Document Type

Article

Publication Date

1-14-2020

Publication Title

Open Journal of Statistics

Volume

10

Issue

1

First page number:

31

Last page number:

51

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.

Keywords

Time Series; ARIMA; ARMA; Autocorrelation, Partial Autocorrelation; Ljung-Box Test; Bootstrap; Simulation

Disciplines

Longitudinal Data Analysis and Time Series | Physical Sciences and Mathematics | Statistics and Probability

File Format

PDF

File Size

5530 KB

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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