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Nonparametric HAC estimation for time series data with missing observations

  • Deepa Dhume Datta
  • Wenxin Du
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    The Newey and West (1987) estimator has become the standard way to estimate a heteroskedasticity and autocorrelation consistent (HAC) covariance matrix, but it does not immediately apply to time series with missing observations. We demonstrate that the intuitive approach to estimate the true spectrum of the underlying process using only the observed data leads to incorrect inference. Instead, we propose two simple consistent HAC estimators for time series with missing data. First, we develop the Amplitude Modulated estimator by applying the Newey-West estimator and treating the missing observations as non-serially correlated. Secondly, we develop the Equal Spacing estimator by applying the Newey-West estimator to the series formed by treating the data as equally spaced. We show asymptotic consistency of both estimators for inference purposes and discuss finite sample variance and bias tradeoff. In Monte Carlo simulations, we demonstrate that the Equal Spacing estimator is preferred in most cases due to its lower bias, while the Amplitude Modulated estimator is preferred for small sample size and low autocorrelation due to its lower variance.

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    File URL: http://www.federalreserve.gov/pubs/ifdp/2012/1060/default.htm
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    File URL: http://www.federalreserve.gov/pubs/ifdp/2012/1060/ifdp1060.pdf
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    Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series International Finance Discussion Papers with number 1060.

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    Date of creation: 2012
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    Handle: RePEc:fip:fedgif:1060
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    1. Samuel S. Kortum & Jonathan Eaton & Brent Neiman & John Romalis, 2010. "Trade and the Global Recession," DEGIT Conference Papers c015_002, DEGIT, Dynamics, Economic Growth, and International Trade.
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