Models with random effects/latent variables are widely used for capturing unobserved heterogeneity in multilevel/hierarchical data and account for associations in multivariate data. The estimation of those models becomes cumbersome as the number of latent variables increases due to...

By incorporating the Harvey accumulator into the large approximate dynamic factor framework of Doz et al. (2006), we are able to construct a coincident index of financial conditions from a large unbalanced panel of mixed frequency financial indicators. We relate our financial conditions index,...

This paper discusses a factor model for estimating monthly GDP using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM...

Potentially adverse human and environmental effects due to hormone mimicry of environmental estrogens are a matter of current concern. Environmental estrogens belong to the so-called endocrine active compounds (EAC), which alter signalling processes of the endocrine system leading to a broad...

We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...

This paper examines maximum likelihood estimation via hill climbing and the expectations maximization (EM) algorithm in the context of Hamilton's Markov switching framework. The techniques are explained in detail and are followed by a discussion of both analytic and computational issues. Both...

This paper investigates whether information from foreign yield curves helps forecast domestic yield curves out-of-sample. A nested methodology to forecast yield curves in domestic and international settings is applied on three major countries (the US, Germany and the UK). This novel methodology...

In this paper we propose a methodology to estimate a dynamic factor model on data sets with an arbitrary pattern of missing data. We modify the Expectation Maximisation (EM) algorithm as proposed for a dynamic factor model by Watson and Engle (1983) to the case with general pattern of missing...

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form...

Generalized linear models might not be appropriate when the probability of extreme events is higher than that implied by the normal distribution. Extending the method for estimating the parameters of a double Pareto lognormal distribution (DPLN) in Reed and Jorgensen (2004), we develop an EM...