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(Non) Linear Regression Modeling

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  • Čížek, Pavel

Abstract

We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1, . . . , Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1, . . . ,Xp), p ∈ N, which explain or predict the dependent variables by means of the model. Such relationships and models are commonly referred to as regression models. --

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Bibliographic Info

Paper provided by Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) in its series Papers with number 2004,11.

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Date of creation: 2004
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Handle: RePEc:zbw:caseps:200411

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  1. Ullah, A. & Srivastava, V. K. & Chandra, R., 1983. "Properties of shrinkage estimators in linear regression when disturbances are not normal," Journal of Econometrics, Elsevier, vol. 21(3), pages 389-402, April.
  2. Hardle, W., 1992. "Applied Nonparametric Methods," Papers 9206, Tilburg - Center for Economic Research.
  3. Kim, Minbo & CarterHill, R., 1995. "Shrinkage estimation in nonlinear regression The Box-Cox transformation," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 1-33.
  4. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
  5. Jan R. Magnus, 2002. "Estimation of the mean of a univariate normal distribution with known variance," Econometrics Journal, Royal Economic Society, vol. 5(1), pages 225-236, June.
  6. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
  7. Kadiyala, Krishna, 1984. "A class of almost unbiased and efficient estimators of regression coefficients," Economics Letters, Elsevier, vol. 16(3-4), pages 293-296.
  8. Anders Björkström, 1999. "A Generalized View on Continuum Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 26(1), pages 17-30.
  9. Hawkins, Douglas M. & Yin, Xiangrong, 2002. "A faster algorithm for ridge regression of reduced rank data," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 253-262, August.
  10. Chawla, J. S., 1990. "A note on ridge regression," Statistics & Probability Letters, Elsevier, vol. 9(4), pages 343-345, April.
  11. Watanabe, Toshiaki, 1999. "A Non-linear Filtering Approach to Stochastic Volatility Models with an Application to Daily Stock Returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 101-21, March-Apr.
  12. Judge, G.G. & Bock, M.E., 1983. "Biased estimation," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 10, pages 599-649 Elsevier.
  13. Wang, Song-Gui & Chow, Shein-Chung, 1990. "A note on adaptive generalized ridge regression estimator," Statistics & Probability Letters, Elsevier, vol. 10(1), pages 17-21, June.
  14. Dagenais, Marcel G., 1983. "Extension of the ridge regression technique to non-linear models with additive errors," Economics Letters, Elsevier, vol. 12(2), pages 169-174.
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