When customers are classified into ordered categories, which are defined from the outset, it may happen that the majority belongs to a single category. If a market researcher is interested in the correlation between the classification and individual characteristics, the natural question is...

We introduce a multi-level smooth transition model for a panel of time series variables, which can be used to examine the presence of common non-linear features across many such variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully...

We propose a simulation-based technique to calculate impulse-response functions and their confidence intervals in a market share attraction model [MCI]. As an MCI model implies a reduced form model for the logs of relative market shares, simulation techniques have to be used to obtain the...

Scanner data for fast moving consumer goods typically amount to panels of time series where both N and T are large. To reduce the number of parameters and to shrink parameters towards plausible and interpretable values, multi-level models turn out to be useful. Such models contain in the second...

In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean...

We propose a new and simple methodology to estimate the loss function associated with experts' forecasts. Under the assumption of conditional normality of the data and the forecast distribution, the asymmetry parameter of the lin-lin and linex loss function can easily be estimated using a linear...

We propose a multivariate nonlinear econometric time series model, which can be used to examine if there is common nonlinearity across economic variables. The model is a multivariate censored latent effects autoregression. The key feature of this model is that nonlinearity appears as separate...

A new time series model is proposed to describe observed asymmetries in postwar unemployment data. We assume that recession periods, when unemployment increases rapidly, are caused by unobserved positive shocks. The generating mechanism of these latent shocks is a censored regression model,...

This paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption series.

The GARCH model and the Stochastic Volatility [SV] model are competing but non-nested models to describe unobserved volatility in asset returns. We propose a GARCH model with an additional error term, which can capture SV model properties, and which can be used to test GARCH against SV. We...