Using HAC Estimators for Intervention Analysis

Abstract

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.

The Road to Recovery: Impact of COVID-19 on Healthcare Utilization in South Korea Using an Interrupted Time-Series Analysis

“…8,22 The model applied the first-order autoregressive structure with heterogenous variances, labeled as AR (1), and Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors to control for serial correlation in the residual errors. [23][24][25] We divided the time series into a pre-COVID-19 phase and then two phases of the COVID-19 period in order to fit a piecewise regression model with three segments. The ITS model included two breakpoints to assess the impact of both the first arrival of the first cases of COVID-19 and the era when vaccination coverage became prevalent in late 2021.…”

The road to recovery: impact of COVID-19 on healthcare utilization in South Korea in 2016–2022 using an interrupted time-series analysis

“… 8 , 22 The model applied the first-order autoregressive structure with heterogenous variances, labeled as AR (1), and Newey–West Heteroskedasticity and Autocorrelation Consistent (HAC) standard errors to control for serial correlation in the residual errors. 23 , 24 , 25 …”