In this issue we have: Volatility and Long Term Relations in Equity Markets ; Large Bayesian VARs ; Quantile Regression Methods of Estimating Confidence Intervals for WASDE Price Forecasts ; Short-Term Forecasts of Euro Area GDP Growth ; Understanding Errors in EIA Projections of Energy Demand.
|The aim of this paper is twofold. First it aims to compare several GARCH family models in order to model and forecast the conditional variance of German, Swiss, and UK stock market indexes. The main result is that all GARCH family models show evidence of asymmetric effects. Based on the "out of sample" forecasts I can say that for each market considered there is a model that will lead to better volatility forecasts. Secondly a long run relation between these markets was investigated using the cointegration methodology. Cointegration tests show that DAX30, FTSE100, and SMI indexes move together in the long term. The VECM model indicates a positive long run relation among these indexes, while the error correction terms indicate that the Swiss market is the initial receptor of external shocks. One of the main findings of this analysis is that although the UK, Switzerland and Germany do not share a common currency! , the diversification benefits of investing in these countries could be very low given that their stock markets seem to move together in the lung term.|
|Keywords:||Stock Returns; Volatility; GARCH models; Cointegration|
|JEL:||C53 G15 C22|
|This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.|
|Keywords:||Bayesian VAR, Forecasting, Monetary VAR, large cross-sections|
|JEL:||C11 C13 C33 C53|
Irwin, Scott H.
Good, Darrel L.
|This paper explores the use of quantile regression for estimation of empirical confidence limits for WASDE forecasts of corn, soybean, and wheat prices. Quantile regressions for corn, soybean, and wheat forecast errors over 1980/81 through 2006/07 were specified as a function of forecast lead time. Estimated coefficients were used to calculate forecast intervals for 2007/08. The quantile regression approach to calculating forecast intervals was evaluated based on out-of-sample performance. The accuracy of the empirical confidence intervals was not statistically different from the target level about 87% of the time prior to harvest and 91% of the time after harvest.|
|Keywords:||Demand and Price Analysis,|
|This paper evaluates models that exploit timely monthly releases to compute early estimates of current quarter GDP (now-casting) in the euro area. We compare traditional methods used at institutions with a new method proposed by Giannone, Reichlin, and Small (2005). The method consists in bridging quarterly GDP with monthly data via a regression on factors extracted from a large panel of monthly series with different publication lags. We show that bridging via factors produces more accurate estimates than traditional bridge equations. We also show that survey data and other ‘soft' information are valuable for now-casting.|
|Keywords:||Forecasting, Monetary Policy, Factor Model, Real Time Data, Large data-sets, News|
|JEL:||E52 C33 C53|
|By:||Fischer, Carolyn (Resources for the Future)
Morgenstern, Richard D.
|This paper investigates the potential for systematic errors in the Energy Information Administration's (EIA) widely used Annual Energy Outlook, focusing on the near- to midterm projections of energy demand as measured in physical quantities. Overall, based on an analysis of the EIA's 22-year projection record, we find a fairly modest but persistent tendency to underestimate total energy demand by an average of 2 percent per year over the one- to five-year projection horizon after controlling for projection errors in gross domestic product, oil prices, and heating/cooling degree days. For the 14 individual fuels/consuming sectors routinely reported by the EIA, we observe a great deal of directional consistency in the error patterns over time, ranging up to 7 percent per year. Electric utility renewables, electric utility natural gas, transportation distillate, and residential electricity all show significant bi! ases, on average, across the full five year projection horizon examined. Projections for certain other fuels/consuming sectors have significant unexplained errors for selected time horizons. Independent evaluation of this type can be useful for validating ongoing analytic efforts and for prioritizing future model revisions.|
|Keywords:||EIA, energy forecasting, bias|
Taken from the NEP-FOR mailing list edited by Ron Hyndman.