CLRM

(redirected from Classical Linear Regression Model)
AcronymDefinition
CLRMClassroom
CLRMClassical Linear Regression Model (econometrics)
CLRMCottagelink Rental Management (Ontario, Canada)
CLRMCook, Little, Rosenblatt, and Manson (law firm; Manchester, NH)
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References in periodicals archive ?
When the results are evaluated, it is explored that the number of predictor variables used in the classical linear regression model can be reduced.
He covers the linear regression model, the classical linear regression model, the classical normal linear regression model: the method of maximum likelihood, distribution theory and hypothesis testing, and extensions of the classical linear regression model: generalized least squares and stochastic or endogenous regressors.
The' t' statistics in AR model do not exactlyfollow the t distribution because one of the basicassumptions of the classical linear regression model has been violated.
The' t' statistics in AR model do not exactlyfollow the t distribution because one of the basicassumptions of the classical linear regression model has been violated.For analysis of variance (ANOVA), the coefficient of determination (R2) can be calculated as This shows that 69.8654% of the variation explainedby the regression model and remaining itself unexplained.
Following the introduction, chapters cover the classical linear regression model, classical linear regression model assumptions and diagnostic tests, univariate time series modeling and forecasting, multivariate models, modeling long-run relationships in finance, modeling volatility and correlation, switching models, panel data, limited dependent variable models, simulation methods, conducting empirical research, and recent and future developments in the modeling of financial time series.
These conclusions are obtained from and therefore pertain to the classical linear regression model with two predictors, assuming that [z.sub.t] is a surrogate for [x.sub.t] (nondifferential errors).
The classical linear regression model is referred to here as ordinary linear regression (OLR).[1] The regression line is calculated by minimizing the squared residuals in the y direction ("least squares").
Thus a key assumption of the classical linear regression model is violated, and the coefficients are no longer unbiased.
Professor Gujarati proceeds effortlessly from a review of probability and statistics together with the detailed classical linear regression model, to the thorny problems of multicollinearity, heteroscedasticity, and autocorrelation.
A classical linear regression model, for which ordinary least squares (OLS) is the optimal estimator, is based on a number of assumptions.
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