On determining the importance of a regressor with small and undersized samples

Peter Jensen and Allan Würtz

Economics Working Papers from Department of Economics and Business Economics, Aarhus University

Abstract: A problem encountered in, for instance, growth empirics is that the number of explanatory variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to determine the importance of a variable of interest. We prove identifying assumptions under which the problem is not ill-posed. Under these assumptions, we derive properties of the most commonly used methods: Extreme bounds analysis, Sala-i-Martin’s method, BACE, generalto- specific, minimum t-statistics, BIC and AIC. We propose a new method and show that it has good finite sample properties.

Keywords: AIC; BACE; BIC; extreme bounds analysis; general-to-specific; identification; ill-posed inverse problem; robustness; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: C12 C51 C52 (search for similar items in EconPapers)
Pages: 39
Date: 2006-07-14
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://repec.econ.au.dk/repec/afn/wp/06/wp06_08.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aah:aarhec:2006-08

Access Statistics for this paper

More papers in Economics Working Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().