nep-for New Economics Papers
on Forecasting
Issue of 2010‒06‒11
eight papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Government Bond Yields with Large Bayesian VARs By A. Carriero; G. Kapetanios; M. Marcellino
  2. The Forecasting Performance of Real Time Estimates of the EURO Area Output Gap By Massimiliano Marcellino; Alberto Musso
  3. Bias Correction and Out-of-Sample Forecast Accuracy By Hyeongwoo Kim; Nazif Durmaz
  4. Combining Non-Replicable Forecasts By Chia-Lin Chang; Philip Hans Franses; Michael McAleer
  5. Empirical Simultaneous Confidence Regions for Path-Forecasts By Òscar Jordà; Malte Knüppel; Massimiliano Marcellino
  6. Forecasting Money Supply in India: Remaining Policy Issues By Das, Rituparna
  7. Are Inflation Forecasts from Major Swedish Forecasters Biased? By Lundholm, Michael
  8. Forecasting Realized Volatility with Linear and Nonlinear Models By Francesco Audrino; Marcelo Cunha Medeiros

  1. By: A. Carriero; G. Kapetanios; M. Marcellino
    Abstract: 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 C53 E43 E47
    Date: 2010
  2. By: Massimiliano Marcellino; Alberto Musso
    Abstract: 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 real-time 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 real-time output gap estimates for inflation forecasting both in the short term (one-quarter and one-year ahead) and the medium term (two-year and three-year 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, real-time data, euro area, inflation forecasts, real GDP forecasts, data revisions.
    JEL: E31 E37 E52 E58
    Date: 2010
  3. By: Hyeongwoo Kim; Nazif Durmaz
    Abstract: We evaluate the usefulness of bias-correction methods for autoregressive (AR) models in terms of out-of-sample 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 bias-uncorrected 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 bias-correction, 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: Small-Sample Bias, Grid Bootstrap, Recursive Mean Adjustment, Out-of-Sample Forecast
    JEL: C52 C53
    Date: 2010–05
  4. By: Chia-Lin Chang; Philip Hans Franses; Michael McAleer (University of Canterbury)
    Abstract: Macro-economic 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 non-replicable and is typically biased. In this paper we propose a methodology to analyze the qualities of combined non-replicable forecasts. One part of the methodology seeks to retrieve a replicable component from the non-replicable 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 non-replicable 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; non-replicable forecasts; expert’s intuition
    JEL: C53 C22 E27 E37
    Date: 2010–05–01
  5. By: Òscar Jordà; Malte Knüppel; Massimiliano Marcellino
    Abstract: Measuring and displaying uncertainty around path-forecasts, 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 non-parametric 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 path-forecast 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 real-time path-forecasts of growth and inflation.
    Keywords: path forecast, forecast uncertainty, simultaneous confidence region, Scheffé’s S-method,Mahalanobis distance, false discovery rate.
    JEL: C32 C52 C53
    Date: 2010
  6. By: Das, Rituparna
    Abstract: 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
    Date: 2010
  7. By: Lundholm, Michael (Dept. of Economics, Stockholm University)
    Abstract: Inflation forecasts made 1999-2005 by Sveriges Riksbank and Konjunkturinstitet of Swedish inflation rates 1999-2007 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
    Date: 2010–06–03
  8. By: Francesco Audrino (University of St. Gallen); Marcelo Cunha Medeiros (Department of Economics PUC-Rio)
    Abstract: In this paper we propose a smooth transition tree model for both the conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term 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: short-term interest rate, regression tree, smooth transition, conditional variance, bagging, asymptotic theory
    Date: 2010–03

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