nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒01‒19
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. On Bias in the Estimation of Structural Break Points By Liang Jiang; Xiaohu Wang; Jun Yu
  2. Estimation and inference of FAVAR models By Bai, Jushan; Li, Kunpeng; Lu, Lina
  3. Poisson qmle of count time series models By Ahmad, Ali; Francq, Christian
  4. Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence By Arturas Juodis
  5. A Bayesian Approach to Modelling Bivariate Time-Varying Cointegration and Cointegrating Rank By Chew Lian Chua; Sarantis Tsiaplias

  1. By: Liang Jiang (Singapore Management University); Xiaohu Wang (The Chinese University of Hong Kong); Jun Yu (Singapore Management University)
    Abstract: Based on the Girsanov theorem, this paper obtains the exact finite sample distribution of the maximum likelihood estimator of structural break points in a continuous time model. The exact finite sample theory suggests that, in empirically realistic situations, there is a strong finite sample bias in the estimator of structural break points. This property is shared by least squares estimator of both the absolute structural break point and the fractional structural break point in discrete time models. A simulation-based method based on the indirect estimation approach is proposed to reduce the bias both in continuous time and discrete time models. Monte Carlo studies show that the indirect estimation method achieves substantial bias reductions. However, since the binding function has a slope less than one, the variance of the indirect estimator is larger than that of the original estimator.
    Keywords: Structural change, Bias reduction, Indirect estimation, Break point
    JEL: C11 C46
    Date: 2014–12
  2. By: Bai, Jushan; Li, Kunpeng; Lu, Lina
    Abstract: The factor-augmented vector autoregressive (FAVAR) model, first proposed by Bernanke, Bovin, and Eliasz (2005, QJE), is now widely used in macroeconomics and finance. In this model, observable and unobservable factors jointly follow a vector autoregressive process, which further drives the comovement of a large number of observable variables. We study the identification restrictions in the presence of observable factors. We propose a likelihood-based two-step method to estimate the FAVAR model that explicitly accounts for factors being partially observed. We then provide an inferential theory for the estimated factors, factor loadings and the dynamic parameters in the VAR process. We show how and why the limiting distributions are different from the existing results.
    Keywords: high dimensional analysis; identification restrictions; inferential theory; likelihood-based analysis; VAR; impulse response.
    JEL: C3 C32 C38
    Date: 2014–12
  3. By: Ahmad, Ali; Francq, Christian
    Abstract: Regularity conditions are given for the consistency of the Poisson quasi-maximum likelihood estimator of the conditional mean parameter of a count time series. The asymptotic distribution of the estimator is studied when the parameter belongs to the interior of the parameter space and when it lies at the boundary. Tests for the significance of the parameters and for constant conditional mean are deduced. Applications to specific INAR and INGARCH models are considered. Numerical illustrations, on Monte Carlo simulations and real data series, are provided.
    Keywords: Boundary of the parameter space; Consistency and asymptotic normality; Integer-valued AR and GARCH models; Non-normal asymptotic distribution; Poisson quasi-maximum likelihood estimator; Time series of counts.
    JEL: C12 C13 C22
    Date: 2014–11
  4. By: Arturas Juodis
    Abstract: This paper considers the Panel Vector Autoregressive Models of order 1 (PVAR(1)) with possibly spatially dependent error terms. We prove that the cointegration testing procedure of Binder, Hsiao, and Pesaran (2005) is not valid due to the singularity of the corresponding Hessian matrices under pure unit roots or cointegrated processes. As an alternative we propose a simple Method of Moments based cointegration test using the rank test of Kleibergen and Paap (2006) for fixed number of time observations. The test is shown to be robust to time series heteroscedasticity as well as unbalanced panels. The novelty of our approach is that we exploit the "weakness" of the Anderson and Hsiao (1982) moment conditions in the construction of the new test. The finite-sample performance of the proposed test statistic is investigated using the simulated data. The results show that for most scenarios the method performs well in terms of both size and power. The proposed test is applied to employment and wage equations using Spanish firm data of Alonso-Borrego and Arellano (1999) and the results show little evidence for cointegration.
    Date: 2014–12–18
  5. By: Chew Lian Chua (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Sarantis Tsiaplias (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)
    Abstract: A bivariate model that allows for both a time-varying cointegrating matrix and time-varying cointegrating rank is presented. The model addresses the issue that, in real data, the validity of a constant cointegrating relationship may be questionable. The model nests the sub-models implied by alternative cointegrating matrix ranks and allows for transitions between stationarity and non-stationarity, and cointegrating and non-cointegrating relationships in accordance with the observed behaviour of the data. A Bayesian test of cointegration is also developed. The model is used to assess the validity of the Fisher effect and is also applied to equity market data.
    Keywords: Error correction models, singular value decomposition, cointegration tests
    JEL: C11 C32 C51 C52
    Date: 2014–12

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