We propose a nonparametric approach to the estimation and testing of structural change in time series regression models. Under the null of a given set of the coefficients being constant, we develop estimators of both the nonparametric and parametric components. Given the estimators under null...

In this paper, we consider a general class of vector error correction models which allow for asymmetric and non-linear error correction. We provide asymptotic results for (quasi-)maximum likelihood (QML) based estimators and tests. General hypothesis testing is considered, where testing for...

A two-step estimation method of stochastic volatility models is proposed: In the first step, we estimate the (unobserved) instantaneous volatility process using the estimator of Kristensen (2010, Econometric Theory 26). In the second step, standard estimation methods for fully observed diffusion...

This paper investigates the asymptotic properties of the Gaussian quasi-maximum-likelihood estimators (QMLE?s) of the GARCH model augmented by including an additional explanatory variable - the so-called GARCH-X model. The additional covariate is allowed to exhibit any degree of persistence as...

We develop a class of Poisson autoregressive models with additional covariates (PARX) that can be used to model and forecast time series of counts. We establish the time series properties of the models, including conditions for stationarity and existence of moments. These results are in turn...

We develop novel methods for estimation and filtering of continuous-time models with stochastic volatility and jumps using so-called Approximate Bayesian Computation which build likelihoods based on limited information. The proposed estimators and filters are computationally attractive relative...

This paper develops a new systematic approach to implement approximate solutions to asset pricing models within multi-factor diffusion environments. For any model lacking a closed-form solution, we provide a solution obtained by expanding the analytically intractable model around a known...

A nonparametric kernel estimator of the drift (diffusion) term in a diffusion model are developed given a preliminary parametric estimator of the diffusion (drift) term. Under regularity conditions, rates of convergence and asymptotic normality of the nonparametric estimators are established. We...

A novel estimation method for two classes of semiparametric scalar diffusion models is proposed: In the first class, the diffusion term is parameterised and the drift is left unspecified, while in the second class only the drift term is specified. Under the assumption of stationarity, the...

Semiparametric models are characterized by a finite- and infinite-dimensional (functional) component. As such they allow for added flexibility over fully parametric models, and at the same time estimators of parametric components can be developed that exhibit standard parametric convergence...