A Conditional Approach to Panel Data Models with Common Shocks

Forchini, G; Peng, B

A Conditional Approach to Panel Data Models with Common Shocks

Forchini, G Peng, B

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Econometrics, 2016, 4 (1), pp. 1 - 12
Issue Date:: 2016-01-12

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Field	Value	Language
dc.contributor.author	Forchini, G	en_US
dc.contributor.author	Peng, B https://orcid.org/0000-0003-4231-4713	en_US
dc.date.issued	2016-01-12	en_US
dc.identifier.citation	Econometrics, 2016, 4 (1), pp. 1 - 12	en_US
dc.identifier.issn	2225-1146	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116848
dc.description.abstract	This paper studies the effects of common shocks on the OLS estimators of the slopes’ parameters in linear panel data models. The shocks are assumed to affect both the errors and some of the explanatory variables. In contrast to existing approaches, which rely on using results on martingale difference sequences, our method relies on conditional strong laws of large numbers and conditional central limit theorems for conditionally-heterogeneous random variables.	en_US
dc.publisher	MDPI AG	en_US
dc.relation.ispartof	Econometrics	en_US
dc.relation.isbasedon	10.3390/econometrics4010004	en_US
dc.title	A Conditional Approach to Panel Data Models with Common Shocks	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	4	en_US
utslib.for	140304 Panel Data Analysis	en_US
utslib.for	140302 Econometric and Statistical Methods	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

This paper studies the effects of common shocks on the OLS estimators of the slopes’ parameters in linear panel data models. The shocks are assumed to affect both the errors and some of the explanatory variables. In contrast to existing approaches, which rely on using results on martingale difference sequences, our method relies on conditional strong laws of large numbers and conditional central limit theorems for conditionally-heterogeneous random variables.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/116842