We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

Many empirical studies have shown that factor models produce relatively accurate forecasts compared to alternative short-term forecasting models. These empirical findings have been established for different macroeconomic data sets and different forecast horizons. However, various specifications...

Dramatic changes in macroeconomic time series volatility pose a challenge to contemporary vector autoregressive (VAR) forecasting models. Traditionally, the conditional volatility of such models had been assumed constant over time or allowed for breaks across long time periods. More recent work,...

This paper examines Australia's terms of trade boom since 2003 with a particular interest in quantifying the links between the terms of trade and sectoral performance and identifying an associated 'secondary services boom'. Comparative static general equilibrium modelling and empirical analysis...

Enormous development of firm valuation from many aspects can be seen in the recent period. One of the main fields is scoring, which provides a probability verdict about the future development of a firm: its probability of default. This article focuses on introducing macroeconomic modelling using...

This article proposes a modified method for the construction of diffusion indexes in macroeconomic forecasting using principal component regres- sion. The method aims to maximize the amount of variance of the origi- nal predictor variables retained by the diffusion indexes, by matching the data...

We propose a new method of leading index construction that combines the need for data compression with the objective of forecasting. This so-called principal covariate index is constructed to forecast growth rates of the Composite Coincident Index. The forecast performance is compared with an...

This paper proposes full-Bayes priors for time-varying parameter vector autoregressions (TVP-VARs) which are more robust and objective than existing choices proposed in the literature. We formulate the priors in a way that they allow for straightforward posterior computation, they require...

We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated...

This paper analyses the international linkages of the Korean economy using the GVAR model developed by Greenwood-Nimmo, Nguyen and Shin (2012a, J. Appl. Econometrics). By employing a combination of generalised impulse response analysis and forecast error variance decompositions, we uncover a...