nep-for New Economics Papers
on Forecasting
Issue of 2010‒07‒03
four papers chosen by
Rob J Hyndman
Monash University

  1. Sveriges Riksbank's Inflation Interval Forecasts 1999-2005 By Lundholm, Michael
  2. Ten Things We Should Know About Time Series By Michael McAleer; Les Oxley
  3. The yield curve and the prediction on the business cycle: a VAR analysis for the European Union By Cinquegrana, Giuseppe; Sarno, Domenico
  4. Realized Volatility Risk By David E. Allen; Michael McAleer; Marcel Scharth

  1. By: Lundholm, Michael (Dept. of Economics, Stockholm University)
    Abstract: Are Sveriges Riksbank's inflation (CPI and KPIX) interval forecasts calibrated in the sense that the intervals cover realised inflation with the stated ex ante coverage probabilities 50, 75 and 90 percent? In total 150 interval forecast 1999:Q2-2005:Q2 are assessed for CPI and KPIX. The main result is that the forecast uncertainty is understated, but there are substantial differences between individual forecast origins and inflation measures.
    Keywords: Inflation; forecast; interval forecast; forecast uncertainty
    JEL: C53 E31 E37
    Date: 2010–06–23
  2. By: Michael McAleer (University of Canterbury); Les Oxley (University of Canterbury)
    Abstract: Time series data affect many aspects of our lives. This paper highlights ten things we should all know about time series, namely: a good working knowledge of econometrics and statistics, an awareness of measurement errors, testing for zero frequency, seasonal and periodic unit roots, analysing fractionally integrated and long memory processes, estimating VARFIMA models, using and interpreting cointegrating models carefully, choosing sensibly among univariate conditional, stochastic and realized volatility models, not confusing thresholds, asymmetry and leverage, not underestimating the complexity of multivariate volatility models, and thinking carefully about forecasting models and expertise.
    Keywords: Unit roots; fractional integration; long memory; VARFIMA; cointegration; volatility; thresholds; asymmetry; leverage; forecasting models and expertise
    JEL: C22 C32
    Date: 2010–06–01
  3. By: Cinquegrana, Giuseppe; Sarno, Domenico
    Abstract: The literature on the yield curve deals with the capacity to predict the future inflation and the future real growth from the term structure of the interest rates. The aim of the paper is to verify this predictive power of the yield curve for the European Union at 16 countries in the 1995-2008 years. With this regard we propose two VAR models. The former is derived from the standard approach, the later is an extended version considering explicitly the macroeconomic effects of the risk premium. We propose the estimates of the models and their out-of-sample forecasts through both the European Union GDP (Gross Domestic Product) quarterly series and the European Union IPI (Industrial Production Index) monthly series. We show that the our extended model performs better than the standard model and that the out-of-sample forecasts of the IPI monthly series are better than ones of the GDP quarterly series. Moreover the out-of-sample exercises seems us very useful because they show the crowding out arising from Lehman Brother’s unexpected crash and the becoming next fine tuning process.
    Keywords: yield curve; monetary policy; business cycle; risk premium; real growth
    JEL: E43 E52 E44 E47
    Date: 2010–01
  4. By: David E. Allen; Michael McAleer (University of Canterbury); Marcel Scharth
    Abstract: In this paper we document that realized variation measures constructed from high- frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation measures are nearly gaussian, this unpredictability brings considerably more uncertainty to the empirically relevant ex ante distribution of returns. Carefully modeling this volatility risk is fundamental. We propose a dually asymmetric realized volatility (DARV) model, which incorporates the important fact that realized volatility series are systematically more volatile in high volatility periods. Returns in this framework display time varying volatility, skewness and kurtosis. We provide a detailed account of the empirical advantages of the model using data on the S&P 500 index and eight other indexes and stocks.
    Keywords: Realized volatility; volatility of volatility; volatility risk; value-at-risk; forecasting; conditional heteroskedasticity
    Date: 2010–05–01

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