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Modified Two Stage Least Squares Estimators for the Estimation of a Structural Vector Autoregressive Integrated Process

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Author Info
Cheng Hsiao
Siyan Wang

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Abstract

We consider the estimation of a structural vector autoregressive model of nonstationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We propose two modified two stage least squares estimators that are consistent and have limiting distributions that are either normal or mixed normal. Limited Monte Carlo studies are also conducted to evaluate their finite sample properties.

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File URL: http://www.usc.edu/dept/LAS/economics/IEPR/Working%20Papers/IEPR_05.23_%5BHsiao,Wang%5D.pdf
File Format: application/pdf
File Function: First version, 2005
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Publisher Info
Paper provided by Institute of Economic Policy Research (IEPR) in its series IEPR Working Papers with number 05.23.

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Length: 36 pages
Date of creation: May 2005
Date of revision:
Handle: RePEc:scp:wpaper:05-23

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Web page: http://www.usc.edu/dept/LAS/economics/IEPR/
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Related research
Keywords: Structural vector autoregression; Unit root; Cointegration; Asymptotic properties; Hypothesis testing;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation

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  1. Cheng Hsiao & Siyan Wang, 2006. "Lag-Augmented Two- and Three-Stage Least Squares Estimators for Integrated Structural Dynamic Models," IEPR Working Papers 06.55, Institute of Economic Policy Research (IEPR). [Downloadable!]
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