nep-ets New Economics Papers
on Econometric Time Series
Issue of 2009‒01‒31
ten papers chosen by
Yong Yin
SUNY at Buffalo

  1. Multiple filtering devices for the estimation of cyclical DSGE models By Fabio Canova; Filippo Ferroni
  2. Discretization of Highly-Persistent Correlated AR(1) Shocks By Damba Lkhagvasuren; Ragchaasuren Galindev
  3. Local GMM Estimation of Time Series Models with Conditional Moment Restrictions By Nikolay Gospodinov; Taisuke Otsu
  4. Bootstrap Unit Root Tests in Models with GARCH(1,1) Errors By Nikolay Gospodinov; Ye Tao
  5. A Class of Simple Semiparametrically Efficient Rank-Based Unit Root Tests By Hallin, M.; Akker, R. van den; Werker, B.J.M.
  6. Modelling Seasonality An Extension of the HEGY Approach in the Presence of Two Structural Breaks By Ozlem Tasseven
  7. GARCH models with leverage effect : differences and similarities By María José Rodríguez; Esther Ruiz
  8. Localized Realized Volatility Modelling By Ying Chen; Wolfgang Härdle; Uta Pigorsch
  9. Panel Cointegration Testing in the Presence of a Time Trend By Bernd Droge; Deniz Dilan Karaman Örsal
  10. An Econometric Analysis of Some Models for Constructed Binary Time Series By Don Harding; Adrian Pagan

  1. By: Fabio Canova; Filippo Ferroni
    Abstract: We propose a method to estimate time invariant cyclical DSGE models using the information provided by a variety of filtering approaches. We treat data filtered with alternative procedures as contaminated proxy of the relevant model-based quantities and estimate structural and nonstructural parameters jointly using an unobservable component structure. We employ simulated data to illustrate the properties of the procedure and compare our estimates with those obtained when just one filter is used. We revisit the role of money in the transmission of monetary business cycles.
    Keywords: DSGE models, Filters, Structural estimation, Business cycles
    JEL: E32 C32
    Date: 2009–01
  2. By: Damba Lkhagvasuren (Concordia University); Ragchaasuren Galindev (Queens University Belfast)
    Abstract: The finite state Markov-Chain approximation method developed by Tauchen (1986) and Tauchen and Hussey (1991) is widely used in economics, finance and econometrics in solving for functional equations where state variables follow an autoregressive process. For highly persistent processes, the method requires a large number of discrete values for the state variables to produce close approximations which leads to an undesirable reduction in computational speed, especially in multidimensional case. This paper proposes an alternative method of discretizing vector autoregressions. The method works well as an approximation and its numerical efficiency applies to a wide range of the parameter space.
    Keywords: Finite State Markov-Chain Approximation, Transition Matrix, Numerical Methods, VAR,
    JEL: C15 C63
    Date: 2008–09
  3. By: Nikolay Gospodinov (Concordia University); Taisuke Otsu (Yale University)
    Abstract: This paper investigates statistical properties of the local GMM (LGMM) estimator for some time series models defined by conditional moment restrictions. First, we consider Markov processes with possible conditional heteroskedasticity of unknown form and establish the consistency, asymptotic normality, and semi-parametric efficiency of the estimator. Second, inspired by simulation results showing that the LGMM estimator has a significantly smaller bias than the OLS estimator, we undertake a higher-order asymptotic expansion and analyze the bias properties of the LGMM estimator. The structure of the asymptotic expansion of the LGMM estimator reveals an interesting contrast with the OLS estimator that helps to explain the bias reduction in the LGMM estimator. The practical importance of these findings is evaluated in terms of a bond and option pricing exercise based on a diffusion model for spot interest rate.
    Keywords: Conditional moment restrictions; Local GMM; Higher-order expansion; Conditional heteroskedasticity
    JEL: C13 C22 G12
    Date: 2008–12
  4. By: Nikolay Gospodinov (Concordia University); Ye Tao (Concordia University)
    Abstract: This paper proposes a bootstrap unit root test in models with GARCH(1,1) errors and establishes its asymptotic validity under mild moment and distributional restrictions. While the proposed bootstrap test for a unit root shares the power enhancing properties of its asymptotic counterpart (Ling and Li, 2003), it offers a number of important advantages. In particular, the bootstrap procedure does not require explicit estimation of nuisance parameters that enter the distribution of the test statistic and corrects the substantial size distortions of the asymptotic test that occur for strongly heteroskedastic processes. The simulation results demonstrate the excellent finite-sample properties of the bootstrap unit root test for a wide range of GARCH specifications.
    Keywords: Unit root test; GARCH; Bootstrap
    JEL: C12 C15 C22
    Date: 2009–01
  5. By: Hallin, M.; Akker, R. van den; Werker, B.J.M. (Tilburg University, Center for Economic Research)
    Abstract: AMS 1980 subject classification : 62G10 and 62G20.
    Keywords: Dickey-Fuller test;Local Asymptotic Normality.
    JEL: C12 C22
    Date: 2009
  6. By: Ozlem Tasseven (Okan University, banking and Finance Department, Istanbul Turkey)
    Abstract: In this paper the HEGY testing procedure (Hylleberg et al. 1990) of analyzing seasonal unit roots is tried to be re-examined by allowing for seasonal mean shifts with exogenous break points. Using some Monte Carlo experiments the distribution of the HEGY and the extended HEGY tests for seasonal unit roots subject to mean shifts and the small sample behavior of the test statistics have been investigated. Based on an empirical analysis upon the conventional money demand relationships in the Turkish economy, our results indicate that seasonal unit roots appear for the GDP deflator, real M2 and the expected inflation variables while seasonal unit roots at annual frequency seem to be disappear for the real M1 balances when the possible structural changes in one or more seasons at 1994 and 2001 crisis years have been taken into account.
    Keywords: HEGY Seasonal unit root test, Deterministic seasonality, Structural breaks, Money demand, Turkish economy
    JEL: C01 C15 C51 C88 E41
    Date: 2008–09
  7. By: María José Rodríguez; Esther Ruiz
    Abstract: In this paper, we compare the statistical properties of some of the most popular GARCH models with leverage e¤ect when their parameters satisfy the positivity, stationarity and nite fourth order moment restrictions. We show that the EGARCH speci cation is the most exible while the GJR model may have important limitations when restricted to have nite kurtosis. On the other hand, we show empirically that the conditional standard deviations estimated by the TGARCH and EGARCH models are almost identical and very similar to those estimated by the APARCH model. However, the estimates of the QGARCH and GJR models di¤er among them and with respect to the other three speci cations.
    JEL: C22
    Date: 2009–01
  8. By: Ying Chen; Wolfgang Härdle; Uta Pigorsch
    Abstract: With the recent availability of high-frequency Financial data the long range dependence of volatility regained researchers' interest and has lead to the consideration of long memory models for realized volatility. The long range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as e.g. one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern (see e.g. Diebold and Inoue, 2001; Mikosch and Starica, 2004b). In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. Using S&P500 data we find that our local approach outperforms long memory type models in terms of predictability.
    Date: 2009–01
  9. By: Bernd Droge; Deniz Dilan Karaman Örsal
    Abstract: The purpose of this paper is to propose a new likelihood-based panel cointegration test in the presence of a linear time trend in the data generating process. This new test is an extension of the likelihood ratio (LR) test of Saikkonen & Lütkepohl (2000) for trend-adjusted data to the panel data framework, and is called the panel SL test. The idea is first to take the average of the individual LR (trace) statistics over the cross-sections and then to standardize the test statistic with the appropriate asymptotic moments. Under the null hypothesis, this standardized statistic has a limiting normal distribution as the number of time periods (T) and the number of cross-sections (N) tend to infinity sequentially. In addition to the approximation based on asymptotic moments, a second approximation approach involving the moments from a vector autoregressive process of order one is also introduced. By means of a Monte Carlo study the finite sample size and size-adjusted power properties of the test are investigated. The test presents reasonable size with the increase in T and N, and has high power in small samples.
    Keywords: Panel Cointegration Test, Likelihood Ratio, Time Trend, Monte Carlo Study
    JEL: C33 C12 C15
    Date: 2009–01
  10. By: Don Harding (La Trobe University); Adrian Pagan (QUT)
    Abstract: Macroeconometric and financial researchers often use secondary or constructed binary random variables that differ in terms of their statistical properties from the primary random variables used in micro-econometric studies. One important difference between primary and secondary binary variables is that, while the former are, in many instances, independently distributed (i.d.), the latter are rarely i.d. We show how popular rules for constructing the binary states interact with the stochastic processes for of the variables they are constructed from, so that the binary states need to be treated as Markov processes. Consequently, one needs to recognize this when performing analyses with the binary variables, and it is not valid to adopt a model like static Probit which fails to recognize such dependence. Moreover, these binary variables are often censored, in that they are constructed in such a way as to result in sequences of them possessing the same sign. Such censoring imposes restrictions upon the DGP of the binary states and it creates difficulties if one tries to utilize a dynamic Probit model with them. Given this we describe methods for modeling with these variables that both respects their Markov process nature and which explicitly deals with any censoring constraints. An application is provided that investigates the relation between the business cycle and the yield spread.
    Keywords: Business cycle; binary variable, Markov process, Probit model, yield curve
    JEL: C22 C53 E32 E37
    Date: 2009–01–21

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