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Lag-Augmented Two- and Three-Stage Least Squares Estimators for Integrated Structural Dynamic Models

Cheng Hsiao and Siyan Wang ()

No 06.55, IEPR Working Papers from Institute of Economic Policy Research (IEPR)

Abstract: We consider a lag-augmented two- or three-stage least squares estimator for a structural dynamic model of nonstationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We show that the conventional two- and three-stage least squares estimators are consistent but contain nonstandard distributions without the strict exogeneity assumption, hence the conventional Wald type test statistics may not be chi-square distributed. We propose a lag order augmented two- or three-stage least squares estimator that is consistent and asymptotically normally distributed. Limited Monte Carlo studies are conducted to shed light on the finite sample properties of various estimators.

Keywords: Structural vector autoregressions; Nonstationary time series; Cointegration; Hypothesis testing; Two and Three Stage Least Squares (search for similar items in EconPapers)
JEL-codes: C1 C3 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2006-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Lag-augmented two- and three-stage least squares estimators for integrated structural dynamic models (2007) Downloads
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