On determining the importance of a regressor with small and undersized samples
Peter Jensen () and
Economics Working Papers from Department of Economics and Business Economics, Aarhus University
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)
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 (4) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
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 ().