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Nonstationary conditional trend analysis: an application to scanner panel data

Author: Tibrewala, Vikas ; Lenk, Peter J ; Rao, Ambar G.INSEAD Area: Marketing Series: Working Paper ; 92/15/MKT Publisher: Fontainebleau : INSEAD, 1992.Language: EnglishDescription: 37 p.Type of document: INSEAD Working Paper Online Access: Click here Abstract: Conditional trend analysis (CTA) predicts the number of purchases in a test period by all households that purchase a given number of items in a base period. The underlying model assumes that households' purchases follow stationary Poisson processes with rate parameters that vary across the households in a market. However, stationarity is often an unrealistic assumption because of marketing variables and seasonal effects. This paper extends CTA to the non stationary setting and compares the stationary and non stationary models. Falsely assuming stationarity systematically biases forecasts. Although modelling nonstationarity reduces bias, under-prediction, especially of the zero class, persists. It is shown that this under-prediction is, in part, a mathematical artefact due to the skewness of the negative binomial distribution. The methodology is applied to scanner panel data
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Conditional trend analysis (CTA) predicts the number of purchases in a test period by all households that purchase a given number of items in a base period. The underlying model assumes that households' purchases follow stationary Poisson processes with rate parameters that vary across the households in a market. However, stationarity is often an unrealistic assumption because of marketing variables and seasonal effects. This paper extends CTA to the non stationary setting and compares the stationary and non stationary models. Falsely assuming stationarity systematically biases forecasts. Although modelling nonstationarity reduces bias, under-prediction, especially of the zero class, persists. It is shown that this under-prediction is, in part, a mathematical artefact due to the skewness of the negative binomial distribution. The methodology is applied to scanner panel data

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