Abstract
Fitting a linear function of some variables (denoted by x 1, …,x k ) to a variable denoted y by least squares is an old statistical technique. In the Fisherian revolution of the 1920s and 1930s, statistical methods were mainly applied in the natural sciences where one could often design the experiments that produced the data. The emphasis was largely on how the experiments should be designed to make the least-squares assumptions valid rather than on checking the correctness of these assumptions.
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References
Aitken, A.C. (1935) On least squares and the linear combination of observations Proc. Roy. Soc. Edinburgh 55. 42–48.
Anderson. R.L. (1942). Distribution of the serial correlation coefficient, Ann. Math Statist 13. 1–13.
Anderson, R.L, and Anderson, T.W. (1950). Distribution of the circular serial correla¬tion coefficient for residuals from a fitted Fourier series, Ann. Math Statist. 21, 59–81.
Anderson, T.W. (1948). On the theory of testing serial correlation, Skand. Aktuarktidskrift, 31. 88–116.
Cochrane, D. and Orcutt, G.H. (1949). “Application of least squares regression to relationships containing auto-correlated error terms,” J. Amer. Statist Assoc. 44, 32–61.
Courant. R., and Hilbert, D. (1953). Mathematical Methods of Physics. Vol. I. Wiley- Interscience. New York.
Durbin. J. (1957). Testing for serial correlation in systems of simultaneous regression equations, Biometrika, 44. 370–377.
Durbin. J. (1970). Testing for serial correlation in least squares regression when some of the regressors are lagged dependent variables. Econometrica, 38, 410–421.
Durbin. J., and Watson. G.S. (1971). Testing for serial correlation in least squares regression, III. Biometrika, 58. 1–19.
Frisch. R. (1934). Statistical Confluence Analysis by Means of Complete Regression Systems. University Oekonomiske Institute. Oslo.
Goldberger, A.S. (1964). Econometric Theory. Wiley. New York.
Hannan, F.J. (1957). Testing for serial correlation in least squares regression. Biometrika, 44. 57–66.
Inder, B.A. (1986). An approximation to the null distribution of the Durbin-Watson statistic in models containing lagged dependent variables. Econometric Theory, 2, 413–428.
Johnston, J. (1963). Econometric Methods. McGraw-Hill, New York.
King, M. L. (1987). Testing for autocorrelation in linear regression models: A survey, in Specification Analysis in the Linear Model ( M.L. King, and D.E.A. Giles, eds.). Routledge and Kegan Paul, London, pp. 19–73.
King. M.L.. and Wu, P.X. (1991). Small-disturbance asymptotics and the Durbin- Watson and related tests in the dynamic regression model J. Econometrics 47. 145–152.
Koopmans, T.C. (1937). Linear Regression Analysis of Economic Time Series. Erven F. Böhm. Haarlem.
Phillips, P.C.B. (1988). The ET interview: Professor James Durbin, Econometric Theory, 4. 125–157.
Stone, R. (1954). The Measurement of Consumers’ Expenditure and Behaviour in the United Kingdom, 1920-1938. Vol. I. Cambridge University Press. Cambridge.
von Neumann. J. (1941). Distribution of the ratio of the mean square successive difference to the variance. Ann. Math. Statist. 12, 367–395.
Watson. G.S. (1986). A boy from the bush, in The Craft of Probabilistic Modelling ( J. Gani, ed.). Springer-Verlag. New York. pp. 43–60.
White. K.J. (1978). A general computer program for econometric methods-SHAZAM. Econometrica, 46, 239–240.
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King, M.L. (1992). Introduction to Durbin and Watson (1950, 1951) Testing for Serial Correlation in Least Squares Regression. I, II. In: Kotz, S., Johnson, N.L. (eds) Breakthroughs in Statistics. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4380-9_19
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