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Optimal Signal Extraction with Correlated Components

Author

Listed:
  • McElroy Tucker S.

    (U.S. Census Bureau – CSRM, 4600 Silver Hill Road, Washington, DC 20233, USA)

  • Maravall Agustin

    (Bank of Spain, Madrid, Spain)

Abstract

While it is typical in the econometric signal extraction literature to assume that the unobserved signal and noise components are uncorrelated, there is nevertheless an interest among econometricians in the hypothesis of hysteresis, i.e. that major movements in the economy are fundamentally linked. While specific models involving correlated signal and noise innovation sequences have been developed and applied using state space methods, there is no systematic treatment of optimal signal extraction with correlated components. This paper provides the mean square error optimal formulas for both finite samples and bi-infinite samples and furthermore relates these filters to the more well-known Wiener–Kolmogorov (WK) and Beveridge–Nelson (BN) signal extraction formulas in the case of ARIMA component models. Then we obtain the result that the optimal filter for correlated components can be viewed as a weighted linear combination of the WK and BN filters. The gain and phase functions of the resulting filters are plotted for some standard cases. Some discussion of estimation of hysteretic models is presented, along with empirical results on an economic time series. Comparisons are made between signal extractions from traditional WK filters and those arising from the hysteretic models.

Suggested Citation

  • McElroy Tucker S. & Maravall Agustin, 2014. "Optimal Signal Extraction with Correlated Components," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 237-273, July.
    • Handle: RePEc:bpj:jtsmet:v:6:y:2014:i:2:p:37:n:3
    DOI: 10.1515/jtse-2013-0016
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
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