental Vector Moving Average Representations

We propose a test for invertibility or fundamentalness of structural vector autoregressive moving average models generated by non-Gaussian independent shocks. We prove that in these models the Wold innovations are serially dependent if and only if the structural shocks are non-fundamental. This simple but powerful characterization suggests an empirical strategy to assess invertibility. We propo...

A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models∗

Recently, there has been a renewed interest in modeling economic time series by vector autoregressive moving-average models. However, this class of models has been unpopular in practice because of estimation problems and the complexity of the identification stage. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. In this paper, sev...