Are any growth theories linear? Why we should care about what the evidence tells us

Recent research on macroeconomic growth has been focused on resolving several key issues, two of which, specification uncertainty of the growth process and variable uncertainty, have received much attention in the recent literature. The standard procedure has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine growth across countries. However, a more appropriate approach would be to recognize that a misspecified model may lead one to conclude that a variable is relevant when in fact it is not. This paper takes a step in this direction by considering conditional variable uncertainty with full blown specification uncertainty. We use recently developed nonparametric model selection techniques to deal with nonlinearities and competing growth theories. We show how one can interpret our results and use them to motivate more intriguing specifications within the traditional studies that use Bayesian Model Averaging or other model selection criteria. We find that the inclusion of nonlinearities is necessary for determining the empirically relevant variables that dictate growth and that nonlinearities are especially important in uncovering key mechanism of the growth process.Growth Nonlinearities, Irrelevant Variables, Least Squares Cross Validation, Bayesian Model Averaging, Parameter Heterogeneity

Semiparametric Deconvolution with Unknown Error Variance

Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about the nature of the measurement error, more specifically, that the distribution is entirely known. We relax this assumption in the context of a regression error component model and develop an estimator for the unknown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method

Are any growth theories linear? Why we should care about what the evidence tells us

Recent research on macroeconomic growth has been focused on resolving several key issues, two of which, specification uncertainty of the growth process and variable uncertainty, have received much attention in the recent literature. The standard procedure has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine growth across countries. However, a more appropriate approach would be to recognize that a misspecified model may lead one to conclude that a variable is relevant when in fact it is not. This paper takes a step in this direction by considering conditional variable uncertainty with full blown specification uncertainty. We use recently developed nonparametric model selection techniques to deal with nonlinearities and competing growth theories. We show how one can interpret our results and use them to motivate more intriguing specifications within the traditional studies that use Bayesian Model Averaging or other model selection criteria. We find that the inclusion of nonlinearities is necessary for determining the empirically relevant variables that dictate growth and that nonlinearities are especially important in uncovering key mechanism of the growth process

Are any growth theories linear? Why we should care about what the evidence tells us

Recent research on macroeconomic growth has been focused on resolving several key issues, two of which, specification uncertainty of the growth process and variable uncertainty, have received much attention in the recent literature. The standard procedure has been to assume a linear growth process and then to proceed with investigating the relevant variables that determine growth across countries. However, a more appropriate approach would be to recognize that a misspecified model may lead one to conclude that a variable is relevant when in fact it is not. This paper takes a step in this direction by considering conditional variable uncertainty with full blown specification uncertainty. We use recently developed nonparametric model selection techniques to deal with nonlinearities and competing growth theories. We show how one can interpret our results and use them to motivate more intriguing specifications within the traditional studies that use Bayesian Model Averaging or other model selection criteria. We find that the inclusion of nonlinearities is necessary for determining the empirically relevant variables that dictate growth and that nonlinearities are especially important in uncovering key mechanism of the growth process

Who benefits from financial development? New methods, new evidence

This paper takes a fresh look at the impact of financial development on economic growth by using recently developed kernel methods that allow for heterogeneity in partial effects, nonlinearities and endogenous regressors. Our results suggest that while the positive impact of financial development on growth has increased over time, it is also highly nonlinear with more developed nations benefiting while low-income countries do not benefit at all. We also conduct a novel policy analysis that confirms these statistical findings. In sum, this set of results contributes to the ongoing policy debate as to whether low-income nations should scale up financial reforms. •The (positive) impact of financial development on growth has increased over time.•Low income countries do not benefit (in terms of growth rates) from financial development.•The growth process is highly nonlinear.•Our policy analysis supports our statistical results.•Our econometric results provide a robust perspective on the relationship between financial development and growth

Examining Ways to Improve Weight Control Programs’ Population Reach and Representativeness: A Discrete Choice Experiment of Financial Incentives

BACKGROUND: Both theoretical and empirical evidence supports the potential of modest financial incentives to increase the reach of evidence-based weight control programs. However, few studies exist that examine the best incentive design for achieving the highest reach and representativeness at the lowest cost and whether or not incentive designs may be valued differentially by subgroups that experience obesity-related health disparities. METHODS: A discrete choice experiment was conducted (n = 1232 participants; over 90% of them were overweight/obese) to collect stated preference towards different financial incentive attributes, including reward amount, program location, reward contingency, and payment form and frequency. Mixed logit and conditional logit models were used to determine overall and subgroup preference ranking of attributes. Using the National Health and Nutrition Examination Survey data sample weights and the estimated models, we predicted US nationally representative participation rates by subgroups and examined the effect of offering more than one incentive design. External validity was checked by using a completed cluster randomized control trial. RESULTS: There were significant subgroup differences in preference toward incentive attributes. There was also a sizable negative response to larger incentive amounts among African Americans, suggesting that higher amounts would reduce participation from this population. We also find that offering participants a menu of incentive designs to choose from would increase reach more than offering higher reward amounts. CONCLUSIONS: We confirmed the existence of preference heterogeneity and the importance of subgroup-targeted incentive designs in any evidence-based weight control program to maximize population reach and reduce health disparities