Jointness of growth determinants

Arbeitspapier

This paper introduces a new measure of dependence or jointness among explanatory variables. Jointness is based on the joint posterior distribution of variables over the model space, thereby taking model uncertainty into account. By looking beyond marginal measures of variable importance, jointness reveals generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct, but mutually reinforcing effects. Negative jointness implies that explanatory variables are substitutes and capture similar underlying effects. In a cross-country dataset we show that jointness among 67 determinants of growth is important, affecting inference and informing economic policy.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CESifo Working Paper ; No. 1978

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Model Evaluation, Validation, and Selection ; Development Planning and Policy: General ; Economywide Country Studies: General
• Subject: Regression ; Bayes-Statistik ; Schätztheorie ; Wirtschaftswachstum ; Schätzung ; Theorie ; Welt

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Doppelhofer, Gernot ; Weeks, Melvyn
• Event: Veröffentlichung
• (who): Center for Economic Studies and ifo Institute (CESifo)
• (where): Munich
• (when): 2007

Further information

• Handle: http://hdl.handle.net/10419/26023
• Last update: 10.03.2025, 11:44 AM CET