New Forecasting Papers 2011-01-25

In this issue we have Using Large Data Sets to Forecast Sectoral Employment, On the Change in the Austrian Business Cycle, Performance forecasts in uncertain environments. The Predictive Information Content of External Imbalances for Exchange Rate Returns, and more.

  1. Using Large Data Sets to Forecast Sectoral Employment
    Date: 2011-01
    By: Rangan Gupta (University of Pretoria)
    Alain Kabundi (University of Johannesburg)
    Stephen M. Miller (University of Connecticut and University of Nevada, Las Vegas)
    Josine Uwilingiye (University of Johannesburg)
    We implement several Bayesian and classical models to forecast employment for eight sectors of the US economy. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of 143 additional monthly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two approaches — extracting common factors (principle components) in a factor-augmented vector autoregressive or vector error-correction, Bayesian factor-augmented vector autoregressive or vector error-correction models, or Bayesian shrinkage in a large-scale Bayesian vector autoregressive models. Using the period of January 1972 to December 1999 as the in-sample period and January 2000 to March 2009 as the out-of-sample horizon, we compare the forecast performance of the alternative models. Finally, we forecast out-of sample from April 2009 through March 2010, using the best forecasting model for each employment series. We find that factor augmented models, especially error-correction versions, generally prove the best in out-of-sample forecast performance, implying that in addition to macroeconomic variables, incorporating long-run relationships along with short-run dynamics play an important role in forecasting employment.
    Keywords: Sectoral Employment, Forecasting, Factor Augmented Models, Large-Scale BVAR models
    JEL: C32
  2. On the Change in the Austrian Business Cycle
    Date: 2011-01-12
    By: Sandra Bilek-Steindl (WIFO)
    This paper analyses the change in the Austrian business cycle over time using data back to 1954. The change in the cyclical pattern is captured using a nonlinear univariate structural time series model where the time of the break point is estimated. Results for GDP series suggest a break in the frequency of the cycle and in the parameter covering the variance of the disturbances of the cycle taking place in the mid 1970s and early 1980s, respectively. Using data for GDP components a break in these variables is found, too, but the timing of the break differs among the series. In a further step the paper assesses the relevance of these findings for forecasting purposes. It is shown that during certain periods the out-of-sample forecasting performance of GDP does improve when a break in one of the two parameters is explicitly modeled.
    Keywords: Structural time series models, Business cycles, Forecasting performance
  3. Performance forecasts in uncertain environments: Examining the predictive power of the VRIO-framework
    Date: 2010
    By: Powalla, Christian
    Bresser, Rudi K. F.
    Strategy tools are widely used in the practice of strategic management to yield a good solution with an acceptable problem-solving effort. This paper presents results of an experimental research project that assesses the practical effectiveness of a theory-based decision-making tool, the VRIO-Framework, in predicting the stock-market performance of different companies. The VRIO's predictive power is compared to the predictions derived from Analyst Ratings that are a widespread and commonly used tool in the decision-making context of this study. Our results suggest that the VRIO-Framework is a particularly effective forecasting tool whereas the power of Analyst Ratings is disputable. The results also provide support for the practical usefulness of resource-based theory. —
  4. The Predictive Information Content of External Imbalances for Exchange Rate Returns: How Much Is It Worth?
    Date: 2011
    By: Della Corte, P.
    Sarno, L.
    Sestieri, G.
    This paper examines the exchange rate predictability stemming from the equilibrium model of international financial adjustment developed by Gourinchas and Rey (2007). Using predictive variables that measure cyclical external imbalances for country pairs, we assess the ability of this model to forecast out-of-sample four major US dollar exchange rates using various economic criteria of model evaluation. The analysis shows that the model provides economic value to a risk-averse investor, delivering substantial utility gains when switching from a portfolio strategy based on the random walk benchmark to one that conditions on cyclical external imbalances.
    Keywords: foreign exchange; predictability; global imbalances; fundamentals.
    JEL: F31
  5. International Evidence on GFC-robust Forecasts for Risk Management under the Basel Accord
    Date: 2011-01
    By: Michael McAleer (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)
    Juan-Ángel Jiménez-Martín (Department of Quantitative Economics, Complutense University of Madrid)
    Teodosio Pérez-Amaral (Department of Quantitative Economics, Complutense University of Madrid)
    A risk management strategy that is designed to be robust to the Global Financial Crisis (GFC), in the sense of selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models, was proposed in McAleer et al. (2010c). The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models. Such a risk management strategy is robust to the GFC in the sense that, while maintaining the same risk management strategy before, during and after a financial crisis, it will lead to comparatively low daily capital charges and violation penalties for the entire period. This paper presents evidence to support the claim that the median point forecast of VaR is generally GFC-robust. We investigate the performance of a variety of single and combined VaR forecasts in terms of daily capital requirements and violation penalties under the Basel II Accord, as well as other criteria. In the empirical analysis, we choose several major indexes, namely French CAC, German DAX, US Dow Jones, UK FTSE100, Hong Kong Hang Seng, Spanish Ibex35, Japanese Nikkei, Swiss SMI and US S&P500. The GARCH, EGARCH, GJR and Riskmetrics models, as well as several other strategies, are used in the comparison. Backtesting is performed on each of these indexes using the Basel II Accord regulations for 2008-10 to examine the performance of the Median strategy in terms of the number of violations and daily capital charges, among other criteria. The Median is shown to be a profitable and safe strategy for risk management, both in calm and turbulent periods, as it provides a reasonable number of violations and daily capital charges. The Median also performs well when both total losses and the asymmetric linear tick loss function are considered
    Keywords: Median strategy, Value-at-Risk (VaR), daily capital charges, robust forecasts, violation penalties, optimizing strategy, aggressive risk management, conservative risk management, Basel II Accord, global financial crisis (GFC).
    JEL: G32
  6. Review of approaches to oil price modeling
    Date: 2010-10-19
    By: Yulia Raskina
    A huge size of an oil market and its relation to economic growth and global wealth distribution make the oil an unique commodity. Oil price prediction is associated with plans of development of states as well as firms. Jumps in oil prices influence world economy similarly to natural disasters of planet scale. There is no surprise that a lot of publications are devoted to research of an oil market, modeling and forecasting of oil prices. This paper gives main facts describing an oil market and oil prices behavior. We give a review of modern literature devoted to an oil market and consider main approaches to modeling and forecasting oil prices.
    Keywords: oil price, modeling, forecasting
    JEL: O13

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