A Discourse on Modified Likelihood Ratio (LR), Wald and Lagrange Multipliers (LM) Tests for Testing General Linear Hypothesis in Stochastic Linear Regression Model

  • Abstract
  • Keywords
  • References
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  • Abstract

    In this research paper various new advanced inferential tools namely modified likelihood ratio (LR), Ward and Lagrange Multiplier test statistics have been proposed for testing general linear hypothesis in stochastic linear regression model. In this process internally studentized residuals have been used. This research study has brought out some new advance tools for analysing inferential aspects of stochastic linear regression models by using internally studentized residuals. Miguel Fonseca et.al [1] developed statistical inference in linear models dealing with the theory of maximum likelihood estimates and likelihood ratio tests under some linear inequality restrictions on the regression coefficients. Tim Coelli [2] used Monte carlo experimentation to investigate the finite sample properties of maximum likelihood (ML) and correct ordinary least squares (COLS) estimators of the half –normal stochastic frontier production function. In 2011, p. Bala siddamuni et.al [3] have developed advanced tools for mathematical and stochastic modelling.



  • Keywords

    Information matrix, OLS residual sum of squares, LR test, Ward test, LM test, stochastic linear regression models, internally stduntized residuals.

  • References

      [1] Miguel Fonseca, Jrau Tiazo Mexia, Bimal K Sinha, Roman Zmyslony, “Likelihood ratio tests in linear models with linear equality restrictions on regression coefficients”, REVSTAT-Statistical journal, Vol.(13), (2015), Pp: 103-118.

      [2] Tim Coelli, “Estimators and hypothesis test for a stochastic frontier function: A Monte Carlo analysis, Journal of productivity analysis, Vol. (6), (1995), Pp: 247- 268.

      [3] Balasiddamuni, P.et.al. “Advanced Tools for Mathematical and Stochastic modeling”, Proceedings of the International Conference on Stochastic modeling and Simulation, Allied Publishers, (2011).

      [4] Byron J.T. Morgan, (2008), “Applied stochastic Modelling”, CRC Press, (2008), 978-1- 58488- 666-2.

      [5] Berry L. Nelson, (1995), “Stochastic Modeling, Analysis and Simulation”, McGraw-Hill, (1995), 978-0070462137.

      [6] Nelson, B.L. “Stochastic Modeling”, McGraw-Hill, New York, (1995), 0-486-47770-3.

      [7] Taylor, H.M. and Samuel karlin, “An Introduction to Stochastic Modeling”, Academic Press, London, (1998), 978-0-12-684887-87.

      [8] Nafeez Umar, S. and Balasiddamuni, P. (2013), “Statistical Inference on Model Specification in Econometrics”, LAMBERT Academic Publishing, Germany,(2013).




Article ID: 21222
DOI: 10.14419/ijet.v7i4.10.21222

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