We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of...

We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure...

This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying...

In this paper we focus on estimating the degree of cross-sectional dependence in the error terms of a classical panel data regression model. For this purpose we propose an estimator of the exponent of cross-sectional dependence denoted by α; which is based on the number of non-zero pair-wise...

We have argued that from the standpoint of a policy maker who has access to a number of expert forecasts, the uncertainty of a combined forecast should be interpreted as that of a typical forecaster randomly drawn from the pool. With a standard factor decomposition of a panel of forecasts, we...

This note examines the stochastic behaviour of US monthly 10-year government bond yields. Specifically, it estimates a fractional integration model suitable to capture both persistence and non-linearities, these being two important properties of interest rates. Two series are analysed, one from...

This paper analyses the stochastic behaviour of Private Equity returns (a measure of profitability) applying fractional integration methods to an extensive dataset including quarterly data spanning the last four decades for various geographical areas (US, Europe, Asia/Pacific, the Rest of the...

This paper considers a first-order autoregressive panel data model with individual-specific effects and a heterogeneous autoregressive coefficient. It proposes estimators for the moments of the cross-sectional distribution of the autoregressive coefficients, with a focus on the first two...

This paper documents a comparative application of algorithms to deal with the problem of missing values in higher frequency data sets. We refer to Swiss business tendency survey (BTS) data which are conducted in both monthly and quarterly frequency, where an information sub-set is collected at...

An important issue in the analysis of cross-sectional dependence which has received renewed interest in the past few years is the need for a better understanding of the extent and nature of such cross dependencies. In this paper we focus on measures of cross-sectional dependence and how such...