Jointness of Growth Determinants

Social Science Research Network, 2009

133 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/7cfc5b11d451a30222dd854d96d72174282ebbc5

Citations in the corpus

Weighted‐Average Least Squares (WALS): A Survey (2016, unknown): “For example, WALS cannot handle jointness (Ley and Steel, 2007; Doppelhofer and Weeks, 2009).”
On the Effect of Prior Assumptions in Bayesian Model Averaging with Applications to Growth Regression (2007, unknown): “The Data Set of SDM and DW SDM and Doppelhofer and Weeks (2008) use a larger data set, and model annual GDP growth per capita between 1960 and 1996 for n = 88 countries as a function of k = 67 potential drivers.”
The Determinants of Economic Growth in European Regions (2014, unknown): “Other studies study the importance of parameter heterogeneity in the uncertain growth process (see Crespo Cuaresma and Doppelhofer (2007), or Doppelhofer and Weeks (2009)).”
Model Averaging and Its Use in Economics (2017, unknown): “Man (2017) compares different jointness measures using data from a variety of disciplines and finds that results differ substantially between the measures of Doppelhofer and Weeks (2009) on the one hand and Ley and Steel (2007) on the other hand.”
Model Averaging in Economics: An Overview (2015, unknown): “Ley and Steel (2007) and Doppelhofer and Weeks (2009) define ex-post measures of dependence among explanatory variables that appear in linear regression models.”