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Modified two-stage least-squares estimators for the estimation of a structural vector autoregressive integrated process [An article from: Journal of Econometrics]
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Book Details
Author(s)
C. Hsiao, S. Wang
Publisher
Elsevier
ISBN / ASIN
B000P6XMI6
ISBN-13
978B000P6XMI6
Marketplace
France 🇫🇷
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Description
This digital document is a journal article from Journal of Econometrics, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
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.
Description:
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.
