A Comparison Between the Power of the Durbin-Watson Test and the Power of the BLUS Test

Abrahamse, A. P. J.; Koerts, J.

doi:10.2307/2283474

Cited by 10 publications

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“…The most commonly used test for serial correlation is based on the Durbin-Watson statistic t where et = Yt ~ Yt = Yt -2. The distribution and properties of this statistic have been investigated, for example, by Watson (1950, 1951); Abrahamse and Koerts (1969); Henshaw (1966);and Blatberg (1973). The Durbin-Watson statistic is used t o test the hypothesis p = 0, assuming that the error structure is first-order autocorrelated of form et = pet-l t ut where ut -N(O, u') for all t.…”

Section: Multiple Regression Analysismentioning

confidence: 99%

Problems in Forecasting Water Requirements1

Domokos

Weber

Duckstein

1976

J American Water Resour Assoc

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ABSTRACT. Methodological problems associated with forecasting water requirements by use of regression analysis are examined. Problems occurring when long‐range forecasts are based on linear and nonlinear extrapolation of time series models include possible changes in socioeconomic conditions, water allocation system structure, and limits to growth. Problems arising in forecasting based on multiple regression models are likely to involve serially dependent errors, multicollinear explanatory variables, and difficulties inherent to the presence of explanatory variables that must themselves be predicted.

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Section: Multiple Regression Analysismentioning

confidence: 99%