mlr_inference.pptx - Multiple Regression Analysis Inference \u2022 Statistical inference in the regression model \u2022 Hypothesis tests about population

mlr_inference.pptx - Multiple Regression Analysis Inference...

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Multiple Regression Analysis: Inference Statistical inference in the regression model Hypothesis tests about population parameters Construction of confidence intervals Sampling distributions of the OLS estimators The OLS estimators are random variables We already know their expected values and their variances However, for hypothesis tests we need to know their distribution In order to derive their distribution we need additional assumptions Assumption about distribution of errors: normal distribution 1
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Multiple Regression Analysis: Inference Assumption MLR.6 (Normality of error terms) independently of It is assumed that the unobserved factors are normally distributed around the population regression function. The form and the variance of the distribution does not depend on any of the explanatory variables. It follows that: 2
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Multiple Regression Analysis: Inference Discussion of the normality assumption The error term is the sum of “many” different unobserved factors Sums of independent factors are normally distributed (CLT) Problems: How many different factors? Number large enough? Possibly very heterogenuous distributions of individual factors How independent are the different factors? The normality of the error term is an empirical question At least the error distribution should be “close” to normal In many cases, normality is questionable or impossible by definition 3
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Multiple Regression Analysis: Inference Discussion of the normality assumption (cont.) Examples where normality cannot hold: Wages (nonnegative; also: minimum wage) Number of arrests (takes on a small number of integer values) Unemployment (indicator variable, takes on only 1 or 0) In some cases, normality can be achieved through transformations of the dependent variable (e.g. use log(wage) instead of wage) Under normality, OLS is the best (even nonlinear) unbiased estimator Important: For the purposes of statistical inference, the assumption of normality can be replaced by a large sample size 4
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Multiple Regression Analysis: Inference Terminology Theorem 4.1 (Normal sampling distributions) Under assumptions MLR.1 – MLR.6: The estimators are normally distributed around the true parameters with the variance that was derived earlier The standardized estimators follow a standard normal distribution “Gauss-Markov assumptions” “Classical linear model (CLM) assumptions” 5
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Multiple Regression Analysis: Inference Testing hypotheses about a single population parameter Theorem 4.2 (t-distribution for the standardized estimators) Null hypothesis (for more general hypotheses, see below) Under assumptions MLR.1 – MLR.6: If the standardization is done using the estimated standard deviation (= standard error), the normal distribution is replaced by a t-distribution The population parameter is equal to zero, i.e. after controlling for the other independent variables, there is no effect of x j on y Note: The t-distribution is close to the standard normal distribution if n-k-1 is large.
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