nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒04‒04
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Empirical Likelihood Block Bootstrapping By Allen, Jason; Gregory, Allan W.; Shimotsu, Katsumi
  2. Bootstrap Union Tests for Unit Roots in the Presence of Nonstationary Volatility By Smeekes Stephan; Taylor A. M. Robert
  3. Testing non-linear dependence in the hedge fund industry By Javier Mencía
  4. Volatility models with innovations from new maximum entropy densities at work By Fischer, Matthias; Gao, Yang; Herrmann, Klaus
  5. A Note on Kalman Filter Approach To Solution of Rational Expectations Models By Marco M. Sorge
  6. Identification problems in ESTAR models and a new model By Donauer, Stefanie; Heinen, Florian; Sibbertsen, Philipp

  1. By: Allen, Jason; Gregory, Allan W.; Shimotsu, Katsumi
    Abstract: Monte Carlo evidence has made it clear that asymptotic tests based on generalized method of moments (GMM) estimation have disappointing size. The problem is exacerbated when the moment conditions are serially correlated. Several block bootstrap techniques have been proposed to correct the problem, including Hall and Horowitz (1996) and Inoue and Shintani (2006). We propose an empirical likelihood block bootstrap procedure to improve inference where models are characterized by nonlinear moment conditions that are serially correlated of possibly infinite order. Combining the ideas of Kitamura (1997) and Brown and Newey (2002), the parameters of a model are initially estimated by GMM which are then used to compute the empirical likelihood probability weights of the blocks of moment conditions. The probability weights serve as the multinomial distribution used in resampling. The first-order asymptotic validity of the proposed procedure is proven, and a series of Monte Carlo experiments show it may improve test sizes over conventional block bootstrapping.
    Keywords: generalized methods of moments, empirical likelihood, block bootstrap
    JEL: C14 C22
    Date: 2010–03
  2. By: Smeekes Stephan; Taylor A. M. Robert (METEOR)
    Abstract: We provide a joint treatment of three major issues that surround testing for a unit root in practice: uncertainty as to whether or not a linear deterministic trend is present in the data, uncertainty as to whether the initial condition of the process is (asymptotically) negligible or not, and the possible presence of nonstationary volatility in the data. Harvey, Leybourne and Taylor (2010, Journal of Econometrics, forthcoming) propose decision rules based on a four-way union of rejections of QD and OLS detrended tests, both with and without allowing for a linear trend, to deal with the first two problems. However, in the presence of nonstationary volatility these test statistics have limit distributions which depend on the form of the volatility process, making tests based on the standard asymptotic critical values invalid. We construct bootstrap versions of the four-way union of rejections test, which, by employing the wild bootstrap, are shown to be asymptotically valid in the presence of nonstationary volatility. These bootstrap union tests therefore allow for a joint treatment of all three of the aforementioned problems.
    Keywords: econometrics;
    Date: 2010
  3. By: Javier Mencía (Banco de España)
    Abstract: This paper proposes a parsimonious approach to test non-linear dependence on the conditional mean and variance of hedge funds with respect to several market factors. My approach introduces non-linear dependence by means of empirically relevant polynomial functions of the factors. For comparison purposes, I also consider multifactor extensions of tests based on piecewise linear alternatives. I apply these tests to a database of monthly returns on 1,071 hedge funds. I find that non-linear dependence on the mean is highly sensitive to the factors that I consider. However, I obtain a much stronger evidence of nonlinear dependence on the conditional variance.
    Keywords: Generalised Hyperbolic Distribution, Correlation, Asymmetry, Multifactor Models
    JEL: C12 G11 C32 C22
    Date: 2010–03
  4. By: Fischer, Matthias; Gao, Yang; Herrmann, Klaus
    Abstract: Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions which result from the assumption of conditionally normal error distributions. In contrast, Bollerslev (1987) and several follow-ups provided evidence that starting with leptokurtic and possibly skewed (conditional) error distributions will achieve better results. Parallel to these exible but to some extend arbitrary chosen parametric distributions, recent years saw a rise in suggestions for maximum entropy distributions (e.g. Rockinger and Jondeau, 2002, Park and Bera, 2009 or Fischer and Herrmann, 2010). Within this contribution we provide a comprehensive comparison between both different ME densities and their parametric competitors within different generalized GARCH models such as APARCH and GJR-GARCH. --
    Keywords: GARCH,APARCH,Entropy density,Skewness,Kurtosis
    Date: 2010
  5. By: Marco M. Sorge
    Abstract: In this note, a class of nonlinear dynamic models under rational expectations is studied. A particular solution is found using a model reference adaptive technique via an extended Kalman filtering algorithm, for which initial conditions knowledge only is required.
    Keywords: Nonlinear dynamic systems; Rational Expectations; Extended Kalman Filter
    JEL: C5 C6
    Date: 2010–03
  6. By: Donauer, Stefanie; Heinen, Florian; Sibbertsen, Philipp
    Abstract: In ESTAR models it is usually difficult to determine parameter estimates, as it can be observed in the literature. We show that the phenomena of getting strongly biased estimators is a consequence of the so-called identification problem, the problem of properly distinguishing the transition function in relation to extreme parameter combinations. This happens in particular for either very small or very large values of the error term variance. Furthermore, we introduce a new alternative model - the T-STAR model - which has similar properties as the ESTAR model but reduces the effects of the identification problem. We also derive a linearity and a unit root test for this model.
    Keywords: Nonlinearities, Smooth transition, Linearity testing, Unit root testing, Real exchange rates
    JEL: C12 C22 C52
    Date: 2010–03

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