nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒11‒25
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. An easy test for two stationary long processes being uncorrelated via AR approximations By WANG , Shin-Huei; HSIAO, Cheng
  2. Practical Issues in the Analysis of Univariate GARCH Models By Eric Zivot
  3. The Calibration of Probabilistic Economic Forecasts By John Galbraith; Simon van Norden
  4. Long Memory versus Structural Breaks in Modeling and Forecasting Realized Volatility By Kyongwook Choi; Wei-Choun Yu; Eric Zivot

  1. By: WANG , Shin-Huei (Université catholique de Louvain (UCL). Center for Operations Research and Econometrics (CORE)); HSIAO, Cheng
    Abstract: This paper proposes an easy test for two stationary autoregressive fractionally integrated moving average (ARFIMA) processes being uncorrelated via AR approximations. We prove that an ARFIMA process can be approximated well by an autoregressive (AR) model and establish the theoretical foundation of Haugh's (1976) statistics to test two ARFIMA processes being uncorrelated. Using AIC or Mallow's Cp criterion as a guide, we demonstrate through Monte Carlo studies that a lower order AR(k) model is sufficient to prewhiten an ARFIMA process and the Haugh test statistics perform very well in finite sample. We illustrate the methodology by investigating the independence between the volatility of two daily nominal dollar exchange rates-Euro and Japanese Yen and find that there exists "strongly simultaneous correlation" between the volatilities of Euro and Yen within 25 days.
    Keywords: forecasting, long memory process, structural break.
    JEL: C22 C53
    Date: 2008–08
  2. By: Eric Zivot (Department of Economics, University of Washington)
    Abstract: This paper gives a tour through the empirical analysis of univariate GARCH models for financial time series with stops along the way to discuss various practical issues associated with model specification, estimation, diagnostic evaluation and forecasting.
    Date: 2008–04
  3. By: John Galbraith; Simon van Norden
    Abstract: A probabilistic forecast is the estimated probability with which a future event will satisfy a specified criterion. One interesting feature of such forecasts is their calibration, or the match between predicted probabilities and actual outcome probabilities. Calibration has been evaluated in the past by grouping probability forecasts into discrete categories. Here we show that we can do so without discrete groupings; the kernel estimators that we use produce efficiency gains and smooth estimated curves relating predicted and actual probabilities. We use such estimates to evaluate the empirical evidence on calibration error in a number of economic applications including recession and inflation prediction, using both forecasts made and stored in real time and pseudoforecasts made using the data vintage available at the forecast date. We evaluate outcomes using both first-release outcome measures as well as later, thoroughly-revised data. We find strong evidence of incorrect calibration in professional forecasts of recessions and inflation. We also present evidence of asymmetries in the performance of inflation forecasts based on real-time output gaps. <P>Une prévision probabiliste représente la probabilité qu’un événement futur satisfasse une condition donnée. Un des aspects intéressants de ces prévisions est leur calibration, c’est-à-dire l’appariement entre les probabilités prédites et les probabilités réalisées. Dans le passé, la calibration a été évaluée en regroupant des probabilités de prévisions en catégories distinctes. Nous proposons d’utiliser des estimateurs à noyaux, qui sont plus efficaces et qui estiment une relation lisse entre les probabilités prédites et réalisées. Nous nous servons de ces estimations pour évaluer l’importance empirique des erreurs de calibration dans plusieurs pratiques économiques, telles que la prévision de récessions et de l’inflation. Pour ce faire, nous utilisons des prévisions historiques, ainsi que des pseudoprévisions effectuées à l’aide de données telles qu’elles étaient au moment de la prévision. Nous analysons les résultats en utilisant autant des estimations préliminaires que des estimations tardives, ces dernières incorporant parfois des révisions importantes. Nous trouvons une forte évidence empirique d’une calibration erronée des prévisions professionnelles de récession et d’inflation. Nous présentons aussi une évidence d’asymétries dans la performance des prévisions d’inflation basées sur des estimations des écarts de la production en temps réel.
    Keywords: calibration, probability forecast, real-time data, inflation, recession, calibration, probabilités de prévisions, données « en temps réel », inflation, récession
    Date: 2008–11–01
  4. By: Kyongwook Choi (Department of Economics, The University of Seoul,); Wei-Choun Yu (Economics and Finance Department, Winona State University); Eric Zivot (Department of Economics, University of Washington)
    Abstract: We explore the possibility of structural breaks in realized volatility with observed long-memory properties for the daily Deutschemark/Dollar, Yen/Dollar and Yen/Deutschemark spot exchange rate realized volatility. We find that structural breaks can partly explain the persistence of realized volatility. We propose a VAR-RV-Break model that provides superior predictive ability compared to most of the forecasting models when the future break is known. With unknown break dates and sizes, we find that the VAR-RV-I(d) long memory model, however, is a very robust forecasting method even when the true financial volatility series are generated by structural breaks.
    Date: 2008–09

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