nep-for New Economics Papers
on Forecasting
Issue of 2007‒09‒09
five papers chosen by
Rob J Hyndman
Monash University

  1. A VAR Framework for Forecasting Hong Kong'S Output and Inflation By Hans Genberg; Jian Chang
  2. Long term regional forecasting with spatial equation systems By Wolfgang Polasek; Richard Sellner; Wolfgang Schwarzbauer
  3. Methods to Elicit Forecasts from Groups: Delphi and Prediction Markets Compared By Green, Kesten C.; Armstrong, J. Scott; Graefe, Andreas
  4. Can investors profit from banks’ stock recommendations? Evidence for the German DAX index By Pierdzioch, Christian; Kempa, Bernd; Hendricks, Torben
  5. A Real Activity Index for Mainland China By Li-gang Liu; Wenlang Zhang; Jimmy Shek

  1. By: Hans Genberg (Research Department, Hong Kong Monetary Authority); Jian Chang (Research Department, Hong Kong Monetary Authority)
    Abstract: This paper develops a multivariate time series model to forecast output growth and inflation in the Hong Kong economy. We illustrate the steps involved in designing and building a vector autoregression (VAR) forecasting model, and consider three types of VAR models, including unrestricted, Bayesian and conditional VARs. Our findings suggest that the Bayesian VAR framework incorporating external influences provide a useful tool to produce more accurate forecasts relative to the unrestricted VARs and univariate time series models, and conditional forecasts have the potential to further improve upon the Bayesian models. In particular, a six-variable Bayesian VAR including domestic output, domestic inflation, domestic investment, world GDP, the best lending rate, and import prices appears to generate good out-of-sample forecasts results.
    Keywords: VAR and BVAR models, conditional forecasts, forecasting, model evaluation
    JEL: C52 C53 E37
    Date: 2007–03
  2. By: Wolfgang Polasek (Institute for Advanced Studies,Vienna, Austria and The Rimini Centre for Economic Analysis, Rimini, Italy); Richard Sellner (Institute for Advanced Studies,Vienna, Austria); Wolfgang Schwarzbauer (Institute for Advanced Studies,Vienna, Austria)
    Abstract: Long-term predictions with a system of dynamic panel models can have tricky properties since the time dimension in regional (cross) sectional models is usually short. This paper describes the possible approaches to make long-term-ahead forecast based on a dynamic panel set, where the dependent variable is a cross-sectional vector of growth rates. Since the variance of the forecasts will depend on number of updating steps, we compare the forecasts behavior of a aggregated and a disaggregated updating procedure. The cross section of the panel data can be modeled by a spatial AR (SAR) or Durbin model, including heteroscedasticity. Since the forecasts are non-linear functions of the model parameters we show what MCMC based approach will produce the best results. We demonstrate the approach by a example where we have to predict 20 years ahead of regional growth in 99 Austrian regions in a space-time dependent system of equations.Creation-Date: 200707
    Date: 2007–07
  3. By: Green, Kesten C.; Armstrong, J. Scott; Graefe, Andreas
    Abstract: Traditional groups meetings are an inefficient and ineffective method for making forecasts and decisions. We compare two structured alternatives to traditional meetings: the Delphi technique and prediction markets. Delphi is relatively simple and cheap to implement and has been adopted for diverse applications in business and government since its origins in the 1950s. It can be used for nearly any forecasting, estimation, or decision making problem not barred by complexity or ignorance. While prediction markets were used more than a century ago, their popularity waned until more recent times. As a consequence there is less evidence on their validity. Prediction markets need many participants. They need clear outcomes in order to determine participants’ pay-offs. Even so, relating their knowledge to market prices is not intuitive to everyone and constructing contracts that will provide a useful forecast may not be possible for some problems. It is difficult to maintain confidentiality with markets and they are vulnerable to manipulation. Delphi is designed to reveal panelists’ knowledge and opinions via their forecasts and the reasoning they provide. This format allows testing of knowledge and learning by panelists as they refine their forecasts. Such a process does not happen explicitly in prediction markets and may not happen at all. The reasoning provided as an output of the Delphi process is likely to be reassuring to forecast users who are uncomfortable with the “black box” nature of prediction markets. We consider that, half a century after its original development, Delphi is greatly under-utilized.
    Keywords: accuracy; forecasting methods; groups; judgment; meetings; structure
    JEL: C88 D84 C42 D82 C49 C44 D81 D83
    Date: 2007–08–31
  4. By: Pierdzioch, Christian; Kempa, Bernd; Hendricks, Torben
    Abstract: We find that banks’ buy and sell recommendations only have a minor effect on the out-of-sample predictability of daily stock returns and the market-timing ability of an investor trading in real time in the German DAX30 stock index. Banks’ stock recommendations may improve the performance of simple trading rules in real time. These improvements, however, are in general small and sensitive to the model-selection criterion being used by an investor to set up a forecasting model for stock returns.
    Keywords: Forecasting stock returns; trading rules; buy and sell recommendations by banks
    JEL: G11 E44 C53
    Date: 2007–01–09
  5. By: Li-gang Liu (Research Department, Hong Kong Monetary Authority); Wenlang Zhang (Research Department, Hong Kong Monetary Authority); Jimmy Shek (Research Department, Hong Kong Monetary Authority)
    Abstract: This paper develops a composite real activity index (RAI) using eight monthly activity indicators for the Mainland economy based on the methodology of the Conference Board. The RAI appears to be able to track the Mainland GDP growth quite well. The results from a logit regression indicate that the RAI can correctly predict the next movement of the quarterly GDP growth rate with a probability of up to 68 percent. In addition, the RAI can beat a random walk process when used to conduct forecasts. Compared with indexes constructed using alternative methods, the RAI has economic properties that are easier to interpret. While the predictability of the RAI can be enhanced further with better data, it is a useful leading indicator to help monitor the momentum of the aggregate activities of the Mainland economy before the official release of the quarterly GDP data.
    Keywords: Real Activity Index, China, Dynamic Factor Model
    JEL: C43 C53
    Date: 2007–05

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