nep-ets New Economics Papers
on Econometric Time Series
Issue of 2008‒11‒11
seven 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. Testing for Smooth Transition Nonlinearity in Adjustments of Cointegrating Systems By Nedeljkovic, Milan
  3. Combination of Forecast Methods Using Encompassing Tests. An Algorithm-Based Procedure By Costantini, Mauro; Pappalardo, Carmine
  4. Stationarity and the term structure of interest rates: a characterisation of stationary and unit root yield curves By Clive G. Bowsher; Roland Meeks
  5. Stochastic Volatility: Origins and Overview By Neil Shephard; Torben Andersen
  6. Dependence Structures in Chinese and U.S. Financial Markets -- A Time-varying Conditional Copula Approach By Hu, Jian
  7. A Study on "Spurious Long Memory in Nonlinear Time Series Models" By Kuswanto, Heri; Sibbertsen, Philipp

  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: Nedeljkovic, Milan (Department of Economics,University of Warwick)
    Abstract: This paper studies testing for the presence of smooth transition nonlinearity in adjustment parameters of the vector error correction model. We specify the generalized model with multiple cointegrating vectors and different transition functions across equations. Given that the nonlinear model is highly complex, this paper proposes an optimal LM test based only on estimation of the linear model. The null asymptotic distribution is derived using empirical process theory and since the transition parameters of the model cannot be identified under the null hypothesis bootstrap procedures are used to approximate the limit. Monte Carlo simulations indicate a good performance of the test.
    Keywords: Nonlinearity ; Cointegration ; Empirical process theory ; Bootstrap
    JEL: C12 C32
    Date: 2008
  3. By: Costantini, Mauro (Department of Economics, University of Vienna BWZ, Vienna, Austria); Pappalardo, Carmine (Institute for Studies and Economic Analysis (ISAE), Rome, Italy)
    Abstract: This paper proposes a strategy to increase the efficiency of forecast combining methods. Given the availability of a wide range of forecasting models for the same variable of interest, our goal is to apply combining methods to a restricted set of models. To this aim, an algorithm procedure based on a widely used encompassing test (Harvey, Leybourne, Newbold, 1998) is developed. First, forecasting models are ranked according to a measure of predictive accuracy (RMSFE) and, in a consecutive step, each prediction is chosen for combining only if it is not encompassed by the competing models. To assess the robustness of this procedure, an empirical application to Italian monthly industrial production using ISAE short-term forecasting models is provided.
    Keywords: Combining forecasts, Econometric models, Evaluating forecasts, Models selection, Time series
    JEL: C32 C53
    Date: 2008–11
  4. By: Clive G. Bowsher; Roland Meeks
    Abstract: The nature of yield curve dynamics and the determinants of the integration order of yields are investigated using a benchmark economy in which the logarithmic expectations theory holds and the regularity condition of a limiting yield and limiting term premium is satisfied. By considering a zero-coupon yield curve with a complete term structure of maturities, a linear vector autoregressive process is constructed that provides an arbitrarily accurate moving average representation of the complete yield curve as its cross-sectional dimension (n) goes to infinity. We use this to prove the following novel results. First, any I(2) component vanishes owing to the almost sure (a.s.) convergence of the innovations to yields, vt(n), as n. Second, the yield curve is stationary if and only if nvt(n) converges a.s., or equivalently the innovations to log discount bond prices converge a.s.; otherwise yields are I(1). A necessary condition for either stationarity or the absence of arbitrage is that the limiting yield is constant over time. Since the time-varying component of term premia is small in various fixed-income markets, these results provide insight into the critical determinants of the stationarity properties of the term structure.
    Keywords: Econometric models ; Interest rates
    Date: 2008
  5. By: Neil Shephard (Oxford-Man Institute and Department of Economics, University of Oxford); Torben Andersen (Kellogg School of Management, Northwestern University and CREATES, University of Aarhus)
    Date: 2008–05–03
  6. By: Hu, Jian
    Abstract: In this paper, we use a Time-Varying Conditional Copula approach (TVCC) to model Chinese and U.S. stock markets‚ dependence structures with other financial markets. The AR-GARCH-t model is used to examine the marginals, while Normal and Generalized Joe-Clayton copula models are employed to analyze the joint distributions. In this pairwise analysis, both constant and time-varying conditional dependence parameters are estimated by a two-step maximum likelihood method. A comparative analysis of dependence structures in Chinese versus U.S. stock markets is also provided. There are three main findings: First, the time-varying-dependence model does not always perform better than constant-dependence model. This result has not previously been reported in the literature. Second, although previous research extensively reports that the lower tail dependence between stock markets tends to be higher than the upper tail dependence, we find a counterexample where the upper tail dependence is much higher than the lower tail dependence in some short periods. Last, Chinese financial market is relatively separate from other international financial markets in contrast to the U.S. market. The tail dependence with other financial markets is much lower in China than in the U.S.
    Keywords: AR-GARCH-t model; Time-varying conditional copula; Dependence structure; Stock market
    JEL: C51 G15 F36 P52
    Date: 2008–10–31
  7. By: Kuswanto, Heri; Sibbertsen, Philipp
    Abstract: This paper discusses the existence of spurious long memory in common nonlinear time series models, namely Markov switching and threshold models. We describe the asymptotic behavior of the process in terms of autocovariance and autocorrelation function and support the theoretical evidences by providing Monte Carlo simulation. The existence of long memory in these nonlinear processes is induced by the nature of the process in certain conditions. In addition, GPH estimator itself introduces bias.
    Keywords: long memory, nonlinear time series, regime switching
    JEL: C12 C22
    Date: 2008–11

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