Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 13 of 13
Full-Text Articles in Statistics and Probability
Accurately Sized Test Statistics With Misspecified Conditional Homoskedasticity, Douglas Steigerwald, Jack Erb
Accurately Sized Test Statistics With Misspecified Conditional Homoskedasticity, Douglas Steigerwald, Jack Erb
Douglas G. Steigerwald
We study the finite-sample performance of test statistics in linear regression models where the error dependence is of unknown form. With an unknown dependence structure there is traditionally a trade-off between the maximum lag over which the correlation is estimated (the bandwidth) and the amount of heterogeneity in the process. When allowing for heterogeneity, through conditional heteroskedasticity, the correlation at far lags is generally omitted and the resultant inflation of the empirical size of test statistics has long been recognized. To allow for correlation at far lags we study test statistics constructed under the possibly misspecified assumption of conditional homoskedasticity. …
Adaptive Estimation, Douglas G. Steigerwald
Adaptive Estimation, Douglas G. Steigerwald
Douglas G. Steigerwald
No abstract provided.
Consumption Function, Douglas G. Steigerwald
Consumption Function, Douglas G. Steigerwald
Douglas G. Steigerwald
No abstract provided.
Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton
Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton
Douglas G. Steigerwald
Specification tests for conditional heteroskedasticity that are derived under the assumption that the density of the innovation is Gaussian may not be powerful in light of the recent empirical results that the density is not Gaussian. We obtain specification tests for conditional heteroskedasticity under the assumption that the innovation density is a member of a general family of densities. Our test statistics maximize asymptotic local power and weighted average power criteria for the general family of densities. We establish both first-order and second-order theory for our procedures. Simulations indicate that asymptotic power gains are achievable in finite samples.
Consumption Adjustment Under Time-Varying Income Uncertainty, Douglas Steigerwald, Joon-Ho Hahm
Consumption Adjustment Under Time-Varying Income Uncertainty, Douglas Steigerwald, Joon-Ho Hahm
Douglas G. Steigerwald
We study the effect of income uncertainty on consumption in a model that includes precautionary saving. In contrast to previous studies, we focus on time-series variation in income uncertainty. Our time-series measure of income uncertainty is constructed from a panel of forecasts. We find evidence of precautionary saving in that increases in income uncertainty are related to increases in aggregate rates of saving. We also find evidence that anticipated income growth rates have less explanatory power for consumption growth rates after conditioning on income uncertainty. The evidence indicates the presence of forward-looking consumers who gradually adjust precautionary savings in response …
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 …
Modeling Volatility Dynamics, Douglas Steigerwald
Modeling Volatility Dynamics, Douglas Steigerwald
Douglas G. Steigerwald
No abstract provided.
Adaptive Estimation In Timeseries Regression Models, Douglas Steigerwald
Adaptive Estimation In Timeseries Regression Models, Douglas Steigerwald
Douglas G. Steigerwald
I develop adaptive estimators for linear regression with serially correlated errors. The efficiency results hold even when the serial correlation structure is unknown. Simulations indicate that efficiency gains can be substantial with samples of only 50 observations. We apply the method to a study of forward exchange rates.
On The Finite Sample Behavior Of Adaptive Estimators, Douglas Steigerwald
On The Finite Sample Behavior Of Adaptive Estimators, Douglas Steigerwald
Douglas G. Steigerwald
With only 50 observations, the adaptive estimator produces confidence intervals that are 20 to 50 percent shorter than those produced by GLS procedures. The key feature is that the underlying error density is symmetric. Under asymmetry the interval length is shortened by a smaller amount.
A Course In Econometrics: A Review, Douglas G. Steigerwald
A Course In Econometrics: A Review, Douglas G. Steigerwald
Douglas G. Steigerwald
No abstract provided.
Uncertainty And Policy Agressiveness, Douglas Steigerwald, Roger Craine
Uncertainty And Policy Agressiveness, Douglas Steigerwald, Roger Craine
Douglas G. Steigerwald
How should a decision maker proceed with uncertain knowledge of the decision outcome? We use the unknown coefficient control problem to shed light on the issue.