nep-for New Economics Papers
on Forecasting
Issue of 2007‒10‒06
seven papers chosen by
Rob J Hyndman
Monash University

  1. A Panel Data Approach to Economic Forecasting: The Bias-Corrected Average Forecast By João Victor Issler; Luiz Renato Lima
  2. Combining forecasts from nested models By Todd E. Clark; Michael W. McCracken
  3. A Multivariate Perspective for Modeling and Forecasting Inflation's Conditional Mean and Variance By Matteo Barigozzi; Marco Capasso
  4. Comparing Models for Forecasting the Yield Curve By Marco S. Matsumura; Ajax R. B. Moreira
  5. Information Misweighting and Stock Recommendations By Martinez, Jose Vicente
  6. Assessing Forecast Uncertainties in a VECX Model for Switzerland: An Exercise in Forecast Combination across Models and Observation Windows By Katrin Assenmacher-Wesche; M. Hashem Pesaran
  7. Exact prediction of inflation and unemployment in Germany By Kitov, Ivan

  1. By: João Victor Issler (EPGE/FGV); Luiz Renato Lima
    Date: 2007–09
  2. By: Todd E. Clark; Michael W. McCracken
    Abstract: Motivated by the common finding that linear autoregressive models forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but as the sample size grows, the data generating process converges to the restricted model. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive MSE-minimizing weights for combining the restricted and unrestricted forecasts. Monte Carlo and empirical analyses verify the practical effectiveness of our combination approach.
    Date: 2007
  3. By: Matteo Barigozzi; Marco Capasso
    Abstract: We test the importance of multivariate information for modelling and forecasting in- flation's conditional mean and variance. In the literature, the existence of inflation's conditional heteroskedasticity has been debated for years, as it seemed to appear only in some datasets and for some lag lengths. This phenomenon might be due to the fact that inflation depends on a linear combination of economy-wide dynamic common fac- tors, some of which are conditionally heteroskedastic and some are not. Modelling the conditional heteroskedasticity of the common factors can thus improve the forecasts of inflation's conditional mean and variance. Moreover, it allows to detect and predict con- ditional correlations between inflation and other macroeconomic variables, correlations that might be exploited when planning monetary policies. The Dynamic Factor GARCH (DF-GARCH) by Alessi et al. [2006] is used here to exploit the relations between inflation and the other macroeconomic variables for inflation fore- casting purposes. The DF-GARCH is a dynamic factor model as the one by Forni et al. [2005], with the addition of an equation for the evolution of static factors as in Giannone et al. [2004] and the assumption of heteroskedastic dynamic factors. When comparing the Dynamic Factor GARCH with univariate models and with the classical dynamic factor models, the DF-GARCH is able to provide better forecasts both of inflation and of its conditional variance.
    Keywords: Inflation, Factor Models, GARCH
    Date: 2007–10–01
  4. By: Marco S. Matsumura; Ajax R. B. Moreira
    Abstract: The evolution of the yields of different maturities is related and can be described by a reduced number of commom latent factors. Multifactor interest rate models of the finance literature, common factor models of the time series literature and others use this property. Each model has advantages and disadvantages, and it is an empirical matter to evaluate the performance of the approaches. This exercise compares 4 alternative models for the term structure using 3 different markets: the Brazilian domestic and sovereign market and the US market.
    Date: 2006–12
  5. By: Martinez, Jose Vicente (Swedish Institute for Financial Research)
    Abstract: I provide evidence that analysts whose earnings forecast revisions showed signs of greater exaggeration in the past make recommendation changes that lead to lower abnormal returns than their peers. Interpreting stock recommendations as a forecast of future abnormal returns, I show that this evidence is consistent with the hypothesis that analysts who typically exaggerate or overstate the weight of their private information when issuing forecasts also do so when making recommendations. I also show that past earnings forecast provide incremental information about analysts' recommending behavior beyond that contained in past recommendations.
    Keywords: Information misweighting; stock recommendations; earnings forecasts; financial analysts
    JEL: G14 G24 J44
    Date: 2007–07–15
  6. By: Katrin Assenmacher-Wesche (Swiss National Bank); M. Hashem Pesaran (Cambridge University, CIMF USC and IZA)
    Abstract: We investigate the effect of forecast uncertainty in a cointegrating vector error correction model for Switzerland. Forecast uncertainty is evaluated in three different dimensions. First, we investigate the effect on forecasting performance of averaging over forecasts from different models. Second, we look at different estimation windows. We find that averaging over estimation windows is at least as effective as averaging over different models and both complement each other. Third, we explore whether using weighting schemes from the machine learning literature improves the average forecast. Compared to equal weights the effect of the weighting scheme on forecast accuracy is small in our application.
    Keywords: Bayesian model averaging, choice of observation window, long-run structural vector autoregression
    JEL: C53 C32
    Date: 2007–09
  7. By: Kitov, Ivan
    Abstract: Potential links between inflation, (t), and unemployment, UE(t), in Germany have been examined. There exists a consistent (conventional) Phillips curve despite some changes in monetary policy. This Phillips curve is characterized by a negative relation between inflation and unemployment with the latter leading the former by one year: UE(t-1) = -1.50(t) + 0.116. Effectively, growing unemployment has resulted in decreasing inflation since 1971, i.e. for the period where GDP deflator observations are available. The relation between inflation and unemployment is statistically reliable with R2=0.86, where unemployment spans the range from 0.01 to 0.12 and inflation, as represented by GDP deflator, varies from -0.01 to 0.07. A linear and lagged relationship between inflation, unemployment and labor force has been also obtained for Germany. Changes in labor force level are leading unemployment and inflation by five and six year, respectively. Therefore this generalized relationship provides a natural prediction of inflation at a six-year horizon, as based upon current estimates of labor force level. The goodness-of-fit for the relationship is 0.87 for the period between 1971 and 2006, i.e. including the periods of high inflation and disinflation.
    Keywords: inflation; unemployment; labor force; prediction; Germany
    JEL: J64 E52 C53 E31
    Date: 2007–09–30

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