nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒04‒29
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Heteroskedasticity-and-Autocorrelation-Consistent Bootstrapping By Russel Davidson; Andrea Monticini
  2. A Factor Analytical Method to Interactive Effects Dynamic Panel Models with or without Unit Root By Westerlund, Joakim; Norkute, Milda
  3. Boosting multi-step autoregressive forecasts By Souhaib Ben Taieb; Rob J Hyndman
  4. Asymptotic Inferences for an AR(1) Model with a Change Point: Stationary and Nearly Non-stationary Cases By Pang, Tianxiao; Zhang, Danna; Chong, Terence Tai-Leung

  1. By: Russel Davidson (Department of Economics and CIREQ McGill University); Andrea Monticini (Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore)
    Abstract: In many, if not most, econometric applications, it is impossible to estimate consistently the elements of the white-noise process or processes that underlie the DGP. A common example is a regression model with heteroskedastic and/or autocorrelated disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can be shown to work very well with many models, both univariate and multivariate, in the presence of heteroskedasticity. Nothing comparable appears to exist for handling serial correlation. Recently, there has been proposed something called the dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and merits considerable further study.
    Keywords: Bootstrap, time series, wild bootstrap, dependent wild bootstrap,HAC covariance matrix estimator
    JEL: C12 C22 C32
    Date: 2014–03
  2. By: Westerlund, Joakim (Deakin University); Norkute, Milda (Department of Economics, Lund University)
    Abstract: In a recent study, Bai (Fixed-Effects Dynamic Panel Models, A Factor Analytical Method. Econometrica 81, 285-314, 2013a) proposes a new factor analytic (FA) method to the estimation of dynamic panel data models, which has the unique and very useful property that it is completely bias-free. However, while certainly appealing, it is restricted to fixed effects models without a unit root. In many situations of practical relevance this is a rather restrictive consideration. The purpose of the current study is therefore to extend the FA approach to cover models with multiple interactive effects and a possible unit root.
    Keywords: Interactive fixed effects; Dynamic panel data models; Unit root; Factor analytical method
    JEL: C12 C13 C33 C36
    Date: 2014–04–14
  3. By: Souhaib Ben Taieb; Rob J Hyndman
    Abstract: Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propose a new forecasting strategy which boosts traditional recursive linear forecasts with a direct strategy using a boosting autoregression procedure at each horizon. First, we investigate the performance of the proposed strategy in terms of bias and variance decomposition of the error using simulated time series. Then, we evaluate the proposed strategy on real-world time series from two forecasting competitions. Overall, we obtain excellent performance with respect to the standard forecasting strategies.
    Keywords: Multi-step forecasting; forecasting strategies; recursive forecasting; direct forecasting; linear time series; nonlinear time series; boosting
    JEL: C22 C53 C14
    Date: 2014
  4. By: Pang, Tianxiao; Zhang, Danna; Chong, Terence Tai-Leung
    Abstract: This paper examines the asymptotic inference for AR(1) models with a possible structural break in the AR parameter β near the unity at an unknown time k₀. Consider the model y_{t}=β₁y_{t-1}I{t≤k₀}+β₂y_{t-1}I{t>k₀}+ε_{t}, t=1,2,⋯,T, where I{⋅} denotes the indicator function. We examine two cases: Case (I) |β₁|
    Keywords: AR(1) model, Change point, Domain of attraction of the normal law, Limiting distribution, Least squares estimator.
    JEL: C22
    Date: 2013–12–30

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