nep-for New Economics Papers
on Forecasting
Issue of 2005‒12‒01
six papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting the yield curve in a data-rich environment - a no-arbitrage factor-augmented VAR approach By Emanuel Mönch
  2. Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases By Domenico Giannone; Lucrezia Reichlin; David Small
  4. Term structure estimation with survey data on interest rate forecasts By Don H. Kim; Athanasios Orphanides
  5. Approximately normal tests for equal predictive accuracy in nested models By Todd E. Clark; Kenneth D. West
  6. Dynamics of State Price Densities By Wolfgang Härdle; Zdenek Hlavka

  1. By: Emanuel Mönch (Humboldt University, School of Business and Economics, Institute of Economic Policy, Spandauer Str. 1, 10178 Berlin, Germany)
    Abstract: This paper suggests a term structure model which parsimoniously exploits a broad macroeconomic information set. The model does not incorporate latent yield curve factors, but instead uses the common components of a large number of macroeconomic variables and the short rate as explanatory factors. Precisely, an affine term structure model with parameter restrictions implied by no-arbitrage is added to a Factor-Augmented Vector Autoregression (FAVAR). The model is found to strongly outperform different benchmark models in out-of-sample yield forecasts, reducing root mean squared forecast errors relative to the random walk up to 50% for short and around 20% for long maturities.
    Keywords: Affine term structure models, Yield curve, Dynamic factor models, FAVAR.
    JEL: C13 C32 E43 E44 E52
    Date: 2005–11
  2. By: Domenico Giannone; Lucrezia Reichlin; David Small
    Abstract: This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing "news" on the basis of an evolving conditioning information set. The marginal contribution is then split into what is due to timeliness of information and what is due to economic content. We find that the Federal Reserve Bank of Philadelphia surveys have a large marginal impact on the nowcast of both inflation variables and real variables, and this effect is larger than that of the Employment Report. When we control for timeliness of the releases, the effect of hard data becomes sizeable. Prices and quantities affect the precision of the estimates of inflation, while GDP is affected only by real variables and interest rates.
    Keywords: Economic forecasting ; Gross domestic product ; Inflation (Finance)
    Date: 2005
  3. By: Roberto Tatiwa Ferreira; Luiz Ivan de Melo Castelar
    Abstract: The present study uses linear and non-linear diffusion index models to produce one-stepahead forecast of quarterly Brazilian GDP growth rate. Diffusion index models are like dynamic factors models. The non-linear diffusion index models used in this work are not only parsimonious ones, but also they try to capture economic cycles using for this goal a Threshold diffusion index model and a Markov-Switching diffusion index model.
    JEL: E32 E37
    Date: 2005
  4. By: Don H. Kim; Athanasios Orphanides
    Abstract: The estimation of dynamic no-arbitrage term structure models with a flexible specification of the market price of risk is beset by a severe small-sample problem arising from the highly persistent nature of interest rates. We propose using survey forecasts of a short-term interest rate as additional input to the estimation to overcome the problem. The three-factor pure-Gaussian model thus estimated with the U.S. Treasury term structure for the 1990-2003 period generates a stable estimate of the expected path of the short rate, reproduces the well-known stylized patterns in the expectations hypothesis tests, and captures some of the short-run variations in the survey forecast of the changes in longer-term interest rates.
    Date: 2005
  5. By: Todd E. Clark; Kenneth D. West
    Abstract: Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for this noise. We propose applying standard methods (West (1996)) to test whether the adjusted mean squared error difference is zero. We refer to nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size. Simulation evidence supports our recommended procedure.
    Date: 2005
  6. By: Wolfgang Härdle; Zdenek Hlavka
    Abstract: State price densities (SPD) are an important element in applied quantitative finance. In a Black-Scholes model they are lognormal distributions with constant volatility parameter. In practice volatility changes and the distribution deviates from log-normality. We estimate SPDs using EUREX option data on the DAX index via a nonparametric estimator of the second derivative of the (European) call price function. The estimator is constrained so as to satisfy no-arbitrage constraints and it corrects for intraday covariance structure. Given a low dimensional representation of this SPD we study its dynamic for the years 1995–2003. We calculate a prediction corridor for the DAX for a 45 day forecast. The proposed algorithm is simple, it allows calculation of future volatility and can be applied to hedging exotic options.
    Keywords: option pricing, state price density estimation, nonlinear least squares, confidence intervals
    JEL: C13 C14 G12
    Date: 2005–04

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