We develop a panel data count model combined with a latent Gaussian spatio-temporal heterogenous state process to analyze monthly severe crimes at the census tract level in Pittsburgh, Pennsylvania. Our data set combines Uniform Crime Reporting data with socio-economic data from the 2000 census....

We develop a new Bayesian panel regression approach to estimating an unknown number of breaks and forecasting future outcomes in the presence of scarce information from new regimes. Our approach allows the parameters to be heterogeneous across units but assumes that the timing of breaks is...

Nowcasting regards the inference on the present realization of random variables, on the basis of information available until a recent past. This paper proposes a modelling strategy aimed at a best use of the data for nowcasting based on panel data with severe deficiencies, namely short times...

Single equation models are well established among academics and practitioners to perform temporal disaggregation of low frequency time series using available related series. In this paper, we propose an extension that exploits information from the cross-sectional dimension. More specifically, we...

This paper develops new model selection methods for forecasting panel data using a set of least squares (LS) vector autoregressions. Model selection is based on minimizing the estimated quadratic forecast risk among candidate models. We provide conditions under which the selection criterion is...

We propose a new forecast combination method for panel data vector autoregressions that permit limited forms of parameterized heterogeneity (including fixed effects or incidental trends). Models are fitted using bias-corrected least squares in order to attenuate the effects of small sample bias...

We develop a new set of model selection methods for direct multistep forecasting of panel data vector autoregressive processes. Model selection is based on minimizing the estimated multistep quadratic forecast risk among candidate models. In order to attenuate the small sample bias of the least...

This paper constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients and cross-sectional heteroskedasticity. The panel considered in this paper features a large cross-sectional dimension N but...

This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This...

This paper examines the impact of climate shocks on 13 European economies analysing jointly business and financial cycles, in different phases and disentangling the effects for different sector channels. A Bayesian Panel Markov-switching framework is proposed to jointly estimate the impact of...