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A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...
Persistent link: https://www.econbiz.de/10005002781
A prediction model is any statement of a probability distribution for an outcome not yet observed. This study considers the properties of weighted linear combinations of n prediction models, or linear pools, evaluated using the conventional log predictive scoring rule. The log score is a concave...
Persistent link: https://www.econbiz.de/10005091090
, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The comparison is based on the accuracy of … volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree. …
Persistent link: https://www.econbiz.de/10010787777
We present an estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH … nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models. …
Persistent link: https://www.econbiz.de/10011116269
We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to … space models with particular attention to MS-GARCH models. Our multi-move sampling strategy is based on the Forward … Filtering Backward Sampling (FFBS) applied to an approximation of MS-GARCH. Another important contribution is the use of multi …
Persistent link: https://www.econbiz.de/10010602299
-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between …
Persistent link: https://www.econbiz.de/10011256766
Forecast models with large cross-sections are often subject to overparameterization leading to unstable parameter estimates and hence inaccurate forecasts. Recent articlessuggest that a large Bayesian vector autoregression (BVAR) with sufficient prior information dominates competing approaches....
Persistent link: https://www.econbiz.de/10010877596
Recent articles suggest that a Bayesian vector autoregression (BVAR) with shrinkage is a good forecast device even when the number of variables is large. In this paper we evaluate different variants of the BVAR with respect to their forecast accuracy for euro area real GDP growth and HICP...
Persistent link: https://www.econbiz.de/10010877728
Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock … forecasting context. While none of the methods clearly emerges as best, some techniques turn out to be useful to improve the … forecasting performance. …
Persistent link: https://www.econbiz.de/10010905649
Employing a large number of financial indicators, we use Bayesian model averaging (BMA) to forecast real-time measures of economic activity. The indicators include credit spreads based on portfolios, constructed directly from the secondary market prices of outstanding bonds, sorted by maturity...
Persistent link: https://www.econbiz.de/10011009943