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Do we need a stochastic trend in cay estimation? Yes

Author

Listed:
  • Marcella Lucchetta

    (Department of Economics, University Of Venice CÃ Foscari)

  • Michele Costola

    (Research Center SAFE, Goethe University Frankfurt.)

  • Lorenzo Frattarolo

    (Department of Economics, University Of Venice CÃ Foscari)

  • Antonio Paradiso

    (Department of Economics, University Of Venice CÃ Foscari)

Abstract

The paper investigates the importance of modeling in cay estimations from a statistical and economic perspective by observing the stochastic trend, a thus far neglected component. In order to do this, we perform an empirical analysis on US secular annual data from 1900 to 2015 considering the cay with non-durables and services and the cay with total consumption expenditure. Findings show the usefulness of including the stochastic trend in cay estimation. Furthermore, out-of-sample statistical and economic significance tests show the ability of the cay model with trend to outperform the traditional cay measure.

Suggested Citation

  • Marcella Lucchetta & Michele Costola & Lorenzo Frattarolo & Antonio Paradiso, 2016. "Do we need a stochastic trend in cay estimation? Yes," Working Papers 2016:24, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2016:24
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    References listed on IDEAS

    as
    1. Thomas Piketty & Gabriel Zucman, 2014. "Capital is Back: Wealth-Income Ratios in Rich Countries 1700–2010," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(3), pages 1255-1310.
    2. John Y. Campbell & N. Gregory Mankiw, 1989. "Consumption, Income, and Interest Rates: Reinterpreting the Time Series Evidence," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 185-246, National Bureau of Economic Research, Inc.
    3. Martin Lettau & Sydney Ludvigson, 2001. "Consumption, Aggregate Wealth, and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 56(3), pages 815-849, June.
    4. Jonathan Ingersoll & Ivo Welch, 2007. "Portfolio Performance Manipulation and Manipulation-proof Performance Measures," The Review of Financial Studies, Society for Financial Studies, vol. 20(5), pages 1503-1546, 2007 17.
    5. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    6. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    7. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    8. repec:hal:pseose:halshs-01109372 is not listed on IDEAS
    9. Della Corte, Pasquale & Sarno, Lucio & Valente, Giorgio, 2010. "A century of equity premium predictability and the consumption-wealth ratio: An international perspective," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 313-331, June.
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    More about this item

    JEL classification:

    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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