First-order Autoregressive Error Research Articles

Standard Errors for Elasticities: A Comparison of Bootstrap and Asymptotic Standard Errors

This article compares two methods of deriving standard errors for elasticities in a linear expenditure demand system with first-order autoregressive errors. The first is the ordinary Taylor series method and the second is Efron's bootstrap. In an example problem, these two methods yielded similar values for the standard errors, with the exception of the income elasticity standard errors, for which the asymptotic standard errors were apparently too large by a factor of two.

Autoregressive errors with a repeated measures design in clinical trials

Repeated measures designs are common in clinical trials where recordings on a patient are made repeatedly over time. A basic assumption for the analysis of repeated measures designs is that a common correlation structure between observations for a given patient exists. When several observations are made for each patient this assumption may be tenuous at best. This article presents a pragmatic approach for combining a repeated measures design with a first-order autoregressive error component. A method of filtering the observed data to account for the autoregressive structure of the errors is considered. The effect on the analysis of variance results after extraction of the autoregressive component is seen by comparison of ANOVA summaries.

Demand for money: tests of functional forms and stability

Extending the family of functional forms of demand for money to the quadratic flexible functional family with first-order autoregressive errors, this paper tests several member functions of the family, tests stability of functions over time, and investigates the effect of temporal aggregation level of data. The stability hypothesis is strongly rejected and the acceptable functional form is found to depend on the temporal aggregation level of data.

A Test for the Presence of First-Order Vector Autoregressive Errors when Lagged Endogenous Variables are Present

This paper derives an asymptotically valid test for first-order autoregressive errors. The test is derived in a simultaneous system of equations context, and allows lagged endogenous variables to be present in the model.

Estimation of Seemingly Unrelated Regressions with Vector Autoregressive Errors

Abstract We derive an asymptotically efficient method of estimating the coefficients of systems of seemingly unrelated regression equations when a first-order vector autoregressive error specification is hypothesized. This error specification is shown to contain several commonly used specifications as special cases. A Monte Carlo experiment was performed to examine the finite sample performance of our estimator in comparison with several other estimators. The general results were that the new estimator performed relatively well.