Multiple Testing in Growth Econometrics

Konferenzbeitrag

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 in panel data sets of n countries, n(n-1)/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 in panels of per capita output using a time series definition with the necessary condition of no unit root in the log per-capita output gap of two economies does not appear to hold.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2010: Ökonomie der Familie - Session: Analysing Macroeconomic Panel Data Sets ; No. B2-V3

Classification and Topics

• Classification: Wirtschaft ; Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence ; Hypothesis Testing: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
• Subject: Growth Empirics ; Panel Data ; Multiple Testing ; Convergence ; Bootstrap

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Deckers, Thomas ; Hanck, Christoph
• Event: Veröffentlichung
• (who): Verein für Socialpolitik
• (where): Frankfurt a. M.
• (when): 2010

Further information

• Handle: http://hdl.handle.net/10419/37534
• Last update: 10.03.2025, 11:41 AM CET