Jointness of Growth Determinants

Model uncertainty arises from uncertainty about correct economic theories, data issues and empirical specification problems. This paper investigates mutual dependence or jointness among variables in explaining the dependent variable. Jointness departs from univariate measures of variable importance, while addressing model uncertainty and allowing for generally unknown forms of dependence. Positive jointness implies that regressors are complements, representing distinct, but interacting economic factors. Negative jointness implies that explanatory variables are substitutes and act as proxies for a similar underlying mechanism. In a cross-country dataset, we show that jointness among 67 determinants of growth is important, ffecting inference and economic policy

Robust Growth Determinants

This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsimonious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model averaging to growth determinants, the paper finds that eight out of eighteen variables found to be significantly related to economic growth by Sala-i-Martin et al. (2004) are sensitive to deviations from benchmark model averaging. For example, the GDP shares of mining or government consumption, are no longer robust or economically significant once deviations from the normal benchmark assumptions are allowed. The paper identifies outlying observations - most notably Botswana - in explaining economic growth in a cross-section of countries.determinants of economic growth, robust model averaging, heteroscedasticity, outliers, mixture models

Jointness of Growth Determinants

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.model uncertainty, dependence among regressors, jointness, determinants of economic growth

Robust Growth Determinants.

This paper investigates the robustness of determinants of economic growth in the presence of model uncertainty, parameter heterogeneity and outliers. The robust model averaging approach introduced in the paper uses a flexible and parsi- monious mixture modeling that allows for fat-tailed errors compared to the normal benchmark case. Applying robust model averaging to growth determinants, the paper finds that eight out of eighteen variables found to be significantly related to economic growth by Sala-i-Martin et al. (2004) are sensitive to deviations from benchmark model averaging. For example, the GDP shares of mining or government consumption, are no longer robust or economically significant once deviations from the normal benchmark assumptions are allowed. The paper identifies outlying observations { most notably Botswana { in explaining economic growth in a cross-section of countries.Determinants of Economic Growth; Robust Model Averaging; Heteroscedasticity; Outliers; Mixture models.

Non-Nested Models and the Likelihood Ratio Statistic: A Comparison of Simulation and Bootstrap Based Tests

We consider an alternative use of simulation in the context of using the Likelihood-Ratio statistic to test non-nested models. To date simulation has been used to estimate the Kullback-Leibler measure of closeness between two densities, which in turn 'mean adjusts' the Likelihood-Ratio statistic. Given that this adjustment is still based upon asymptotic arguments, an alternative procedure is to utilise bootstrap procedures to construct the empirical density. To our knowledge this study represents the first comparison of the properties of bootstrap and simulation-based tests applied to non-nested tests. More specifically, the design of experiments allows us to comment on the relative performance of these two testing frameworks across models with varying degrees of nonlinearity. In this respect although the primary focus of the paper is upon the relative evaluation of simulation and bootstrap-based nonnested procedures in testing across a class of nonlinear threshold models, the inclusion of a similar analysis of the more standard linear/log-linear models provides a point of comparison.Non-nested tests, Simulation-based inference, Bootstrap tests, Nonlinear threshold models

Estimation of Discrete Choice Models Using DCM for Ox

DCM (Discrete Choice Models) is a package for estimating a class of discrete choice models. Written in Ox, DCM is a class that implements a wide range of discrete choice models including standard binary response models, with notable extensions including conditional mixed logit, mixed probit, multinomial probit, and random coefficient ordered choice models. The current version can handle both cross-section and static panel data. DCM represents an important development for the discrete choice computing environment in making available a broad range of models which are now widely used by academics and practitioners. Developed as a derived class of Modelbase, users may access the functions within DCM by either writing Ox programs which create and use an object of the DCM class, or use the program in an interactive fashion via OxPack in GiveWin. We demonstrate the capabilities of DCM by using a number of applications from the discrete choice literature

Social interactions, ethnicity, religion, and fertility in Kenya

AbstractReproductive externalities are important for fertility behavior in Kenya. We identify from anthropology structural forms of social interaction operating across individuals belonging to different ethnic and religious groups on the number of children ever born. We use the 1998 Demographic and Health Survey, combined with primary meteorological data on Kenya, and GMM methods, to show that social interaction effects by ethnicity are important over and above an individual's characteristics such as their religion to explain variations in fertility. Our findings have implications for policy debates in Kenya and in other developing countries about ethnic, religious, and other differences in fertility behavior.St Catharine's College, Cambridg