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Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some way. Theoretical results about estimators usually require assumptions about the data generating mechanism.

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with the error term, violating gauss markov assumptions and generating bias. Problem: Consider a model of the most basic form: $$y_i = \alpha + \beta x_i + e_i$$ Now, we all know that leaving out …
asked Apr 7 '16 by Dole
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All right, time to attempt answering my own question. My preferred explanation here is that the conditions for the omitted variable bias are in fact, not complete. If it is assumed that the constant i …
answered Apr 9 '16 by Dole
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Is the assumption that: $E(e_i|x_i) = 0$ And the two assumptions that: $cov(x_i, e_i)=0$ $E(e_i)=0$ Equivalent? (I have seen both formulation of the assumptions). The two are certainly not … that $E(e_i|x_i) = 0$ should replace the autocorrelation assumption as well. That means that the assumptions are more coherent using the 2nd formulation as no condition is stated twice. Adding the …
asked Feb 26 '16 by Dole
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The most important assumptions are that the data is randomly sampled and that the error term and independent variables are not correlated. Only in these cases will you have wrong expected value for …
answered Apr 7 '16 by Dole
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Assume that OLS regression of the form: $$Y_t = X_t'\beta + u_t$$ Suppose $X_t$ are stochastic, thus standard Gauss-Markov assumptions need to be accommodated. Given that: $$\text{E} {(\hat\beta … difference? I don't see how a random variable could not be correlated with the error. Would it be correct to assume that the assumptions break down and betas are biased downward in practice, when random variables are introduced? …
asked Apr 7 '16 by Dole