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Time-Series Near-Neighbor Regression

Author

Listed:
  • Jaditz Ted

    (CNA Corporation)

  • Riddick Leigh A.

    (American University)

Abstract

Near-neighbor regression is a popular empirical tool. In this note, we describe the general algorithm for the technique and we identify several potential problem areas that researchers should consider. Standard statistical packages are generally inflexible, and hide important modeling decisions from the researcher. We provide code for a simple but very flexible routine that readers can customize for their own use.

Suggested Citation

  • Jaditz Ted & Riddick Leigh A., 2000. "Time-Series Near-Neighbor Regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(1), pages 1-11, April.
    • Handle: RePEc:bpj:sndecm:v:4:y:2000:i:1:n:al1
    DOI: 10.2202/1558-3708.1054
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    References listed on IDEAS

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    1. LeBaron, Blake, 1992. "Forecast Improvements Using a Volatility Index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 137-149, Suppl. De.
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    4. Jaditz, Ted & Riddick, Leigh A. & Sayers, Chera L., 1998. "MULTIVARIATE NONLINEAR FORECASTING Using Financial Information to Forecast the Real Sector," Macroeconomic Dynamics, Cambridge University Press, vol. 2(3), pages 369-382, September.
    5. Jaditz, Ted & Sayers, Chera L, 1998. "Out-of-Sample Forecast Performance as a Test for Nonlinearity in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 110-117, January.
    6. Hsieh, David A., 1993. "Implications of Nonlinear Dynamics for Financial Risk Management," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(1), pages 41-64, March.
    7. Hsieh, David A., 1993. "Using non-linear methods to search for risk premia in currency futures," Journal of International Economics, Elsevier, vol. 35(1-2), pages 113-132, August.
    8. Mizrach, B, 1992. "Multivariate Nearest-Neighbor Forecasts of EMS Exchange Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 151-163, Suppl. De.
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    Citations

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    Cited by:

    1. Theodore Panagiotidis, 2010. "An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application," Computational Economics, Springer;Society for Computational Economics, vol. 36(2), pages 121-132, August.
    2. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2007. "Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non-linear models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(8), pages 601-619.
    3. Álvarez-Díaz, Marcos & Hammoudeh, Shawkat & Gupta, Rangan, 2014. "Detecting predictable non-linear dynamics in Dow Jones Islamic Market and Dow Jones Industrial Average indices using nonparametric regressions," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 22-35.
    4. Marcos Álvarez-Díaz, 2020. "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods," Empirical Economics, Springer, vol. 59(3), pages 1285-1305, September.
    5. Marcos Álvarez-Díaz & Shawkat Hammoudeh & Rangan Gupta, 2013. "Detecting Predictable Non-linear Dynamics in Dow Jones Industrial Average and Dow Jones Islamic Market Indices using Nonparametric Regressions," Working Papers 201385, University of Pretoria, Department of Economics.
    6. Szarek, Dawid & Bielak, Łukasz & Wyłomańska, Agnieszka, 2020. "Long-term prediction of the metals’ prices using non-Gaussian time-inhomogeneous stochastic process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    7. Ilias Lekkos & Costas Milas & Theodore Panagiotidis, 2005. "On the predictability of common risk factors in the US and UK interest rate swap markets: Evidence from non-linear and linear models," Discussion Paper Series 2005_9, Department of Economics, Loughborough University, revised Sep 2005.

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