Starting from a day-to-day model on hotel specific guest nights we obtain an integer-valued moving average model by cross-sectional and temporal aggregation. The two parameters of the aggregate model reflect the daily mean check-in and the daily check-out probability. Letting the parameters be...

The paper discusses some model related issues for time series of the number of shareholders in a stock. The point of departure is an integer-valued autoregressive model of order one. Empirical results are presented for some frequently traded stocks on the Finnish and Swedish stock markets. In...

This note defines the asymmetric count data, first order moving average model and gives some of its basic properties. A brief account of conditional least squares estimation of unknown parameters is also given.

This note gives dynamic effects of discrete and continuous explanatory variables for count data or integer-valued moving average models. An illustration based on a model for the number of transactions in a stock is included.

The integer-valued AR(1) model is generalized to encompass some of the more likely features of economic time series of count data. The generalizations come at the price of loosing exact distributional properties. For most specifications the first and second order both conditional and...

A characterization of endogeneity in the binomial regression model is given. The relationship to a model subject to selection is indicated. Maximum likelihood and nonlinear instrumental variable estimators are obtained and evaluated in a Monte Carlo study. A new LM test and a Hausman test...

Conditional heteroskedasticity properties are derived for some common count data regression and time series models. New extensions are suggested and discussed.

The daily number of occupied hotel rooms in three large Swedish cities is modelled by an integer-valued and binomial autoregression. The model includes the capacity constraint and price variables are incorporated through the parameters of the model. The model implies a duration of hotel visit...

The paper introduces a new approach to incorporating time dependent overdispersion for Poisson related regression models. To handle the added flexibility in conditional heteroskedasticity in time series count data some wellknown estimators are adapted and a GMM type estimator is suggested. The...

A new approach to limited-dependent variable count data or other model types is considered. Instead of adopting maximum likelihood estimation based on a full distributional assumption or smoothing techniques and semiparametric estimation, the novel idea is to use an approximation to the...