This Paper describes a procedure for constructing theory restricted prior distributions for BVAR models. The Bayes Factor, which is obtained without any additional computational effort, can be used to assess the plausibility of the restrictions imposed on the VAR parameter vector by competing DSGE models. In other words, it is possible to rank the amount of abstraction implied by each DSGE mode...

We develop a portmanteau test of extremal serial dependence. The test statistic is asymptotically chi-squared under a null of "extremal white noise", as long as extremes are Near-Epoch-Dependent, covering linear and nonlinear distributed lags, stochastic volatility, and GARCHprocesses with possib ly unit or explosive roots. We apply tail speci...c tests to equity market and exchange rate return...

This paper examines how robust economic, political, and demographic variables are related to water and air pollution. Employing Bayesian Averaging of Classical Estimates (BACE) for a cross section of up to 74 countries, 33 variables and 3 proxies for air and water pollution over a period from 1980 to 1995 we confirm the Environmental Kuznets Curve hypothesis, highlight the relevance of efficien...

Godfrey (1996, Journal of Econometrics 72, 275}299) has shown that the Glejser test for heteroskedasticity is valid only under conditional symmetry. Here, modi"cations of the Glejser test are suggested. The proposed test statistics are asymptotically valid even when the disturbances are not symmetrically distributed and can be used to test for heteroskedasticity when conditional location functi...

Assuming elliptically contoured distribution for portfolio asset returns, we derive the exact marginal and joint densities of the global minimum variance portfolio variance, and weights estimators. We also construct a test for the hypothesis that the global minimum variance is less then or equal to a certain value. A stochastic representation and moments of its estimator is provided. We illustr...

We expand Nakamura’s (2005) neural network based inflation forecasting experiment to an alternative non-linear model; a Markov switching autoregressive (MS-AR) model. The two non-linear models perform approximately on par and outperform the linear autoregressive model on short forecast horizons of one and two quarters. Furthermore, the MS-AR model is the best performer on longer horizons of thr...

∗The authors would like to thank the Editor and a referee for some very thoughtful and constructive comments on an earlier draft of the paper. This research has been supported by Australian Research Council Discovery Grant No. DP0208333 and a Ph.D. Scholarship from the School of Business Systems, Monash University. All numerical results in the paper have been produced using the MATLAB software....

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions ability to flexibly whole distribution inflation. In order make our approach accessible and relevant forecasting, we derive an efficient Gibbs sampler by transforming stat...