Moment Ratio Estimation of Autoregressive/Unit Root Parameters and Autocorrelation-Corrected Standard Errors
نویسنده
چکیده
A Moment Ratio Estimator is proposed for the parameters of an Autoregressive (AR) model of the error in an Ordinary Least Squares (OLS) linear regression. Although it is computed from the conventional residual autocorrelation coefficients, it greatly reduces their bias, and provides corrected standard errors with far less bias and confidence intervals with far less size distortion than conventional alternatives. The estimator is in the spirit of the Median Unbiased estimator of Andrews (1993) and McCulloch (2008), but is more easily computed and provides smaller standard error bias in most cases. The presence of a unit root in the errors, and therefore the absence of a cointegrating relationship, requires reposing the problem, but does not by itself indicate that an OLS correlation between the variables is spurious. Hypothesis testing is standard, provided it is based on squared quasi-differenced residuals, and not on the squared residuals themselves. Although the present paper is restricted to the AR(1) case, the approach is readily extendable to higher-order AR processes. An exact unit root test similar to that of Andrews (1993) is implemented for the AR(1) case. The Moment Ratio estimator is applied to an income trend line regression, as well as to a monetary base demand function. In both cases, the Moment Ratio autoregressive coefficient estimate is quite close to unity, and a unit root in the errors cannot be rejected. However, the trend slope remains highly significant in the income trend line regression, and both the income elasticity and interest semi-elasticity remain highly significant in the base demand equation, even when a unit root is imposed. It is observed that despite their consistency, the popular HAC standard errors of Newey and West (1987) can greatly overstate the precision of OLS coefficient estimates with sample sizes and serial correlation commonly found in economic studies when, as has become standard, “automatic bandwidth selection” is employed.
منابع مشابه
Moment Ratio estimation of autoregressive/unit root parameters and autocorrelation-consistent standard errors
A Moment Ratio estimator is proposed for an AR(p) model of the errors in an OLS regression, that provides standard errors with far less median bias and confidence intervals with far better coverage than conventional alternatives. A unit root, and therefore the absence of cointegration, does not necessarily mean that a correlation between the variables is spurious. The estimator is applied to a ...
متن کاملAutocorrelation-Corrected Standard Errors Using Moment Ratio Estimates of the Autoregressive/Unit Root Parameter
A Moment Ratio Estimator is proposed for the parameters of an Autoregressive (AR) model of the error in an Ordinary Least Squares (OLS) linear regression. Although it is computed from the conventional residual autocorrelation coefficients, it greatly reduces their bias, and provides corrected standard errors with far less bias than alternatives. The estimator is in the spirit of the Median Unbi...
متن کاملForecasting autoregressive time series in the presence of deterministic components
This paper studies the error in forecasting an autoregressive process with a deterministic component. We show that when the data are strongly serially correlated, forecasts based on a model that detrends the data using OLS before estimating the autoregressive parameters are much less precise than those based on an autoregression that includes the deterministic components, and the asymptotic dis...
متن کاملMedian-Unbiased Estimation of Higher Order Autoregressive/Unit Root Processes and Autocorrelation Consistent Covariance Estimation in a Money Demand Model
It is shown that the Newey-West (1987) Heteroskedasticity and Autocorrelation Consistent (HAC) covariance matrix estimator can greatly understate the standard errors of OLS regression coefficient estimates in finite samples, and therefore comparably overstate t-statistics. Although the bias vanishes in infinite samples and is tolerable in samples as small as 10, it can lead to t-statistics that...
متن کاملپیشبینی خشکسالی هیدرولوژیک با استفاده از سریهای زمانی
INTRODUCTION Hydrologic drought in the sense of deficient river flow is defined as the periods that river flow does not meet the needs of planned programs for system management. Drought is generally considered as periods with insignificant precipitation, soil moisture and water resources for sustaining and supplying the socioeconomic activities of a region. Thus, it is difficult to give a univ...
متن کامل