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Working Paper
ivcrc: An Instrumental Variables Estimator for the Correlated Random Coefficients Model
We present the ivcrc command, which implements an instrumental variables (IV) estimator for the linear correlated random coefficients (CRC) model. This model is a natural generalization of the standard linear IV model that allows for endogenous, multivalued treatments and unobserved heterogeneity in treatment effects. The proposed estimator uses recent semiparametric identification results that allow for flexible functional forms and permit instruments that may be binary, discrete, or continuous. The command also allows for the estimation of varying coefficients regressions, which are ...
Working Paper
Closed-Form Estimation of Finite-Order ARCH Models: Asymptotic Theory and Finite-Sample Performance
Strong consistency and weak distributional convergence to highly non-Gaussian limits are established for closed-form, two stage least squares (TSLS) estimators for a class of ARCH(p) models. Conditions for these results include (relatively) mild moment existence criteria that are supported empirically by many (high frequency) financial returns. These conditions are not shared by competing closed-form estimators like OLS. Identification of these TSLS estimators depends on asymmetry, either in the model's rescaled errors or in the conditional variance function. Monte Carlo studies reveal TSLS ...
Working Paper
ivcrc: An Instrumental Variables Estimator for the Correlated Random Coefficients Model
We discuss the ivcrc module, which implements an instrumental variables (IV) estimator for the linear correlated random coefficients (CRC) model. The CRC model is a natural generalization of the standard linear IV model that allows for endogenous, multivalued treatments and unobserved heterogeneity in treatment effects. The estimator implemented by ivcrc uses recent semiparametric identification results that allow for flexible functional forms and permit instruments that may be binary, discrete, or continuous. The ivcrc module also allows for the estimation of varying coefficients ...
Working Paper
Testing for Endogeneity: A Moment-Based Bayesian Approach
A standard assumption in the Bayesian estimation of linear regression models is that the regressors are exogenous in the sense that they are uncorrelated with the model error term. In practice, however, this assumption can be invalid. In this paper, under the rubric of the exponentially tilted empirical likelihood, we develop a Bayes factor test for endogeneity that compares a base model that is correctly specified under exogeneity but misspecified under endogeneity against an extended model that is correctly specified in either case. We provide a comprehensive study of the log-marginal ...