Applied Time-Series Analysis in Marketing

Time-series models constitute a core component of marketing research and are applied to solve a wide spectrum of marketing problems. This chapter covers traditional and modern time-series models with applications in extant marketing research. We first introduce basic concepts and diagnostics including stationarity test (the augmented Dicky-Fuller test of unit roots), and autocorrelation plots via autocorrelation function (ACF) and partial autocorrelation function (PACF). We then discuss single-equation time-series models such as autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models with and without exogenous variables. Multiple-equation dynamic systems including vector autoregressive (VAR) models together with generalized impulse response functions (GIRFs) and generalized forecast error variance decomposition (GFEVD) are then discussed in detail. Other relevant models such as generalized autoregressive conditional heteroskedasticity (GARCH) models are covered. Finally, a case study accompanied by data and R codes is provided to demonstrate detailed estimation steps of key models covered in this chapter.