nep-for New Economics Papers
on Forecasting
Issue of 2008‒12‒07
six papers chosen by
Rob J Hyndman
Monash University

  1. The Taylor rule and forecast intervals for exchange rates By Jian Wang; Jason J. Wu
  2. Neural Network Models for Inflation Forecasting: An Appraisal By Ali Choudhary; Adnan Haider
  3. Uncertainty and disagreement in economic forecasting By Stefania D'Amico; Athanasios Orphanides
  4. Comparing and evaluating Bayesian predictive distributions of asset returns. By John Geweke; Gianni Amisano
  5. Priors from DSGE Models for Dynamic Factor Analysis By Gregor Bäurle
  6. Individual Expectations and Aggregate Behavior in Learning to Forecast Experiments By Cars Hommes; Thomas Lux

  1. By: Jian Wang; Jason J. Wu
    Abstract: This paper attacks the Meese-Rogoff (exchange rate disconnect) puzzle from a different perspective: out-of-sample interval forecasting. Most studies in the literature focus on point forecasts. In this paper, we apply Robust Semi-parametric (RS) interval forecasting to a group of Taylor rule models. Forecast intervals for twelve OECD exchange rates are generated and modified tests of Giacomini and White (2006) are conducted to compare the performance of Taylor rule models and the random walk. Our contribution is twofold. First, we find that in general, Taylor rule models generate tighter forecast intervals than the random walk, given that their intervals cover out-of-sample exchange rate realizations equally well. This result is more pronounced at longer horizons. Our results suggest a connection between exchange rates and economic fundamentals: economic variables contain information useful in forecasting the distributions of exchange rates. The benchmark Taylor rule model is also found to perform betterthan the monetary and PPP models. Second, the inference framework proposed in this paper for forecast-interval evaluation, can be applied in a broader context, such as inflation forecasting, not just to the models and interval forecasting methods used in this paper.
    Keywords: Foreign exchange ; Forecasting ; Taylor's rule ; Econometric models - Evaluation
    Date: 2008
  2. By: Ali Choudhary (University of Surrey and State Bank of Pakistan); Adnan Haider (State Bank of Pakistan)
    Abstract: We assess the power of artificial neural network models as forecasting tools for monthly inflation rates for 28 OECD countries. For short out-of-sample forecasting horizons, we find that, on average, for 45% of the countries the ANN models were a superior predictor while the AR1 model performed better for 21%. Furthermore, arithmetic combinations of several ANN models can also serve as a credible tool for forecasting inflation.
    Keywords: Artificial Neural Networks; Forecasting; Inflation
    JEL: C51 C52 C53 E31 E37
    Date: 2008–11
  3. By: Stefania D'Amico; Athanasios Orphanides
    Abstract: Using the probabilistic responses from the Survey of Professional Forecasters, we study the evolution of uncertainty and disagreement associated with inflation forecasts in the United States since 1968. We compare and contrast alternative measures summarizing the distributions of mean forecasts and forecast uncertainty across individuals at an approximate one-year-ahead horizon. In light of the heterogeneity in individual uncertainty reflected in the survey responses, we provide quarterly estimates for both average uncertainty and disagreement regarding uncertainty. We propose direct estimation of parametric distributions characterizing the uncertainty across individuals in a manner that mitigates errors associated with rounding and approximation of responses when individual uncertainty is small. Our results indicate that higher average expected inflation is associated with both higher average inflation uncertainty and greater disagreement about the inflation outlook. Disagreement about the mean forecast, however, may be a weak proxy for forecast uncertainty. We also examine the relationship of these measures with the term premia embedded in the term-structure of interest rates.
    Date: 2008
  4. By: John Geweke (Departments of Statistics and Economics, University of Iowa, 430 N. Clinton St., Iowa City, IA 52242-2020, USA.); Gianni Amisano (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: Bayesian inference in a time series model provides exact, out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative models of asset returns applied to daily S&P 500 returns from 1976 through 2005. The comparison exercise uses predictive likelihoods and is inherently Bayesian. The evaluation exercise uses the probability integral transform and is inherently frequentist. The illustration shows that the two approaches can be complementary, each identifying strengths and weaknesses in models that are not evident using the other. JEL Classification: C11, C53.
    Keywords: Forecasting, GARCH, inverse probability transform, Markov mixture, predictive likelihood, S&P 500 returns, stochastic volatility.
    Date: 2008–11
  5. By: Gregor Bäurle
    Abstract: We propose a method to incorporate information from Dynamic Stochastic General Equilibrium (DSGE) models into Dynamic Factor Analysis. The method combines a procedure previously applied for Bayesian Vector Autoregressions and a Gibbs Sampling approach for Dynamic Factor Models. The factors in the model are rotated such that they can be interpreted as variables from a DSGE model. In contrast to standard Dynamic Factor Analysis, a direct economic interpretation of the factors is given. We evaluate the forecast performance of the model with respect to the amount of information from the DSGE model included in the estimation. We conclude that using prior information from a standard New Keynesian DSGE model improves the forecast performance. We also analyze the impact of identified monetary shocks on both the factors and selected series. The interpretation of the factors as variables from the DSGE model allows us to use an identification scheme which is directly linked to the DSGE model. The responses of the factors in our application resemble responses found using VARs. However, there are deviations from standard results when looking at the responses of specific series to common shocks.
    Keywords: Dynamic Factor Model; DSGE Model; Bayesian Analysis; Forecasting; Transmission of Shocks
    JEL: C11 C32 E0
    Date: 2008–08
  6. By: Cars Hommes; Thomas Lux
    Abstract: Models with heterogeneous interacting agents explain macro phenomena through interactions at the micro level. We propose genetic algorithms as a model for individual expectations to explain aggregate market phenomena. The model explains all stylized facts observed in aggregate price fluctuations and individual forecasting behaviour in recent learning to forecast laboratory experiments with human subjects (Hommes et al. 2007), simultaneously and across different treatments
    Keywords: Learning, heterogeneous expectations, genetic algorithms, experimental economics
    JEL: C91 C92 D83 D84 E3
    Date: 2008–11

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