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Unobserved heterogeneity is common in economic data and has nontrivial impacts on modeling, estimation and statistical inference. This dissertation consists of three chapters that explore and illustrate the implications of unobserved heterogeneity for different types of data. The first two chapters focus on panel data, and the third chapter focuses on cross--sectional data. A popular way to control for unobserved heterogeneity in panel data models is to include fixed effects, but fixed effect estimators of dynamic and nonlinear panel models are subject to the incidental parameter problem. This problem has two implications for applied research: (1) point estimators are largely biased, and (2) confidence intervals have incorrect coverages. Chapter 1 proposes a new method for bias reduction based on indirect inference. The method simulates data using the model with estimated individual effects, and finds estimators of the model parameters by equating the fixed effect estimates obtained from observed and simulated data. The asymptotic framework provides consistency, bias correction, and asymptotic normality results. An application to female labor force participation and numerical simulations illustrate the finite--sample performance of the method. Chapter 2 is coauthored with Victor Chernozhukov at MIT, Iván Fernández-Val at BU, and Hiroyuki Kasahara and Paul Schrimpf at The University of British Columbia. It is based on the observation that existing jackknife methods that deal with the incidental parameter problem require stationary variables. However, many applications feature covariates of interests that have trends or structural breaks. This chapter proposes a new jackknife bias correction method that relaxes stationarity. The method is named crossover jackknife because it partitions the panel in two halves, each including half of the time series observations for each cross sectional unit, but where the time periods are crossed over between the two halves of the cross section units. We derive the theoretical properties of this method and illustrate its finite--sample performance via calibrated numerical simulations. Chapter 3 is coauthored with Hiroaki Kaido at BU. In many important discrete choice models, whether the model makes a unique prediction or not depends on its policy-relevant features and can be examined by testing restrictions on underlying structural parameters. Imposing strong assumptions completes the model and allows to predict unique outcomes, but it masks heterogeneity of the data and affects statistical inference. We provide a new test of model incompleteness using a score-based statistic. Our test statistic remains computationally tractable even with a moderate number of nuisance parameters because they only need to be estimated in the restricted complete model. Two empirical applications illustrate the computational feasibility of the method. A Monte Carlo experiment shows the score test outperforms existing tests in terms of local power.

The first essay of this dissertation studies the determinants and effects of firms' participation in a voluntary pollution reduction program (VPR) initiated by government regulators. This research presents empirical evidence in support of the "enforcement theory" for VPRs, which predicts that (1) participation is rewarded by relaxed regulatory scrutiny; (2) the anticipation of this reward spurs firms to participate in the program; and (3) the program rewards regulators with reduced pollution. The results also indicate that firms' VPR participation, and pollutant reductions themselves, were prompted by a firm's likelihood of becoming a boycott target and/or being subject to environmental interest group lobbying for tighter standards.In the second essay, a nonparametric regression estimator which can accommodate two empirically relevant data environments is proposed. The first data environment assumes that at least one of the explanatory variables is discrete. In such an environment, a "cell" approach which estimates a separate regression for each discrete cell, has generally been employed. The second data environment assumes that one needs to estimate a set of regression functions that belong to different individuals. In both environments the proposed estimator attempts to reduce estimation error by incorporating extraneous data from the other individuals or "cells" when estimating the regression function for a given individual or "cell". The simulation results for the proposed estimator demonstrate a strong potential in empirical applications.In the third essay, the nonparametric approach proposed in the second essay is used to estimate the parameters of the short-term interest rate diffusion. The nonparametric estimators of the drift of the short rate proposed by Stanton (1997) and Jiang (1998) can produce spurious nonlinearities due to the persistent dependence and limited sampling period of interest rates. The simulations show that the proposed estimator significantly attenuates the spurious nonlinearities of Stanton's nonparametric estimator. An empirical study of the US term structure of interest rates is presented based on the proposed estimator and two other competing models. The results suggest that the estimation of the short rate diffusion parameters using additional data from yields of different maturities has significant economic implications on the valuation interest rate derivatives.

Projections of electrical energy sales constitutes the foundation for planning in the electric utility industry. Every distributor of electric energy, large or small, wholesaler or retailer, should have sales forecasts available upon which to base its physical and financial planning. Others associated with the industry, such as regulatory agencies and suppliers, also need such forecasts. The purpose of this paper is to develop an econometric model for forecasting annual industrial kilowatt-hour sales for the State of Indiana. The model is designated so that State agencies, suppliers and individual companies can use the model.

Demand for meat in Saudi Arabia increased dramatically during the last two decades which lead the government to promote economic development and increased self-sufficiency in meat commodities. This study represents a dynamic specification of the Almost Ideal Demand System (AIDS) based on recent developments on cointegration techniques and error correction models, based on meat and fish consumption data in Saudi Arabia over the period 1981 to 2000. The results show that there is evidence of long-run relationship between expenditure on meat with commodity prices and total meat expenditure. Also, the results for all budget shares elasticities indicate that the demand of poultry and fish, in both the short and long-rum are price inelastic. While the demand for beef and lamb in the short-run was price inelastic, in the long-run these were found to have unit elasticity. The magnitude of short and long-run cross-price elasticities indicates that lamb is a substitute for all meat items, Beef is a substitute for fish only. Poultry is a substitute for lamb and beef, while fish is a substitute for lamb only. In addition, poultry is a complement for fish, while fish is a complement for beef and poultry, beef is a complement for lamb and poultry. The short and long run expenditure elasticities indicate that beef, lamb and poultry were found to have a short run elasticity of less than one. In the long run beef, lamb and poultry, exhibited similar behaviour with regard to expenditure changes, indicating that these three meat items can be considered as necessities. On the other hand, fish is found in the short run and the long run to behave as a luxury good.

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