nep-ets New Economics Papers
on Econometric Time Series
Issue of 2004‒12‒20
fourteen papers chosen by
Yong Yin
SUNY at Buffalo

  1. Estimating Long Memory in Volatility By Clifford Hurvich; Eric Moulines; Philippe Soulier
  2. Asymptotics for Duration-Driven Long Range Dependent Processes By Mengchen Hsieh; Clifford Hurvich; Philippe Soulier
  3. Semiparametric Estimation of Fractional Cointegrating Subspaces By Willa Chen; Clifford Hurvich
  4. Integration of Statistical Methods and Judgment for Time Series By JS Armstrong; Fred Collopy
  5. Predictive Regressions: A Reduced-Bias Estimation Method By Yakov Amihud; Clifford Hurvich
  6. Multipower Variation and Stochastic Volatility By Ole E. Barndorff-Nielsen; Neil Shephard
  7. Hypothesis Testing in Predictive Regressions By Yakov Amihud; Clifford Hurvich; Yi Wang
  8. Ergodicity, mixing, and existence of moments of a class of Markov models with applications to GARCH and ACD models By Meitz, Mika; Saikkonen, Pentti
  9. Nonlinear Forecasting Analysis Using Diffusion Indexes: An Application to Japan By Mototsugu Shintani
  10. Regular and Modified Kernel-Based Estimators of Integrated Variance: The Case with Independent Noise By OLE E. BARNDORFF-NIELSEN; PETER REINHARD HANSEN; ASGER LUNDE; NEIL SHEPHARD
  11. Semiparametric Causality Tests Using the Policy Propensity Score By Joshua D. Angrist; Guido M. Kuersteiner
  12. A smooth permanent surge process By González Gómez, Andrés
  13. Forecasting Time-Series with Correlated Seasonality By Phillip Gould; Anne B. Koehler; Farshid Vahid-Araghi; Ralph D. Snyder; J. Keith Ord; Rob J. Hyndman
  14. ‘Weak’ trends for inference and forecasting in finite samples By Guillaume Chevillon

  1. By: Clifford Hurvich (New York University USA); Eric Moulines (ENST, Paris, France); Philippe Soulier (Universite Paris X, France)
    Abstract: We consider semiparametric estimation of the memory parameter in a model which includes as special cases both the long-memory stochastic volatility (LMSV) and fractionally integrated exponential GARCH (FIEGARCH) models. Under our general model the logarithms of the squared returns can be decomposed into the sum of a long-memory signal and a white noise. We consider periodogram-based estimators using a local Whittle criterion function. We allow the optional inclusion of an additional term to account for possible correlation between the signal and noise processes, as would occur in the FIEGARCH model. We also allow for potential nonstationarity in volatility, by allowing the signal process to have a memory parameter d^* >= 1/2. We show that the local Whittle estimator is considtent for d^* in (0,1). We also show that the local Whittle estimator is asymptotically normal for d^* in (0,3/4) and asymptotically recovers the optimal semiparametric rate of convergence for this problem. In particular, if the spectral density of the short memory component of the signal is sufficiently smooth, a convergence rate of n^{2/5-\delta} for d^* in (0,3/4) can be attained, where n is the sample size and \delta > 0 is arbitrarily small. This represents a strong improvement over the performance of existing semiparametric estimators of persistence in volatility. We also prove that the standard Gaussian semiparametric estimator is asymptotically normal if d^*=0. This yields a test for long memory in volatility.
    Keywords: LMSV, FIEGARCH
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2004–12–14
  2. By: Mengchen Hsieh (New York University); Clifford Hurvich (New York University); Philippe Soulier (Universite Paris X)
    Abstract: We consider processes with second order long range dependence resulting from heavy tailed durations. We refer to this phenomenon as duration- driven long range dependence (DDLRD), as opposed to the more widely studied linear long range dependence based on fractional differencing of an $iid$ process. We consider in detail two specific processes having DDLRD, originally presented in Taqqu and Levy (1986), and Parke (1999). For these processes, we obtain the limiting distribution of suitably standardized discrete Fourier transforms (DFTs) and sample autocovariances. At low frequencies, the standardized DFTs converge to a stable law, as do the standardized autocovariances at fixed lags. Finite collections of standardized autocovariances at a fixed set of lags converge to a degenerate distribution. The standardized DFTs at high frequencies converge to a Gaussian law. Our asymptotic results are strikingly similar for the two DDLRD processes studied. We calibrate our asymptotic results with a simulation study which also investigates the properties of the semiparametric log periodogram regression estimator of the memory parameter.
    Keywords: Long Memory; Structural Change
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2004–12–15
  3. By: Willa Chen (Texas A&M University); Clifford Hurvich (New York University)
    Abstract: We consider a common components model for multivariate fractional cointegration, in which the s>=1 components have different memory parameters. The cointegrating rank is allowed to exceed 1. The true cointegrating vectors can be decomposed into orthogonal fractional cointegrating subspaces such that vectors from distinct subspaces yield cointegrating errors with distinct memory parameters, denoted by d_k for k=1,...,s. We estimate each cointegrating subsspace separately using appropriate sets of eigenvectors of an averaged periodogram matrix of tapered, differenced observations. The averaging uses the first m Fourier frequencies, with m fixed. We will show that any vector in the k'th estimated coingetraging subspace is, with high probability, close to the k'th true cointegrating subspace, in the sense that the angle between the estimated cointegrating vector and the true cointegrating subspace converges in probability to zero. The angle is O_p(n^{- \alpha_k}), where n is the sample size and \alpha_k is the shortest distance between the memory parameters corresponding to the given and adjacent subspaces. We show that the cointegrating residuals corresponding to an estimated cointegrating vector can be used to obtain a consistent and asymptotically normal estimate of the memory parameter for the given cointegrating subspace, using a univariate Gaussian semiparametric estimator with a bandwidth that tends to \infty more slowly than n. We also show how these memory parameter estimates can be used to test for fractional cointegration and to consistently identify the cointegrating subspaces.
    Keywords: Fractional Cointegration; Long Memory; Tapering; Periodogram
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2004–12–15
  4. By: JS Armstrong (The Wharton School - University of Pennsylvania); Fred Collopy (Case Western Reserve University)
    Abstract: We consider how judgment and statistical methods should be integrated for time-series forecasting. Our review of published empirical research identified 47 studies, all but four published since 1985. Five procedures were identified: revising judgment; combining forecasts; revising extrapolations; rule-based forecasting; and econometric forecasting. This literature suggests that integration generally improves accuracy when the experts have domain knowledge and when significant trends are involved. Integration is valuable to the extent that judgments are used as inputs to the statistical methods, that they contain additional relevant information, and that the integration scheme is well structured. The choice of an integration approach can have a substantial impact on the accuracy of the resulting forecasts. Integration harms accuracy when judgment is biased or its use is unstructured. Equal-weights combining should be regarded as the benchmark and it is especially appropriate where series have high uncertainty or high instability. When the historical data involve high uncertainty or high instability, we recommend revising judgment, revising extrapolations, or combining. When good domain knowledge is available for the future as well as for the past, we recommend rule- based forecasting or econometric methods.
    Keywords: statistical methods, statistics, time series, forecasting, empirical research
    JEL: A
    Date: 2004–12–10
  5. By: Yakov Amihud (New York University); Clifford Hurvich (New York University)
    Abstract: Standard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable; see Stambaugh (1999) for the single-regressor model. This paper proposes a direct and convenient method to obtain reduced-bias estimators for single and multiple regressor models by employing an augmented regression, adding a proxy for the errors in the autoregressive model. We derive bias expressions for both the ordinary least squares and our reduced-bias estimated coefficients. For the standard errors of the estimated predictive coefficients we develop a heuristic estimator which performs well in simulations, for both the single-predictor model and an important specification of the multiple- predictor model. The effectiveness of our method is demonstrated by simulations and by empirical estimates of common predictive models in finance. Our empirical results show that some of the predictive variables that were significant under ordinary least squares become insignificant under our estimation procedure.
    Keywords: Stock Returns; Dividend Yields; Autoregressive Models
    JEL: C1 C2 C3 C4 C5 C8
    Date: 2004–12–15
  6. By: Ole E. Barndorff-Nielsen; Neil Shephard
    Abstract: In this brief note we review some of our recent results on the use of high frequency financial data to estimate objects like integrated variance in stochastic volatility models. Interesting issues include multipower variation, jumps and market microstructure effects.
    Date: 2004
  7. By: Yakov Amihud (New York University); Clifford Hurvich (New York University); Yi Wang (New York University)
    Abstract: We propose a new hypothesis testing method for multi-predictor regressions with finite samples, where the dependent variable is regressed on lagged variables that are autoregressive. It is based on the augmented regressiom method (ARM; Amihud and Hurvich (2004)), which produces reduced-bias coefficients and is easy to implement. The method's usefulness is demonstrated by simulations and by an empirical example, where stock returns are predicted by dividend yield and by bond yield spread. For single-predictor regressions, we show that the ARM outperforms bootstrapping and that the ARM performs better than Lewellen's (2003) method in many situations.
    Keywords: Augmented Regression Method (ARM); Bootstrapping; Hypothesis Testing
    JEL: G
    Date: 2004–12–15
  8. By: Meitz, Mika (Dept. of Economic Statistics, Stockholm School of Economics); Saikkonen, Pentti (Dept. of Statistics, University of Helsinki)
    Abstract: This paper studies a class of Markov models which consist of two components. Typically, one of the components is observable and the other is unobservable or 'hidden'. Conditions under which (a form of) geometric ergodicity of the unobservable component is inherited by the joint process formed of the two components are given. This immediately implies the existence of initial values such that the joint process is strictly stationary and beta-mixing. In addition to this, conditions for beta-mixing and existence of moments for the joint process are also provided in the case of (possibly) nonstationary initial values. All these results are applied to a general model which includes as special cases various first order generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional duration (ACD) models with possibly complicated nonlinear structures.
    Keywords: -
    JEL: C22
    Date: 2004–10–07
  9. By: Mototsugu Shintani (Department of Economics, Vanderbilt University)
    Abstract: This paper extends the diffusion index (DI) forecast approach of Stock and Watson (1998, 2002) to the case of possibly nonlinear dynamic factor models. When the number of series is large, a two-step procedure based on the principal components method is useful since it allows the wide variety of the nonlinearity in the factors. The factors extracted from a large Japanese data suggest some evidence of nonlinear structure. Furthermore, both the linear and nonlinear DI forecasts in Japan outperform traditional time series forecasts, while the linear DI forecast, in most cases, performs as well as the nonlinear DI forecast.
    Keywords: Diffusion Index, Dynamic Factor Model, Nonlinearity, Prediction
    JEL: F31 F41
    Date: 2003–10
    Abstract: We consider kernel-based estimators of integrated variances in the presence of independent market microstructure effects. We derive the bias and variance properties for all regular kernel-based estimators and derive a lower bound for their asymptotic variance. Further we show that the subsample-based estimator is closely related to a Bartlett-type kernel estimator. The small difference between the two estimators due to end effects, turns out to be key for the consistency of the subsampling estimator. This observation leads us to a modified class of kernel-based estimators, which are also consistent. We study the efficiency of our new kernel-based procedure. We show that optimal modified kernel-based estimator converges to the integrated variance at the optimal rate, m^1/4, where m is the number of intraday returns.
    JEL: C13 C22
    Date: 2004
  11. By: Joshua D. Angrist; Guido M. Kuersteiner
    Abstract: Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a semi-parametric test for causality in models linking a binary treatment or policy variable with unobserved potential outcomes. The procedure is semiparametric in the sense that we model the process determining treatment -- the policy propensity score -- but leave the model for outcomes unspecified. This general approach is motivated by the notion that we typically have better prior information about the policy determination process than about the macro-economy. A conceptual innovation is that we adapt the cross-sectional potential outcomes framework to a time series setting. This leads to a generalized definition of Sims (1980) causality. We also develop a test for full conditional independence, in contrast with the usual focus on mean independence. Our approach is illustrated using data from the Romer and Romer (1989) study of the relationship between the Federal reserve's monetary policy and output.
    JEL: C14 C22 E52
    Date: 2004–12
  12. By: González Gómez, Andrés (Dept. of Economic Statistics, Stockholm School of Economics)
    Abstract: In this paper we introduce the Smooth Permanent Surge [SPS] model. The model is an integrated non lineal moving average process with possibly unit roots in the moving average coefficients. The process nests the Stochastic Permanent Break [STOPBREAK] process by Engle and Smith (1999) and in a limiting case it converges to Threshold Integrated Moving Average [TIMA] models by Gonzalo and Martinez (2003). A test of SPS against STOPBREAK process is presented. Additionally, we introduce a new test for testing SPS process against the random walk. The small sample properties of these tests are <p> investigated by Monte Carlo experiments. An application to the stock markets is presented.
    Keywords: Linearity test; Monte Carlo testing; Smooth transitions; Moving Averages Models; Permanent Shock; Transitory Shocks.
    JEL: C12 C15 C22 C51 C52
    Date: 2004–12–07
  13. By: Phillip Gould; Anne B. Koehler; Farshid Vahid-Araghi; Ralph D. Snyder; J. Keith Ord; Rob J. Hyndman
    Abstract: A new approach to forecasting seasonal data is proposed where seasonal terms can be updated using the most recent relevant information. It was developed to handle features encountered in hourly electricity load data and daily hospital admissions data. The associated state space model is estimated with methods adapted from exponential smoothing, although the Kalman filter may also be used. It nests existing seasonal models and outperforms them over a range of prediction horizons on the data. The approach is likely to be useful in a wide range of applications involving both high and low frequency data.
    Keywords: Exponential smoothing; Holt-Winters; Seasonality; Structural time series model
    JEL: C22
    Date: 2004–12
  14. By: Guillaume Chevillon (Observatoire Français des Conjonctures Économiques)
    Date: 2004

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