University of California, Berkeley
Grant: $365,324 - National Science Foundation - Jul. 22, 2009
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Award Description: That the availability of panel data, or multiple observations of eh same sampling unit (e.g., individual, firm, state etc.) over time, can help to control for the presence of unobserved heterogeneity is both intuitive and plausible. The inclusion of unit-specific intercepts in linear regression models is among the most widespread methods of controlling for omitted variables in empirical work. The appropriateness of this modeling strategy requires that any time-invariant correlated heterogeneity enter the outcome equation additively. Unfortunately, additivity, while statistically convenient, is difficult to motivate economically. Economic models of optimization suggest than the input choice of an agent should covary with its marginal return. Furthermore, non-additive forms of unobserved heterogeneity appear to be empirically relevant (e.g., Browning and Carro, 2007). Unfortunately, few identification and estimation results for panel data models with such heterogeneity are available. The increasing availability of panel data, the presumption that such data allow for a weakening of restrictions required in the cross-section case, and a growing appreciation by empirical researchers of the importance of nonseparable heterogeneity, suggests that a comprehensive analysis of the identifying power of panel data in a semiparametric context would be valuable. This project studies the use of panel data for identifying and estimating what is perhaps the simplest statistical model admitting nonseparable heterogeneity: the static correlated random coefficients (CRC) model. In this model the outcome for each individual varies linearly with a regressor or input. The coefficients characterizing this linear response vary across individuals and over time. In the context of such a model the proposed research characterizes (features of) the effect on an exogenous change in the input on the probability distribution of the outcome. This type of knowledge is important for predicting the effects of counterfactual policies. The proposed approach is a fixed effects one, that is the joint distribution of the regressors and any time-invariant unobserved heterogeneity (i.e., the individual-specific effects) is left unmodeled. The potential intellectual merits of the proposed activity include increasing our understanding of fixed effect panel data models for continuously-valued outcomes with nonseparable heterogeneity. Panel data are widely-used in practice, yet the menu of methods available to empirical researchers studying continuously-valued outcomes is still heavily organized around the linear model with constant coefficients surveyed by Chamberlain (1984) twenty-five years ago. The proposed projects represent one approach to extending fixed effect panel data methods to models with non-separable heterogeneity. While the main goal is to provide usable identification and estimation results for CRC panel data models, the work also contributes to the theoretical literature on semiparametric estimation. Panel data methods are employed in virtually all fields of empirical economics and the other social sciences. They are essential to the implementation of several leading approaches to policy evaluation and production function estimation. A virtue of the CRC model is its simplicity and ease of interpretability. For this reason the broader impacts resulting from the proposed activities include the real possibility of widespread adoption of the methods developed by empirical researchers in economics and the other social sciences. Publicly available computer software and an integrative survey paper oriented toward practitioners will facilitate such adoption. The proposed methods will be used to study the elasticity of calorie demand with respect to total household resources. This elasticity is an important parameter for food policy analysis and plays a prominent role in theoretical models of nutritional poverty traps.
Project Description: Work related to average partial effects in panel data models is currently under review and has been presented in various scholarly forums (most recently at the Princeton econometrics workshop). Work relating to quantile panel data models is ongoing with initial results presented recently at the All-UC Econometrics conference in Riverside.
Jobs Summary: The graduate student researcher has helped with some difficult computer work. This position is retained. (Total jobs reported: 0)
Project Status: Less Than 50% Completed
This award's data was last updated on Jul. 22, 2009. Help expand these official descriptions using the wiki below.
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