Time-Series Models in Marketing

Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains that have received considerable attention in the marketing literature. These are (1) the use of persistence modelling and (2) the use of state space models.Marketing;Persistence;State Space;Time Series

Evaluation of survey effects in pre-election polls

Pre-election polls can suffer from survey effects. For instance, individuals taking part in the poll may become more aware of the upcoming election so that they become more inclined to vote. Such effects cause biases in forecasted outcomes of elections. We propose a simple methodology that takes such survey effects explicitly into account when translating poll results into election outcomes. By collecting data both before and after the election, the survey effects can be estimated and used as correction factors in later polls. We illustrate our method by means of a field study with data collected before and after the2007 regional elections (for `Provincial States') in the Netherlands. Our study provides empirical evidence of significant positive survey effects with respect to voter participation, and this effect is the largest for left-wing voters. That is, surveys seem to motivate left-wing people who otherwise would not have participated in the elections. This means that both the voter turnout and the number of seats going to left-wing parties may be overestimated by pre-election polls that do not correct for survey effects.data collection;bias correction;survey effects;intention modification;pre-election polls;turnout forecast

Seasonal adjustment and the business cycle in unemployment

Several recent studies show that seasonal variation and cyclical variation in unemployment are correlated. A common finding is that seasonality tends to differ across the business cycle stages of recessions and expansions. Since seasonal adjustment methods assume that the two sources of variation can somehow be separated, the present study examines the impact of seasonal adjustment on the analysis of cyclical patterns. Seasonally adjusted quarterly unemployment data for 5 G-7 countries are modeled by a Smooth Transition Autoregression [STAR] while the corresponding unadjusted data are modeled by a so-called Seasonal STAR [SEASTAR]. A comparison of the implied estimated peaks and troughs shows that there is substantial agreement on the business cycle chronologies, albeit that for seasonally adjusted data recessionary periods tend to last longer.unemployment;seasonality;seasonal adjustment;business cycle

Seasonal adjustment and the business cycle in unemployment

Several recent studies show that seasonal variation and cyclical variation in unemployment are correlated. A common finding is that seasonality tends to differ across the business cycle stages of recessions and expansions. Since seasonal adjustment methods assume that the two sources of variation can somehow be separated, the present study examines the impact of seasonal adjustment on the analysis of cyclical patterns. Seasonally adjusted quarterly unemployment data for 5 G-7 countries are modeled by a Smooth Transition Autoregression [STAR] while the corresponding unadjusted data are modeled by a so-called Seasonal STAR [SEASTAR]. A comparison of the implied estimated peaks and troughs shows that there is substantial agreement on the business cycle chronologies, albeit that for seasonally adjusted data recessionary periods tend to last longer

Modeling item nonresponse in questionnaires

The statistical analysis of empirical questionnaire data can be hampered by the fact that not all questions are answered by all individuals. In this paper we propose a simple practical method to deal with such item nonresponse in case of ordinal questionnaire data, where we assume that item nonresponse is caused by an incomplete set of answers between which the individuals are supposed to choose. Our statistical method is based on extending the ordinal regression model with an additional category for nonresponse, and on investigating whether this extended model describes and forecasts the data well. We illustrate our approach for two questions from a questionnaire held amongst a sample of clients of a financial investment company

Seasonal smooth transition autoregression

In this paper we put forward a new time series model, which describes nonlinearity and seasonality simultaneously. We discuss its representation, estimation of the parameters and inference. This seasonal STAR (SEASTAR) model is examined for its practical usefulness by applying it to 18 quarterly industrial production series. The data are tested for smooth-transitionnonlinearity and for time-varying seasonality. We find that the model fits the data well for 14 of the 18 series. We also consider out-of-sample forecasting where we compare forecasts from theSEASTAR models with forecasts from nested models. It turns out that the SEASTAR model sometimes outperforms the other models, particularly for large horizons. Finally, we compare the SEASTAR models with STAR models for the 14 corresponding seasonally adjusted series, and we find that the estimated business cycle chronologies can be markedly different.seasonality;forecasting;nonlinearity;smooth transition autoregression

Time-Series Models in Marketing

Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains that have received considerable attention in the marketing literature. These are (1) the use of persistence modelling and (2) the use of state space models

Seasonal smooth transition autoregression

In this paper we put forward a new time series model, which describes nonlinearity and seasonality simultaneously. We discuss its representation, estimation of the parameters and inference. This seasonal STAR (SEASTAR) model is examined for its practical usefulness by applying it to 18 quarterly industrial production series. The data are tested for smooth-transition nonlinearity and for time-varying seasonality. We find that the model fits the data well for 14 of the 18 series. We also consider out-of-sample forecasting where we compare forecasts from the SEASTAR models with forecasts from nested models. It turns out that the SEASTAR model sometimes outperforms the other models, particularly for large horizons. Finally, we compare the SEASTAR models with STAR models for the 14 corresponding seasonally adjusted series, and we find that the estimated business cycle chronologies can be markedly different

Evaluation of survey effects in pre-election polls

Pre-election polls can suffer from survey effects. For instance, individuals taking part in the poll may become more aware of the upcoming election so that they become more inclined to vote. Such effects cause biases in forecasted outcomes of elections. We propose a simple methodology that takes such survey effects explicitly into account when translating poll results into election outcomes. By collecting data both before and after the election, the survey effects can be estimated and used as correction factors in later polls. We illustrate our method by means of a field study with data collected before and after the 2007 regional elections (for `Provincial States') in the Netherlands. Our study provides empirical evidence of significant positive survey effects with respect to voter participation, and this effect is the largest for left-wing voters. That is, surveys seem to motivate left-wing people who otherwise would not have participated in the elections. This means that both the voter turnout and the number of seats going to left-wing parties may be overestimated by pre-election polls that do not correct for survey effects