Forecasting papers 2009-10-21

In this issue we have Macroeconomic Forecasting and Structural Change, Comparing forecast accuracy: A Monte Carlo investigation, Non-linear relation between industrial production and business surveys data, and Nowcasting Euro Area Economic Activity in Real-Time: The Role of Confidence Indicator.

  1. Macroeconomic Forecasting and Structural Change
    Date: 2009
    By: Antonello D'Agostino
    Luca Gambetti
    Domenico Giannone
    The aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts. We produce real time out-of-sample forecasts for inflation, the unemployment rate and the interest rate using a Time-Varying Coe±cients VAR with Stochastic Volatility (TV-VAR) for the US. The model generates accurate predictions for the three variables. In particular for inflation the TV-VAR outperforms, in terms of mean square forecast error, all the competing models: fixed coefficients VARs, Time-Varying ARs and the naaive random walk model. These results are also shown to hold over the most recent period in which it has been hard to forecast inflation.
    Keywords: Forecasting, infation, stochastic Volatility, time varying vector autoregression.
    JEL: C32
  2. Comparing forecast accuracy: A Monte Carlo investigation
    Date: 2009-09
    By: Fabio Busetti (Bank of Italy)
    Juri Marcucci (Bank of Italy)
    Giovanni Veronese (Bank of Italy)
    The size and power properties of several tests of equal Mean Square Prediction Error (MSPE) and of Forecast Encompassing (FE) are evaluated, using Monte Carlo simulations, in the context of dynamic regressions. For nested models, the F-type test of forecast encompassing proposed by Clark and McCracken (2001) displays overall the best properties. However its power advantage tends to become smaller as the prediction sample increases and for multi-step ahead predictions; in these cases a standard FE test based on Gaussian critical values becomes relatively more attractive. The ranking among the tests remains broadly unaltered for one-step and multi-step ahead predictions, for partially misspecified models and for highly persistent data. A similar setup is then used to analyze the case of non-nested models. Again it is found that FE tests have a significantly better performance than tests of equal MSPE for discriminating bet! ween correct and misspecified models. An empirical application evaluates the predictive ability of nested and non-nested models for GDP in Italy and the euro-area.
    Keywords: Forecast encompassing, Model evaluation, Nested models, Non-nested models, Equal predictive ability
    JEL: C12
  3. Non-linear relation between industrial production and business surveys data
    Date: 2009-09
    By: Giancarlo Bruno (ISAE – Institute for Studies and Economic Analyses)
    In this paper I compare different models, a linear and a non-linear one, for forecasting industrial production by means of some related indicators. I claim that the difficulties associated with the correct identification of a non-linear model could be a possible cause of the often observed worse performance of non-linear models with respect to linear ones observed in the empirical literature. To cope with this issue I use a non-linear non-parametric model. The results are promising, as the forecasting performance shows a clear improvement over the linear parametric model.
    Keywords: Forecasting, Business Surveys, Non-linear time-series models, Non-parametric models.
    JEL: C52
  4. Nowcasting Euro Area Economic Activity in Real-Time: The Role of Confidence Indicator
    Date: 2009
    By: Domenico Giannone
    Lucrezia Reichlin
    Saverio Simonelli
    This paper assesses the role of surveys for the early estimates of GDP in the euro area in a model-based automated procedures which exploits the timeliness of their release. The analysis is conducted using both an historical evaluation and a real time case study on the current conjuncture.
    Keywords: Forecasting, factor model, real time data, large data sets, survey

Taken from the NEP-FOR mailing list edited by Rob Hyndman.