Using HAC Estimators for Intervention Analysis
Open Journal of Statistics
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The purpose of this article is to present an alternative method for intervention analysis of time series data that is simpler to use than the traditional method of fitting an explanatory Autoregressive Integrated Moving Average (ARIMA) model. Time series regression analysis is commonly used to test the effect of an event on a time series. An econometric modeling method, which uses a heteroskedasticity and autocorrelation consistent (HAC) estimator of the covariance matrix instead of fitting an ARIMA model, is proposed as an alternative. The method of parametric bootstrap is used to compare the two approaches for intervention analysis. The results of this study suggest that the time series regression method and the HAC method give very similar results for intervention analysis, and hence the proposed HAC method should be used for intervention analysis, instead of the more complicated method of ARIMA modeling. The alternative method presented here is expected to be very helpful in gaming and hospitality research.
Time Series; ARIMA; ARMA; Autocorrelation, Partial Autocorrelation; Ljung-Box Test; Bootstrap; Simulation
Longitudinal Data Analysis and Time Series | Physical Sciences and Mathematics | Statistics and Probability
Singh, A. K.,
Dalpatadu, R. J.
Using HAC Estimators for Intervention Analysis.
Open Journal of Statistics, 10(1),