Deckers, Thomas and Hanck, Christoph (2009): Multiple Testing Techniques in Growth Econometrics.

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Abstract
This paper discusses two longstanding questions in growth econometrics which involve multiple hypothesis testing. In cross sectional GDP growth regressions many variables are simultaneously tested for significance. Similarly, when investigating pairwise convergence of output for $n$ countries, $n(n1)/2$ tests are performed. We propose to control the false discovery rate (FDR) so as not to erroneously declare variables significant in these multiple testing situations only because of the large number of tests performed. Doing so, we provide a simple new way to robustly select variables in economic growth models. We find that few other variables beyond the initial GDP level are needed to explain growth. We also show that convergence of per capita output using a time series definition with the necessary condition of no unit root in the log percapita output gap of two economies does not appear to hold
Item Type:  MPRA Paper 

Original Title:  Multiple Testing Techniques in Growth Econometrics 
Language:  English 
Keywords:  Growth Empirics; Multiple Testing; Convergence; Bootstrap 
Subjects:  O  Economic Development, Innovation, Technological Change, and Growth > O4  Economic Growth and Aggregate Productivity > O47  Empirical Studies of Economic Growth ; Aggregate Productivity ; CrossCountry Output Convergence C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General 
Item ID:  17843 
Depositing User:  Christoph Hanck 
Date Deposited:  13. Oct 2009 16:10 
Last Modified:  13. Feb 2013 18:05 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/17843 