A model of cocoa replanting and new planting in Bahia, Brazil, 1966-85

In 1966, 90 percent of the cocoa growing areas in Bahia, Brazil had trees more than 30 years old. By 1985 most of the area had been replanted or supplied with new trees. Throughout most of this period there were high or rising cocoa prices, and zero or negative interest rates. High prices and low interest rates directly encouraged new planting, but their relationship to replanting is more complex. In the short term, higher prices discourage replanting, which involves uprooting and a temporary loss of revenue. But over the long run, higher prices increase expectations of future profits and encourage replanting. Lowering the interest rate beloow its real level provided cocoa growers with a subsidy that encouraged both replanting and new planting.Economic Growth,Economic Theory&Research,Environmental Economics&Policies,Crops&Crop Management Systems,Banks&Banking Reform

Keynote lecture: Estimation of count-data panel models

In this talk, I will cover a number of topics related to the estimation of panel models for count data, with empirical illustrations estimated using Stata. For the theoretical background, I will rely on my book with Colin Cameron, Microeconometrics: Methods and Applications (2005, Cambridge University Press). Some of my illustrations will be based on material in my recent book with Colin Cameron, Microeconometrics Using Stata (2009, Stata Press), but several others will be based on as yet unpublished material. This talk will be operational in orientation and, for specificity, I will rely on examples estimated in Stata. I plan to cover the following topics: nonlinear panel-data modeling for exponential mean models, fixed- and random-effects panel models for the Poisson and negative binomial regression, nonlinear GMM estimation of Poisson panel regression with sample selection or endogenous regressors, dynamic panel Poisson regression with correlated random effects, dynamic panel Poisson regression with linear feedback, finite mixture models for panel Poisson regression

Modeling the Differences in Counted Outcomes using Bivariate Copula Models: with Application to Mismeasured Counts

This paper makes three contributions. First, it uses copula functions to obtain a flexible bivariate parametric model for nonnegative integer-valued data (counts). Second, it recovers the distribution of the difference in the two counts from a specifed bivariate count distribution. Third, the methods are applied to counts that are measured with error. Specifically we model the determinants of the difference between the self-reported number of doctor visits (measured with error) and true number of doctor visits (also available in the data used).

Dynamic Cost-offsets of Prescription Drug Expenditures: Panel Data Analysis Using a Copula-based Hurdle Model

This paper presents a new multivariate copula-based modeling approach for analyzing cost-offsets between drug and nondrug expenditures. Estimates are based on panel data from the Medical Expenditure Panel Survey (MEPS) with quarterly measures of medical expenditures. The approach allows for nonlinear dynamic dependence between drug and nondrug expenditures as well as asymmetric contemporaneous dependence. The specification uses the standard hurdle model with two significant extensions. First, it is adapted to the bivariate case. Second, because the cost-offset hypothesis is inherently dynamic, the bivariate hurdle framework is extended to accommodate dynamic relationships between drug and nondrug spending. The econometric analysis is implemented for six different groups defined by specific health conditions. There is evidence of modest cost-offsets of expenditures on prescribed drugs.

Joint and separate score tests for state dependence and unobserved heterogeneity

The paper compares separate, conditional, and joint score tests of duration dependence and unobserved heterogeneity when the null is the exponential model and the alternative is the heterogeneous Weibull model. The score tests based on the conditional score function include the Neyman C(x) test as a special case. An examination of the non-null distribution of the joint test explains when all score tests have low power in the presence of multiple misspecifications. Monte Carlo experiments show that the conditional score tests are superior to the standard separate tests which confound unobserved heterogeneity and duration dependence