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Noncausal Bayesian Vector Autoregression

Markku Lanne and Jani Luoto

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution as a by-product. We apply the methods to postwar quarterly U.S. inflation and GDP growth series. The noncausal VAR model turns out to be superior in terms of both in-sample fit and out-of-sample forecasting performance over its conventional causal counterpart. In addition, we find GDP growth to have predictive power for the future distribution of inflation over and above the own history of inflation, but not vice versa. This may be interpreted as evidence against the new Keynesian model that implies Granger causality from inflation to GDP growth, provided GDP growth is a reasonable proxy of the marginal cost.

Keywords: Noncausal time series; non-Gaussian time series; Bayesian analysis; New Keynesian model (search for similar items in EconPapers)
JEL-codes: C11 C32 E31 (search for similar items in EconPapers)
Pages: 33
Date: 2014-03-24
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mac
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