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Refers to a general estimation technique that selects the parameter value to minimize the squared difference between two quantities, such as the observed value of a variable, and the expected value of that observation conditioned on the parameter value. Gaussian linear models are fit by least squares and least squares is the idea underlying the use of mean-squared-error (MSE) as a way of evaluating an estimator.

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My professor gave me some data to study basic econometrics and I'm trying to develop the best model possible to fit financial returns of a company based on two different stock indexes (one from the lo …
asked Jul 3 by Delta
1
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I have estimated a regression and found a breakpoint using chow: Then I created a dummy variable which is 0 before the break and 1 from the break until the end. This is the result: I was assumin …
asked Jul 3 by Delta