This paper uses a panel structural vector autoregressive (VAR) model to investigate the extent to which global financial conditions, i.e., a global risk-free interest rate and global financial risk, and country spreads contribute to macroeconomic fluctuations in emerging countries. The main findings are: (1) Global financial risk shocks explain about 20 percent of movements both in the country ...

Currently, evidence on the ‘resource curse’ yields a conundrum. While there is much crosssection evidence to support the curse hypothesis, time series analyses using vector autoregressive (VAR) models have found that commodity booms raise the growth of commodity exporters. This paper adopts panel cointegration methodology to explore longer term effects than permitted using VARs. We find strong ...

This paper sets up a nested random effects spatial autoregressive panel data model to explain annual house price variation for 2000-2007 across 353 local authority districts in England. The estimation problem posed is how to allow for the endogeneity of the spatial lag variable producing the simultaneous spatial spillover of prices across districts together with the nested random effects in a p...

In this paper a new approach to factor vector autoregressive estimation, based on Stock and Watson (Implications of dynamic factor models for VAR analysis, NBER Working Paper, no. 11467, 2005), is introduced. In addition to sharing all the relevant features of the Stock–Watson approach, in its static formulation, the proposed method has the advantage of allowing for a more clear-cut interpretat...

This paper examines frequentist risks of Bayesian estimates of VAR regression coefficient and error covariance matrices under competing loss functions, under a variety of non-informative priors, and in the normal and Student-t models. Simulation results show that for the regression coefficient matrix an asymmetric LINEX estimator does better overall than the posterior mean. For the error covari...

Consider a vector autoregressive (VAR) model of order d: xt = A1xt−1 + . . .+Adxt−d + t, t = 0,±1,±2, . . . , (1) where xt ∈ R is a random vector, Ai ∈ Rp×p, i = 1, . . . , d are fixed coefficient matrices and t is a vector of zero-mean white noise, i.e., E( t) = 0, E( t t ) = Σ and E( t T t+h) = 0, for h 6= 0. We assume that the noise covariance matrix Σ is positive definite with bounded large...

Abstract While there has been a great deal of interest in the modelling of non-linearities and regime shifts in economic time series, there is no clear consensus regarding the forecasting abilities of these models. In this paper we develop a general approach to predict multiple time series subject to Markovian shifts in the regime. The feasibility of the proposed forecasting techniques in empir...

As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force...

In this paper, we propose improved IV/GMM estimators for panel vector autoregressive models by extending Hayakawa (2009) where an alternative form of instruments is suggested. We show that the proposed IV estimator has the same asymptotic distribution as the bias corrected maximum likelihood estimator by Hahn and Kuersteiner (2002). Since the proposed estimator is simply change the form of inst...