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Full-Text Articles in Social and Behavioral Sciences
Uniformly Adaptive Estimation For Models With Arma Errors, Douglas Steigerwald
Uniformly Adaptive Estimation For Models With Arma Errors, Douglas Steigerwald
Douglas G. Steigerwald
A semiparametric estimator based on an unknown density is uniformly adaptive if the expected loss of the estimator converges to the asymptotic expected loss of the maximum likelihood estimator based on the true density (MLE), and if convergence does not depend on either the parameter values or the form of the unknown density. Without uniform adaptivity, the asymptotic expected loss of the MLE need not approximate the expected loss of a semiparamteric estimator for any finite sample. I show that a two-step semiparametric estimator is uniformly adaptive for the parameters of nonlinear regression models with autoregressive moving average errors.
Econometric Estimation Of Foresight: Tax Policy And Investment In The U.S., Douglas G. Steigerwald, Charles Stuart
Econometric Estimation Of Foresight: Tax Policy And Investment In The U.S., Douglas G. Steigerwald, Charles Stuart
Douglas G. Steigerwald
We develop a method for measuring the foresight agents have. We first dichotomize an agent's information at current date t into knowledge up to date t+f and expectations after t+f. We then form a residual-based test statistic that allows us to compare prediction errors for econometric models based on different values of f. We illustrate the method, examining investment around tax reforms to measure the foresight firms have about tax policy. In this illustration, current investment appears to reflect currently available information but little foresight other than foresight of enacted policy changes.
Asymptotic Bias For Quasi-Maximum Likelihood Estimators In Models With Conditional Heteroskedasticity, Douglas G. Steigerwald, Whitney Newey
Asymptotic Bias For Quasi-Maximum Likelihood Estimators In Models With Conditional Heteroskedasticity, Douglas G. Steigerwald, Whitney Newey
Douglas G. Steigerwald
Virtually all applications of time-varying conditional variance models use a quasi-maximum likelihood estimator (QMLE). Consistency of a QMLE requires an identification condition that the quasi-log-likelihood have a unique maximum at the true conditional mean and relative scale parameters. We show that the identification condition holds for a non-Gaussian QMLE if the conditional mean is identically zero or if a symmetry condition is satisfied. Without symmetry an additional parameter, for the location of the innovation density, must be added for consistency. We calculate the efficiency loss from adding such a parameter under symmetry, when the parameter is not needed. We also …