Marginal-likelihood score-based tests of regression disturbances in the presence of nuisance parameters
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- Maxwell King & Ping Wu, 1997. "Locally optimal one-sided tests for multiparameter hypotheses," Econometric Reviews, Taylor & Francis Journals, vol. 16(2), pages 131-156.
- Lee, John H H & King, Maxwell L, 1993. "A Locally Most Mean Powerful Based Score Test for ARCH and GARCH Regression Disturbances," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 17-27, January.
- Brooks, R.D. & King, M.L., 1994. "Hypothesis Testing of Varying Coefficient Regression Models: Procedures and Applications," Monash Econometrics and Business Statistics Working Papers 5/94, Monash University, Department of Econometrics and Business Statistics.
- Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-1294, September.
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- Griffiths, W. E. & Surekha, K., 1986. "A Monte Carlo evaluation of the power of some tests for heteroscedasticity," Journal of Econometrics, Elsevier, vol. 31(2), pages 219-231, March.
- Honda, Yuzo, 1988. "A size correction to the Lagrange multiplier test for heteroskedasticity," Journal of Econometrics, Elsevier, vol. 38(3), pages 375-386, July.
- King, Maxwell L & Shively, Thomas S, 1993. "Locally Optimal Testing When a Nuisance Parameter Is Present Only under the Alternative," The Review of Economics and Statistics, MIT Press, vol. 75(1), pages 1-7, February.
- Ara, I. & King, M.L., 1995. "Marginal Likelihood Based Tests of a Subvector of the Parameter Vector of Linear Regression Disturbances," Monash Econometrics and Business Statistics Working Papers 12/95, Monash University, Department of Econometrics and Business Statistics.
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- Jahar Bhowmik & Maxwell King, 2007.
"Maximal invariant likelihood based testing of semi-linear models,"
Springer, vol. 48(3), pages 357-383, September.
- Maxwell L. King & Jahar L. Bhowmik, 2004. "Maximal Invariant Likelihood Based Testing of Semi-Linear Models," Econometric Society 2004 Australasian Meetings 245, Econometric Society.
- Marc K. Francke & Siem Jan Koopman & Aart F. de Vos, 2010.
"Likelihood functions for state space models with diffuse initial conditions,"
Journal of Time Series Analysis,
Wiley Blackwell, vol. 31(6), pages 407-414, November.
- Marc K. Francke & Siem Jan Koopman & Aart de Vos, 2008. "Likelihood Functions for State Space Models with Diffuse Initial Conditions," Tinbergen Institute Discussion Papers 08-040/4, Tinbergen Institute.
- Badi Baltagi & Seuck Heun Song & Byoung Cheol Jung, 2002. "Simple Lm Tests For The Unbalanced Nested Error Component Regression Model," Econometric Reviews, Taylor & Francis Journals, vol. 21(2), pages 167-187.
- Jae Kim & Mahbuba Yeasmin, 2005. "The Size and Power of the Bias-Corrected Bootstrap Test for Regression Models with Autocorrelated Errors," Computational Economics, Springer;Society for Computational Economics, vol. 25(3), pages 255-267, June.
- Horowitz, Joel L. & Savin, N. E., 2000. "Empirically relevant critical values for hypothesis tests: A bootstrap approach," Journal of Econometrics, Elsevier, vol. 95(2), pages 375-389, April.
- Sriananthakumar, Sivagowry, 2013. "Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach," Economic Modelling, Elsevier, vol. 33(C), pages 126-136.
- Martellosio, Federico, 2008. "Power Properties of Invariant Tests for Spatial Autocorrelation in Linear Regression," MPRA Paper 7255, University Library of Munich, Germany.
- Willa W. Chen & Rohit S. Deo, 2009. "The restricted likelihood ratio test at the boundary in autoregressive series," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 618-630, November.
- Jahar L. Bhowmik & Maxwell L. King, 2005. "Parameter Estimation in Semi-Linear Models Using a Maximal Invariant Likelihood Function," Monash Econometrics and Business Statistics Working Papers 18/05, Monash University, Department of Econometrics and Business Statistics.
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