The multivariate reduced rank regression model plays an important role in econometrics. Examples include co-integration analysis and models with a factor structure. Geweke (1996) provided the foundations for a Bayesian analysis of this model. Unfortunately several of the full conditional posterior distributions, which forms the basis for constructing a Gibbs sampler for the poster distribution,...

The forecast combination literature has optimal combination methods, however, empirical studies have shown that the simple average is notoriously di cult to improve upon. This paper introduces a novel way to choose a subset of forecasters who might have specialized knowledge to improve upon the simple average over all forecasters in the SPF. In particular, taking the average of forecasters that...

This paper develops optimal tests for model selection between two nested models in the presence of underlying parameter instability. These are joint tests for both parameter instability and a null hypothesis on a subset of the parameters. They modify the existing tests for parameter instability to allow the parameter vector to be unknown. These tests statistics are useful if one is interested i...

Nowadays, energy modeling is among the most required tools for optimization of system performance on a regional, national and global scale. The need studies models justified by increasing energetic demand, evolution power generation technologies transition to modern economics developing countries. aim this study provide different aspects, techniques characteristics existing in literature. A bet...

We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by autoregressive (AR) and Markov-Switching AR models, which are commonly used for business cycle analysis. In ...

In the exponential smoothing approach to forecasting, restrictions are often imposed on the smoothing parameters which ensure that certain components are exponentially weighted averages. In this paper, a new general restriction is derived on the basis that the one-step ahead prediction error can be decomposed into permanent and transient components. It is found that this general restriction red...

Density forecast combinations are becoming increasingly popular as a means of improving forecast ‘accuracy’, as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score of the generalised density combination where the combination weights depend on the variable one is try...

We generalise the Black-Litterman (BL) portfolio management framework to incorporate time-variation in the conditional distribution of returns in the asset allocation process. We evaluate the performance of the dynamic BL model using both standard performance ratios as well as other measures that are designed to capture tail risk in the presence of non-normally distributed asset returns. We fin...

This paper proposes a computationally efficient algorithm for quantifying the impact of interest-rate risk and longevity risk on the distribution of annuity values in the distant future. The algorithm simulates the state variables out to the end of the horizon period and then uses a Taylor series approximation to compute approximate annuity values at the end of that period, thereby avoiding a c...

The methods used in most SAT validity studies cannot be justified by any sample selection assumptions and are uninformative about the source of the SAT’s predictive power. A new omitted variables estimator is proposed; plausibly consistent estimates of the SAT’s contribution to predictions of University of California freshman grade point averages are about 20% smaller than the usual methods imp...