In this issue we have: Forecasting Economic and Financial Variables with Global VARs ; Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails.
|This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model previously estimated over the 1979Q1-2003Q4 period by Dees, de Mauro, Pesaran, and Smith (2007), is used to generate out-of-sample one quarter and four quarters ahead forecasts of real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions made up of 33 countries covering about 90% of world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative s! ample periods. Given the size of the modeling problem, and the heterogeneity of economies considered – industrialised, emerging, and less developed countries – as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed the double-averaged GVAR forecasts performed better than the benchmark competitors, especially for output, inflation and real equity prices.|
|Keywords:||Forecasting using GVAR, structural breaks and forecasting, average forecasts across models and windows, financial and macroeconomic forecasts.|
|JEL:||C32 C51 C53|
|By:||Cees Diks (University of Amsterdam)
Valentyn Panchenko (School of Economics, University of New South Wales)
Dick van Dijk (Econometric Institute, Erasmus University Rotterdam)
|We propose new scoring rules based on partial likelihood for assessing the relative out-of-sample predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. By construction, existing scoring rules based on weighted likelihood or censored normal likelihood favor density forecasts with more probability mass in the given region, rendering predictive accuracy tests biased towards such densities. Our novel partial likelihoodbased scoring rules do not suffer from this problem, as illustrated by means of Monte Carlo simulations and an empirical application to daily S&P 500 index returns.|
|Keywords:||density forecast evaluation; scoring rules; weighted likelihood ratio scores; partial likelihood; risk management.|
|JEL:||C12 C22 C52 C53|
Source: NEP-FOR mailing list edited by Rob Hyndman.