nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒02‒16
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Bootstrapping the portmanteau tests in weak auto-regressive moving average models By Zhu, Ke
  2. PANICCA - PANIC on Cross-Section Averages By Reese, Simon; Westerlund, Joakim
  3. Unit Roots and Smooth Transitions: A Replication By Kulaksizoglu, Tamer
  4. Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models By Kripfganz, Sebastian
  5. The estimation uncertainty of permanent-transitory decompositions in co-integrated systems By Schreiber, Sven

  1. By: Zhu, Ke
    Abstract: This paper uses a random weighting (RW) method to bootstrap the critical values for the Ljung-Box/Monti portmanteau tests and weighted Ljung-Box/Monti portmanteau tests in weak ARMA models. Unlike the existing methods, no user-chosen parameter is needed to implement the RW method. As an application, these four tests are used to check the model adequacy in power GARCH models. Simulation evidence indicates that the weighted portmanteau tests have the power advantage over other existing tests. A real example on S&P 500 index illustrates the merits of our testing procedure. As one extension work, the block-wise RW method is also studied.
    Keywords: Bootstrap method; Portmanteau test; Power GARCH models; Random weighting approach; Weak ARMA models; Weighted portmanteau test.
    JEL: C0 C01 C12
    Date: 2015–02–06
  2. By: Reese, Simon (Department of Economics, Lund University); Westerlund, Joakim (Department of Economics, Lund University)
    Abstract: The cross-section average (CA) augmentation approach of Pesaran (2007) and Pesaran et al. (2013), and the principal components-based panel analysis of non-stationarity in idiosyncratic and common components (PANIC) of Bai and Ng (2004, 2010) are among the most popular “second-generation” approaches for cross-section correlated panels. One feature of these approaches is that they have different strengths and weaknesses. The purpose of the current paper is to develop PANICCA, a combined approach that exploits the strengths of both CA and PANIC.
    Keywords: PANIC; cross-section average augmentation; unit root test; cross-section dependence; common factors
    JEL: C12 C13 C33 C36
    Date: 2014–10–20
  3. By: Kulaksizoglu, Tamer
    Abstract: This paper replicates Leybourne et al. (1998), who propose a Dickey-Fuller type test for unit root that is most appropriate when there is reason to suspect the possibility of deterministic structural change in the series. We find that our replicated results are quite similar to the authors' results. We also make the Ox source code available.
    Keywords: Dickey-Fuller test, Integrated process, Nonlinear trend, Structural change
    JEL: C12 C15
    Date: 2015–02–04
  4. By: Kripfganz, Sebastian
    Abstract: I derive the unconditional transformed likelihood function and its derivatives for a fixed-effects panel data model with time lags, spatial lags, and spatial time lags that encompasses the pure time dynamic and pure space dynamic models as special cases. In addition, the model can accommodate spatial dependence in the error term. I demonstrate that the model-consistent representation of the initial-period distribution involves higher-order spatial lag polynomials. Their order is linked to the minimal polynomial of the spatial weights matrix and, in general, tends to infinity with increasing sample size. Consistent estimation requires an appropriate truncation of these lag polynomials unless the spatial weights matrix has a regular structure. The finite sample evidence from Monte Carlo simulations shows that a misspecification of the spatial structure for the initial observations results in considerable biases while the correctly specified estimator behaves well. As an application, I estimate a time-space dynamic wage equation allowing for peer effects within households.
    JEL: C13 C23 J31
    Date: 2014
  5. By: Schreiber, Sven
    Abstract: The topic of this paper is the estimation uncertainty of the Stock-Watson and Gonzalo-Granger permanent-transitory decompositions in the framework of the co-integrated vector autoregression. We suggest an approach to construct the confidence interval of the transitory component estimate in a given period (e.g. the latest observation) by conditioning on the observed data in that period. To calculate asymptotically valid confidence intervals we use the delta method and two bootstrap variants. As an illustration we analyze the uncertainty of (US) output gap estimates in a system of output, consumption, and investment.
    JEL: C32 C15 E32
    Date: 2014

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