This thesis consists of four papers concerning modelling of count data and tourism demand. For three of the papers the focus is on the integer-valued autoregressive moving average model class (INARMA), and especially on the INAR(1) model. The fourth paper studies the interaction between...

In this paper, I study the estimation of nonlinear models of spatial processes. Generalized estimating equations (GEE) are applied to cross section data with spatial correlations. I use a partial quasi-maximum likelihood estimator (PQMLE) in the first step and use a GEE approach in the second...

In this paper we present a non-linear demand system for households' joint choice of number of trips and days to spend at a destination. The approach, which facilitates welfare analysis of exogenous policy and price changes, is used empirically to study the effects of an increased CO2 tax. In the...

Studying behaviour in economics, sociology, and statistics often involves fitting models in which the response variable depends on a dummy variable (also known as a regime switch variable) or in which the response variable is observed only if a particular selection condition is met. In either...

This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and...

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling...

This paper extends the canonical model of epidemiology, SIRD model, to allow for time varying parameters for real-time measurement of the stance of the COVID-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modelling structure designed for...

This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying distribution. The moments with conditional heteroscedasticity have been discussed. In a Monte Carlo experiment, it was found that the QML estimator performs as well as CLS and...

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling...

We develop a model to account for the long memory property in a bivariate count data framework. We propose a bivariate integer-valued fractional integrated (BINFIMA) model and apply the model to high frequency stock transaction data. The BINFIMA model allows for both positive and negative...