Likelihood-Based Specification Tests for Dynamic Factor Models

Specification Tests for Nonlinear Dynamic Models

We propose a new adequacy test and a graphical evaluation tool for nonlinear dynamic models. The proposed techniques can be applied in any setup where parametric conditional distribution of the data is specified, in particular to models involving conditional volatility, conditional higher moments, conditional quantiles, asymmetry, Value at Risk models, duration models, diffusion models, etc. Co...

Generalized Likelihood Ratio Tests for Additive Models

Likelihood-ratio tests for hidden Markov models.

We consider hidden Markov models as a versatile class of models for weakly dependent random phenomena. The topic of the present paper is likelihood-ratio testing for hidden Markov models, and we show that, under appropriate conditions, the standard asymptotic theory of likelihood-ratio tests is valid. Such tests are crucial in the specification of multivariate Gaussian hidden Markov models, whi...

Specification Tests for Nonlinear Time Series Models

This paper proposes a new parametric model adequacy test for possibly nonlinear time series models such as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional duration (ACD). We consider the correct specification of parametric conditional distributions, not only some particular conditional characteristics. Using the true parametric conditional distri...

Tests for Parameter Instability in Dynamic Factor Models

We develop tests for structural breaks of factor loadings in dynamic factor models. We focus on the joint null hypothesis that all factor loadings are constant over time. Because the number of factor loading parameters goes to infinity as the sample size grows, the conventional test cannot be used. Based on the fact that the estimated factors will demonstrate a higher dimension under the altern...