nep-for New Economics Papers
on Forecasting
Issue of 2007‒06‒30
nine papers chosen by
Rob J Hyndman
Monash University

  1. Forecast Combination with Entry and Exit of Experts By Carlos Capistrán; Allan Timmermann
  2. Disagreement and Biases in Inflation Expectations By Carlos Capistrán; Allan Timmermann
  3. How useful are historical data for forecasting the long-run equity return distribution? By John M Maheu; Thomas H McCurdy
  4. Volatility forecasting for crude oil futures By Marzo, Massimiliano; Zagaglia, Paolo
  5. Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious? By Carlos Capistrán
  6. Bayesian VARs with Large Panels By Banbura, Marta; Giannone, Domenico; Reichlin, Lucrezia
  7. A dynamic model to estimate the long-run trends in potential GDP By Albu, Lucian-Liviu
  8. Robust Bayesian Analysis of Loss Reserves Data Using the Generalized-t Distribution By Jennifer Chan; Boris Choy; Udi Makov
  9. Shadow sovereign ratings for unrated developing countries By Mohapatra, Sanket; De, Prabal; Ratha, Dilip

  1. By: Carlos Capistrán; Allan Timmermann
    Abstract: Combination of forecasts from survey data is complicated by the frequent entry and exit in real time of individual forecasters which renders conventional least squares regression approaches to estimation of the combination weights infeasible. We explore the consequences of this for a variety of forecast combination methods in common use and propose a new method that projects actual outcomes on the equal-weighted forecast as a means of adjusting for biases and noise in the underlying forecasts. Through simulations and an empirical application to inflation forecasts we show that the entry and exit of individual forecasters can have a large effect on the real time performance of conventional forecast combination methods. We also find that the proposed projection on the equal-weighted forecast works well in practice.
    Keywords: Forecasting, forecast combination, inflation, surveys
    JEL: C53 E37
    Date: 2006–09
  2. By: Carlos Capistrán; Allan Timmermann
    Abstract: Recent empirical work documents substantial disagreement in inflation expectations obtained from survey data. Furthermore, the extent of such disagreement varies systematically over time in a way that reflects the level and variance of current inflation. This paper offers a simple explanation for these facts based on asymmetries in the forecaster's costs of over- and under-predicting inflation. Our model implies biased forecasts with positive serial correlation in forecast errors and a cross-sectional dispersion that rises with the level and the variance of the inflation rate. It also implies that biases in forecaster's ranks should be preserved over time and that forecast errors at different horizons can be predicted through the spread between the short- and long-term variance of inflation. We find empirically that these patterns are present in inflation forecasts from the Survey of Professional Forecasters.
    Keywords: Inflation, Expectations, Forecasting, Asymmetric loss, Inflation dynamics
    JEL: C53 E31 E37
    Date: 2006–06
  3. By: John M Maheu; Thomas H McCurdy
    Abstract: We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higher-order moments of excess returns when forecasting the return distribution and its moments. The shape of the long-run distribution and the dynamics of the higher-order moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixed-length moving window. These differences in long-run forecasts have implications for many financial decisions, particularly for risk management and long-run investment decisions.
    Keywords: density forecasts, structural change, model risk, parameter uncertainty, Bayesian learning, market returns
    JEL: C51 C53 C11
    Date: 2007–06–28
  4. By: Marzo, Massimiliano (Department of Economics, Universit`a di Bologna); Zagaglia, Paolo (Dept. of Economics, Stockholm University)
    Abstract: This paper studies the forecasting properties of linear GARCH models for closing-day futures prices on crude oil, first position, traded in the New York Mercantile Exchange from January 1995 to November 2005. In order to account for fat tails in the empirical distribution of the series, we compare models based on the normal, Student’s t and Generalized Exponential distribution. We focus on out-of-sample predictability by ranking the models according to a large array of statistical loss functions. The results from the tests for predictive ability show that the GARCH-G model fares best for short horizons from one to three days ahead. For horizons from one week ahead, no superior model can be identified. We also consider out-of-sample loss functions based on Value-at-Risk that mimic portfolio managers and regulators’ preferences. EGARCH models display the best performance in this case.
    Keywords: GARCH models; kurtosis; oil prices; forecasting
    JEL: C22 G19
    Date: 2007–06–21
  5. By: Carlos Capistrán
    Abstract: Inflation forecasts of the Federal Reserve seem to have systematically under-predicted inflation from the fourth quarter of 1968 until Volcker's appointment as Chairman, and to systematically over-predict it afterwards until the second quarter of 1998. Furthermore, under quadratic loss, commercial forecasts seem to have information not contained in those forecasts. To investigate the cause of this apparent irrationality, this paper recovers the loss function implied by Federal Reserve's inflation forecasts. The results suggest that the cost of having inflation above an implicit time-varying target was larger than the cost of having inflation below it for the period since Volcker, and that the opposite was true for the pre-Volcker era. Once these asymmetries are taken into account, the Federal Reserve's inflation forecasts are found to be rational.
    Keywords: Inflation forecasts, Forecast evaluation, Monetary policy
    JEL: C53 E52
    Date: 2006–12
  6. By: Banbura, Marta; Giannone, Domenico; Reichlin, Lucrezia
    Abstract: This paper assesses the performance of Bayesian Vector Autoregression (BVAR) for models of different size. We consider standard specifications in the macroeconomic literature based on, respectively, three and eight variables and compare results with those obtained by larger models containing twenty or over one hundred conjunctural indicators. We first study forecasting accuracy and then perform a structural exercise focused on the effect of a monetary policy shock on the macroeconomy. Results show that BVARs estimated on the basis of hundred variables perform well in forecasting and are suitable for structural analysis.
    Keywords: Bayesian VAR; forecasting; large cross-sections; monetary VAR
    JEL: C11 C13 C33 C53
    Date: 2007–06
  7. By: Albu, Lucian-Liviu
    Abstract: To estimate long-run growth based on the so-called potential GDP became a constant preoccupation among economists. However, one remaining problem in every long-run growth model is to estimate a persistent trend in labour productivity outside of it, in order to avoid the implicit circular relationship between actual productivity growth and potential level of production. Coming from recent literature on natural rate of unemployment estimation we used a specific methodology in order to estimate NAIRU in case of post-communist economies and based on it to evaluate the potential GDP. Taking into account that the “classic” Hodrick-Prescott method is in fact equivalent to an interpolation procedure, we used in our experiment other three filters demonstrating very similar output. Moreover, we conceived a simple autonomous model in order to estimate the growth of a so-called “pure” productivity independently from the actual level of employment and to compare its dynamics with that of natural rate of unemployment.
    Keywords: natural rate of unemployment; potential GDP; pure productivity
    JEL: P24 C13 E24 D58
    Date: 2006
  8. By: Jennifer Chan (University of Sydney); Boris Choy (Department of Mathematical Sciences, University of Technology, Sydney); Udi Makov (University of Haifa)
    Abstract: This paper presents a Bayesian approach using Markov chain Monte Carlo methods and the generalized-t (GT) distribution to predict loss reserves for the insurance companies. Existing models and methods cannot cope with irregular and extreme claims and hence do not offer an accurate prediction of loss reserves. To develop a more robust model for irregular claims, this paper extends the conventional normal error distribution to the GT distribution which nests several heavytailed distributions including the Student-t and exponential power distributions. It is shown that the GT distribution can be expressed as a scale mixture of uniforms (SMU) distribution which facilitates model implementation and detection of outliers by using mixing parameters. Different models for the mean function, including the log-ANOVA, log-ANCOVA, state space and threshold models, are adopted to analyze real loss reserves data. Finally, the best model is selected according to the deviance information criterion (DIC).
    Keywords: Bayesian approach; state space model; threshold model; scale mixtures of uniform distribution; device information criterion
    Date: 2007–05–01
  9. By: Mohapatra, Sanket; De, Prabal; Ratha, Dilip
    Abstract: The authors attempt to predict sovereign ratings for developing countries that do not have risk ratings from agencies such as Fitch, Moody ' s, and Standard and Poor ' s. Ratings affect capital flows to developing countries through international bond, loan, and equity markets. Sovereign rating also acts as a ceiling for the foreign currency rating of sub-sovereign borrowers. As of the end of 2006, however, only 86 developing countries have been rated by the rating agencies. Of these, 15 countries have not been rated since 2004. Nearly 70 developing countries have never been rated. The results indicate that the unrated countries are not always at the bottom of the rating spectrum. Several unrated poor countries appear to have a " B " or higher rating, in a similar range as the emerging market economies with capital market access. Drawing on the literature, the analysis presents a stylized relationship between borrowi ng costs and the credit rating of sovereign bonds. The launch spread rises as the credit rating deteriorates, registering a sharp rise at the investment grade threshold. Based on these findings, a case can be made in favor of helping poor countries obtain credit ratings not only for sovereign borrowing, but for sub-sovereign entities ' access to international debt and equity capital. The rating model, along with the stylized relationship between spreads and ratings can be useful for securitization and other financial structures, and for leveraging official aid for improving borrowing terms in poor countries.
    Keywords: Economic Theory & Research,Country Strategy & Performance,Financial Intermediation,External Debt,Inequality
    Date: 2007–06–01

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