In this issue we have Forecasting Government Bond Yields with Large Bayesian VARs, the Forecasting Performance of Real Time Estimates of the EURO Area Output Gap, Bias Correction and OutofSample Forecast Accuracy, Combining NonReplicable Forecasts, and more.
 Forecasting Government Bond Yields with Large Bayesian VARs
Date: 
2010 
By: 
A. Carriero 
URL: 

We propose a new approach to forecasting the term structure of interest rates, which allows to efficiently extract the information contained in a large panel of yields. In particular, we use a large Bayesian Vector Autoregression (BVAR) with an optimal amount of shrinkage towards univariate AR models. Focusing on the U.S., we provide an extensive study on the forecasting performance of our proposed model relative to most of the existing alternative speci.cations. While most of the existing evidence focuses on statistical measures of forecast accuracy, we also evaluate the performance of the alternative forecasts when used within trading schemes or as a basis for portfolio allocation. We extensively check the robustness of our results via subsample analysis and via a data based Monte Carlo simulation. We .nd that: i) our proposed BVAR approach produces forecasts systematically more accurate than the random walk forecasts, though the gains are small; ii) some models beat the BVAR for a few selected maturities and forecast horizons, but they perform much worse than the BVAR in the remaining cases; iii) predictive gains with respect to the random walk have decreased over time; iv) di¤erent loss functions (i.e., “statistical” vs “economic”) lead to di¤erent ranking of speci.c models; v) modelling time variation in term premia is important and useful for forecasting. 

Keywords: 
Bayesian methods, Forecasting, Term Structure. 
JEL: 
C11 
 The Forecasting Performance of Real Time Estimates of the EURO Area Output Gap
Date: 
2010 
By: 
Massimiliano Marcellino 
URL: 

This paper provides real time evidence on the usefulness of the euro area output gap as a leading indicator for inflation and growth. A genuine realtime data set for the euro area is used, including vintages of several alternative gap estimates. It turns out that, despite some difference across output gap estimates and forecast horizons, the results point clearly to a lack of any usefulness of realtime output gap estimates for inflation forecasting both in the short term (onequarter and oneyear ahead) and the medium term (twoyear and threeyear ahead). By contrast, we find some evidence that several output gap estimates are useful to forecast real GDP growth, particularly in the short term, and some appear also useful in the medium run. A comparison with the US yields similar conclusions. 

Keywords: 
Output gap, realtime data, euro area, inflation forecasts, real GDP forecasts, data revisions. 
JEL: 
E31 
 Bias Correction and OutofSample Forecast Accuracy
Date: 
201005 
By: 
Hyeongwoo Kim 
URL: 

We evaluate the usefulness of biascorrection methods for autoregressive (AR) models in terms of outofsample forecast accuracy, employing two popular methods proposed by Hansen (1999) and So and Shin (1999). Our Monte Carlo simulations show that these methods do not necessarily achieve better forecasting performances than the biasuncorrected Least Squares (LS) method, because bias correction tends to increase the variance of the estimator. There is a gain from correcting for bias only when the true data generating process is sufficiently persistent. Though the bias arises in finite samples, the sample size (N) is not a crucial factor of the gains from biascorrection, because both the bias and the variance tend to decrease as N goes up. We also provide a real data application with 7 commodity price indices which confirms our findings. 

Keywords: 
SmallSample Bias, Grid Bootstrap, Recursive Mean Adjustment, OutofSample Forecast 
JEL: 
C52 
 Combining NonReplicable Forecasts
Date: 
20100501 
By: 
ChiaLin Chang 
URL: 

Macroeconomic forecasts are often based on the interaction between econometric models and experts. A forecast that is based only on an econometric model is replicable and may be unbiased, whereas a forecast that is not based only on an econometric model, but also incorporates an expert’s touch, is nonreplicable and is typically biased. In this paper we propose a methodology to analyze the qualities of combined nonreplicable forecasts. One part of the methodology seeks to retrieve a replicable component from the nonreplicable forecasts, and compares this component against the actual data. A second part modifies the estimation routine due to the assumption that the difference between a replicable and a nonreplicable forecast involves a measurement error. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the methodological approach. 

Keywords: 
Combined forecasts; efficient estimation; generated regressors; replicable forecasts; nonreplicable forecasts; expert’s intuition 
JEL: 
C53 
 Empirical Simultaneous Confidence Regions for PathForecasts
Date: 
2010 
By: 
Òscar Jordà 
URL: 

Measuring and displaying uncertainty around pathforecasts, i.e. forecasts made in period T about the expected trajectory of a random variable in periods T+1 to T+H is a key ingredient for decision making under uncertainty. The probabilistic assessment about the set of possible trajectories that the variable may follow over time is summarized by the simultaneous confidence region generated from its forecast generating distribution. However, if the null model is only approximative or altogether unavailable, one cannot derive analytic expressions for this confidence region, and its nonparametric estimation is impractical given commonly available predictive sample sizes. Instead, this paper derives the approximate rectangular confidence regions that control false discovery rate error, which are a function of the predictive sample covariance matrix and the empirical distribution of the Mahalanobis distance of the pathforeca st errors. These rectangular regions are simple to construct and appear to work well in a variety of cases explored empirically and by simulation. The proposed techniques are applied to provide con.dence bands around the Fed and Bank of England realtime pathforecasts of growth and inflation. 

Keywords: 
path forecast, forecast uncertainty, simultaneous confidence region, Scheffé’s Smethod,Mahalanobis distance, false discovery rate. 
JEL: 
C32 
 Forecasting Money Supply in India: Remaining Policy Issues
Date: 
2010 
By: 
Das, Rituparna 
URL: 

This article analyzes the issues, unaddressed in the contemporary econometric literature on forecasting money supply in India, with the help of the relevant studies. In doing so there is an attempt to ascertain what could be the best fit model to forecast money supply in India. 

Keywords: 
Interest Rate; Forecast; Money Supply; Assets; Deregulation; Market 
JEL: 
E47 
 Are Inflation Forecasts from Major Swedish Forecasters Biased?
Date: 
20100603 
By: 
Lundholm, Michael (Dept. of Economics, Stockholm University) 
URL: 

Inflation forecasts made 19992005 by Sveriges Riksbank and Konjunkturinstitet of Swedish inflation rates 19992007 are tested for unbiasedness; i.e., are the mean forecast errors zero? The bias is in the order of 0.1 percentage units for horizons below one year and in the order of 0.1 and 0.6 (depending on inflation measure) above one year. Using the maximum entropy bootstrap for inference bias is significant whereas inference using HAC indicates insignificance. 

Keywords: 
Forecast evaluation; inflation; unbiasedness; maximum entropy bootstrap 
JEL: 
E37 
 Forecasting Realized Volatility with Linear and Nonlinear Models
Date: 
201003 
By: 
Francesco Audrino (University of St. Gallen) 
URL: 

In this paper we propose a smooth transition tree model for both the conditional mean and variance of the shortterm interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasimaximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US shortterm interest rate we find (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes’ structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging). 

Keywords: 
shortterm interest rate, regression tree, smooth transition, conditional variance, bagging, asymptotic theory 
Taken from the NEPFOR mailing list edited by Rub Hyndman.