Research Discussion Papers, Bank of Finland
No 18/2009:
Noncausal vector autoregression
Markku Lanne ()
and Pentti Saikkonen ()
Abstract: In this paper, we propose a new noncausal vector
autoregressive (VAR) model for non-Gaussian time series. The assumption of
non-Gaussianity is needed for reasons of identifiability. Assuming that the
error distribution belongs to a fairly general class of elliptical
distributions, we develop an asymptotic theory of maximum likelihood
estimation and statistical inference. We argue that allowing for
noncausality is of importance in empirical economic research, which
currently uses only conventional causal VAR models. Indeed, if noncausality
is incorrectly ignored, the use of a causal VAR model may yield suboptimal
forecasts and misleading economic interpretations. This is emphasized in
the paper by noting that noncausality is closely related to the notion of
nonfundamentalness, under which structural economic shocks cannot be
recovered from an estimated causal VAR model. As detecting
nonfundamentalness is therefore of great importance, we propose a procedure
for discriminating between causality and noncausality that can be seen as a
test of nonfundamentalness. The methods are illustrated with applications
to fiscal foresight and the term structure of interest rates.
Keywords: elliptic distribution; fiscal foresight; maximum likelihood estimation; noncausal; nonfundamentalness; non-Gaussian; term structure of interest rates; (follow links to similar papers)
JEL-Codes: C32; C46; C52; E62; G12; (follow links to similar papers)
63 pages, August 12, 2009
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