nep-for New Economics Papers
on Forecasting
Issue of 2011‒09‒05
six papers chosen by
Rob J Hyndman
Monash University

  1. Bayesian estimation of bandwidths for a nonparametric regression model with a flexible error density By Xibin Zhang; Maxwell L. King; Han Lin Shang
  2. Forecast Optimality Tests in the Presence of Instabilities By Barbara Rossi; Tatevik Sekhposyan
  3. An Evaluation of the Forecasting Performance of Three Econometric Models for the Eurozone and the USA By David Mortimer Krainz
  4. Combination Schemes for Turning Point Predictions By Monica Billio; Roberto Casarin; Francesco Ravazzolo; Herman K. van Dijk
  5. Conclusive Evidence on the Benefits of Temporal Disaggregation to Improve the Precision of Time Series Model Forecasts By Ramirez, Octavio A.
  6. Exchange rate dynamics, expectations, and monetary policy By Chen, Qianying

  1. By: Xibin Zhang; Maxwell L. King; Han Lin Shang
    Abstract: We approximate the error density of a nonparametric regression model by a mixture of Gaussian densities with means being the individual error realizations and variance a constant parameter. We investigate the construction of a likelihood and posterior for bandwidth parameters under this Gaussian-component mixture density of errors in a nonparametric regression. A Markov chain Monte Carlo algorithm is presented to sample bandwidths for the kernel estimators of the regression function and error density. A simulation study shows that the proposed Gaussian-component mixture density of errors is clearly favored against wrong assumptions of the error density. We apply our sampling algorithm to a nonparametric regression model of the All Ordinaries daily return on the overnight FTSE and S&P 500 returns, where the error density is approximated by the proposed mixture density. With the estimated bandwidths, we estimate the density of the one-step-ahead point forecast of the All Ordinaries return, and therefore, a distribution-free value-at-risk is obtained. The proposed Gaussian component mixture density of regression errors is also validated through the nonparametric regression involved in the state-price density estimation proposed by Aït-Sahalia and Lo (1998).
    Keywords: Bayes factors, Gaussian-component mixture density, Markov chain Monte Carlo, state-price density, value-at-risk.
    JEL: C11 C14 C15 G15
    Date: 2011–08–22
  2. By: Barbara Rossi; Tatevik Sekhposyan
    Abstract: This paper proposes forecast optimality tests that can be used in unstable environments. They include tests for forecast unbiasedness, efficiency, encompassing, serial uncorrelation, and, in general, regression-based tests of forecasting ability. The proposed tests are applied to evaluate the rationality of the Federal Reserve Greenbook forecasts as well as a variety of survey-based private forecasts. In addition, we consider whether Money Market Services forecasts are rational. Our robust tests suggest more empirical evidence against forecast rationality than previously found but con…firm that the Federal Reserve has additional information about current and future states of the economy relative to market participants.
    Keywords: Forecasting, forecast optimality, regression-based tests of forecasting ability, Greenbook forecasts, survey forecasts, real-time data
    JEL: C22 C52 C53
    Date: 2011
  3. By: David Mortimer Krainz
    Abstract: This paper compares the forecasting performance of three different econometric models for the Eurozone and the USA: A vector auto regression (VAR), a Bayesian vector auto regression (BVAR), and a structural vector error correction model (SVEC). The forecast evaluation is based on 19 vintages of real time data for output, inflation rates, interest rates, the exchange rate and the money stock from the fourth quarter of 2004 until the first quarter of 2010. The oil price is used as the only exogenous variable in the model. Imposing a stringent set of long-run assumptions on the econometric model results in less accurate forecasts. The difference is significant for several variables and forecast horizons. Reducing the comparison to data from the pre-financial crisis period reduces the size of forecast errors but does not change the overall picture.
    Keywords: Eurozone, USA, econometric models, forecasting performance
    Date: 2011–08–30
  4. By: Monica Billio (University of Venice, Gretta Assoc. and School for Advanced Studies In Venice); Roberto Casarin (University of Venice, Gretta Assoc. and School for Advanced Studies In Venice); Francesco Ravazzolo (Norges Bank); Herman K. van Dijk (Erasmus University Rotterdam, VU University Amsterdam)
    Abstract: We propose new forecast combination schemes for predicting turning points of business cycles. The combination schemes deal with the forecasting performance of a given set of models and possibly providing better turning point predictions. We consider turning point predictions generated by autoregressive (AR) and Markov-Switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach to both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and Euro area business cycles.
    Keywords: Turning Points; Markov-switching; Forecast Combination; Bayesian Model Averaging
    JEL: C11 C15 C53 E37
    Date: 2011–08–22
  5. By: Ramirez, Octavio A.
    Abstract: Simulation methods are used to measure the expected differentials between the Mean Square Errors of the forecasts from models based on temporally disaggregated versus aggregated data. This allows for novel comparisons including long-order ARMA models, such as those expected with weekly data, under realistic conditions where the parameter values have to be estimated. The ambivalence of past empirical evidence on the benefits of disaggregation is addressed by analyzing four different economic time series for which relatively large sample sizes are available. Because of this, a sufficient number of predictions can be considered to obtain conclusive results from out-of-sample forecasting contests. The validity of the conventional method for inferring the order of the aggregated models is revised.
    Keywords: Data Aggregation, Efficient Forecasting, Research Methods/ Statistical Methods,
    Date: 2011–08
  6. By: Chen, Qianying
    Abstract: This paper re-investigates the implications of monetary policy rules on changes in exchange rate, in a risk-adjusted, uncovered interest parity model with unrestricted parameters, emphasizing the importance of modeling market expectations of monetary policy. I use consensus forecasts as a proxy for market expectations. The analysis on the Deutsche mark, Canadian dollar, Japanese yen, and the British pound relative to the U.S. dollar from 1979 to 2008 shows that, through the expectations of future monetary policy, Taylor rule fundamentals are able to forecast changes in the exchange rate, even over short-term horizons of less than two years. Furthermore, the market expectation formation processes of short-term interest rates change over time and differ across countries, which contributes to the time varying relationship between exchange rates and macroeconomic fundamentals, together with the time varying currency risk premia and exchange rate forecast errors. --
    Keywords: Exchange Rate,Monetary Policy,Expectation,Learning,VAR,Consensus Forecast
    JEL: F31 E52 D83 C32
    Date: 2011

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