Prepared for the Handbook of Economic Forecasting, vol 2 This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and spe-...

Prepared for the Handbook of Economic Forecasting, vol 2 <p> This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and spe-...</p>

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 generalized impulse response analysis and forecast error variance decompositions, we uncover a...

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...

In this paper I propose a novel optimal linear filter for smoothing, trend and signal extraction for time series with a unit root. The filter is based on the Singular Spectrum Analysis (SSA) methodology, takes the form of a particular moving average and is different from other linear filters...

This chapter examines the problems of dealing with trending type data when there is uncertainty over whether or not we really have unit roots in the data. This uncertainty is practical – for many macroeconomic and financial variables theory does not imply a unit root in the data however unit...

The Israeli-Palestinian conflict constitutes a prominent example of a long-lasting political conflict which has major consequences for the livelihoods of the people on both sides. The agricultural sectors of the Palestinian and Israeli economies are tightly connected. However, various security...

The Factor-augmented Error Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run...

Starting from the dynamic factor model for non-stationary data we derive the factor-augmented error correction model (FECM) and, by generalizing the Granger representation theorem, its moving-average representation. The latter is used for the identification of structural shocks and their...

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor- augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual...