nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒09‒03
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Detecting Periods of Exuberance: A Look at the Role of Aggregation with an Application to House Prices By Pavlidis, Efthymios; Martinez-Garcia, Enrique; Grossman, Valerie
  2. Semiparametric GARCH via Bayesian model averaging By Wilson Ye Chen; Richard H. Gerlach
  3. Residual-based diagnostic tests for noninvertible ARMA models By Nyholm, Juho
  4. In-fill Asymptotic Theory for Structural Break Point in Autoregression: A Unified Theory By Jiang, Liang; Wang, Xiaohu; Yu, Jun
  5. Asymptotic Theory for Estimating Drift Parameters in the Fractional Vasicek Model By Xiao, Weilin; Yu, Jun
  6. Combining Sharp and Smooth Transitions in Volatility Dynamics: a Fuzzy Regime Approach By Giampiero M. Gallo; Edoardo Otranto
  7. Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing By Pötscher, Benedikt M.; Preinerstorfer, David

  1. By: Pavlidis, Efthymios (Lancaster University); Martinez-Garcia, Enrique (Federal Reserve Bank of Dallas); Grossman, Valerie (Federal Reserve Bank of Dallas)
    Abstract: The recently developed SADF and GSADF unit root tests of Phillips et al. (2011) and Phillips et al. (2015) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on actual housing data for both U.S. metropolitan and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2015), can perform substantially better than univariate tests applied to aggregated series.
    JEL: C12 C22 G12 R30 R31
    Date: 2017–08–01
  2. By: Wilson Ye Chen; Richard H. Gerlach
    Abstract: As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast (news impact function). We propose a new class of functional coefficient semiparametric volatility models where the news impact function is allowed to be any smooth function, and study its ability to estimate volatilities compared to the well known parametric proposals, in both a simulation study and an empirical study with real financial data. We estimate the news impact function using a Bayesian model averaging approach, implemented via a carefully developed Markov chain Monte Carlo (MCMC) sampling algorithm. Using simulations we show that our flexible semiparametric model is able to learn the shape of the news impact function from the observed data. When applied to real financial time series, our new model suggests that the news impact functions are significantly different in shapes for different asset types, but are similar for the assets of the same type.
    Date: 2017–08
  3. By: Nyholm, Juho
    Abstract: This paper proposes two residual-based diagnostic tests for noninvertible ARMA models. The tests are analogous to the portmanteau tests developed by Box and Pierce (1970), Ljung and Box (1978) and McLeod and Li (1983) in the conventional invertible case. We derive the asymptotic chi-squared distribution for the tests and study the size and power properties in a Monte Carlo simulation study. An empirical application employing financial time series data points out the usefulness of noninvertible ARMA model in analyzing stock returns and the use of the proposed test statistics.
    Keywords: Non-Gaussian time series; noninvertible ARMA model; model selection
    JEL: C22 C52
    Date: 2017–08
  4. By: Jiang, Liang (School of Economics, Singapore Management University); Wang, Xiaohu (The Chinese University of Hong Kong); Yu, Jun (School of Economics, Singapore Management University)
    Abstract: This paper obtains the exact distribution of the maximum likelihood estimator of structural break point in the Ornstein-Uhlenbeck process when a continuous record is available. The exact distribution is asymmetric, tri-modal, dependent on the initial condition. These three properties are also found in the finite sam- ple distribution of the least squares (LS) estimator of structural break point in autoregressive (AR) models. Motivated by these observations, the paper then develops an in-fill asymptotic theory for the LS estimator of structural break point in the AR(1) coefficient. The in-fill asymptotic distribution is also asymmetric, tri-modal, dependent on the initial condition, and delivers excellent approximations to the finite sample distribution. Unlike the long-span asymptotic theory, which depends on the underlying AR root and hence is tailor-made but is only available in a rather limited number of cases, the in-fill asymptotic theory is continuous in the underlying roots. Monte Carlo studies show that the in-fill asymptotic theory performs better than the long-span asymptotic theory for cases where the long-span theory is available and performs very well for cases where no long-span theory is available.
    Keywords: Asymmetry; Bias; Exact distribution; Long-span asymptotics; In-fill asymptotics; Trimodality.
    JEL: C11 C46
    Date: 2017–05–19
  5. By: Xiao, Weilin (School of Management, Zhejiang University); Yu, Jun (School of Economics, Singapore Management University)
    Abstract: This paper develops the asymptotic theory for estimators of two parameters in the drift function in the fractional Vasicek model when a continuous record of observations is available. The fractional Vasicek model is assumed to be driven by the fractional Brownian motion with a known Hurst parameter greater than or equal to one half. It is shown that the asymptotic theory for the persistent parameter depends critically on its sign, corresponding asymptotically to the stationary case, the explosive case, and the null recurrent case. In all three cases, the least squares method is considered. When the persistent parameter is positive, the estimate method of Hu and Nualart (2010) is also considered. The strong consistency and the asymptotic distribution are obtained in all three cases.
    Keywords: Least squares; Fractional Vasicek model; Stationary process; Explosive process; Null recurrent; Strong consistency; Asymptotic distribution
    JEL: C15 C22 G32
    Date: 2017–04–27
  6. By: Giampiero M. Gallo (Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti", Università di Firenze); Edoardo Otranto (Dipartimento di Economia and CRENoS, Università di Messina)
    Abstract: Volatility in financial markets is characterized by alternating persistent turmoil and quiet periods, but also by a slowly-varying average level. This slow moving component keeps open the question of whether some of its features are better represented as abrupt or smooth changes between local averages of volatility. We provide a new class of models with a set of parameters subject to abrupt changes in regime (Markov Switching -- MS) and another set subject to smooth transition (ST) changes. These models capture the possibility that regimes may overlap with one another ( fuzzy ). The empirical application is carried out on the volatility of four US indices. It shows that the flexibility of the new model allows for a better overall performance over either MS or ST, and provides a Local Average Volatility measure as a parametric estimation of the low frequency component.
    Keywords: Volatility modeling, Volatility forecasting, Multiplicative Error Model, Markov Switching, Smooth Transition, Common Trend
    JEL: C22 C32 C52 C58 C53
    Date: 2017–08
  7. By: Pötscher, Benedikt M.; Preinerstorfer, David
    Abstract: We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample results on size and power of heteroskedasticity and autocorrelation robust tests. These allow us, in particular, to show that the sufficient conditions for the existence of size-controlling critical values recently obtained in Pötscher and Preinerstorfer (2016) are often also necessary. We furthermore apply the results obtained to tests for hypotheses on deterministic trends in stationary time series regressions, and find that many tests currently used are strongly size-distorted.
    Keywords: size-distortion, autocorrelation and heteroskedasticity robust testing, trend testing
    JEL: C12 C22
    Date: 2017

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