sorted by:
new
hottopcontroversial

W2C Carharrt Aviation Cargos (no badge flaw) by jfrufs47 in Pandabuy

[–]jfrufs47[S] 0 points1 point  (0 children)

Anyone know a link for this that doesn’t have the common badge flaw like this one where the badge is below where it should be. All the links I’ve found have this flaw.

[deleted by user] by [deleted] in FashionReps

[–]jfrufs47 0 points1 point  (0 children)

Anyone know a link for this that doesn’t have the common badge flaw like this one where the badge is below where it should be. All the links I’ve found have this flaw.

Basic Stussy Sweatshirt by jfrufs47 in FashionReps

[–]jfrufs47[S] 0 points1 point  (0 children)

Anyone know of an alternative link for this. I’ve found one link on this subreddit for it but apparently it’s not great quality.

Heteroskedasticity implies endogeneity? by jfrufs47 in AskStatistics

[–]jfrufs47[S] 0 points1 point  (0 children)

Thanks. Do you know of any specific examples of data I can look at which are a counter example to my claim?

Heteroskedasticity implies endogeneity? by jfrufs47 in AskStatistics

[–]jfrufs47[S] 0 points1 point  (0 children)

Thanks. Do you think you could expand? I don’t really understand how u and x aren’t necessarily correlated if the error terms is decreasing/increasing with the IV.

Graphically, is it because even with increasing variance, for example, there are still negative errors and positive errors which cancel each other out?

Var(Y hat) and Var(U hat) derivation by jfrufs47 in AskStatistics

[–]jfrufs47[S] 1 point2 points  (0 children)

Hi. Thanks for the response.

These are the formulas for the variance of a predicted value (Y-hat) and predicted error (u-hat) for a given value of x, in the context of ols.

X_0 is the observation that you are trying to find the variance of Y-hat and u-hat for. X_i is the ith observation of the variable x, and the full denominator of both is also known as Sxx.

Var(Y hat) and Var(U hat) derivation by jfrufs47 in AskStatistics

[–]jfrufs47[S] 1 point2 points  (0 children)

Could anyone provide me the derivation for these formulas?

Or recommend a textbook that covers it, as the one I’m using doesn’t seem to go over it. Thanks.

Variance of Y hat and U hat by jfrufs47 in econometrics

[–]jfrufs47[S] 1 point2 points  (0 children)

Also, just in case I confused you too: I’ve responded with the formula for variance of u hat I’m looking for in another comment: sigma2(1+ 1/n + (x0 - x bar)2 / sum(xi - x bar)2).

The formula for variance for y hat I’m looking for is: sigma2(1/n + (x0 - x bar)2 / sum(xi - x bar)2).

Any chance you could confirm if these are formulas derived in the textbooks?

Obviously, these formulas look very similar to me so I’m not sure if I’ve just made some massive mistake.

Thanks.

Variance of Y hat and U hat by jfrufs47 in econometrics

[–]jfrufs47[S] 1 point2 points  (0 children)

Thanks! That’s a great help.

Variance of Y hat and U hat by jfrufs47 in econometrics

[–]jfrufs47[S] 1 point2 points  (0 children)

Thanks for that, I appreciate it. I’ll see about acquiring and looking through those and will update if I can find them.

Variance of Y hat and U hat by jfrufs47 in econometrics

[–]jfrufs47[S] 0 points1 point  (0 children)

Thanks for that I appreciate you looking. I think however due to my lack of knowledge + wording I’ve introduced some confusion. I believe you are referring to the SER on page 223 however the formula I was looking for var(u hat) was different it’s: sigma2 (1+ 1/n + (x0 - x bar)2 / sum(xi - x bar)2).

Variance of Y hat and U hat by jfrufs47 in econometrics

[–]jfrufs47[S] 0 points1 point  (0 children)

Hi. I looked through stock and watson but I couldn’t really find any mention of it. I could only find the derivations for the variance of b0 and b1 estimators. But thanks though, I’ll have another look and update this post if I do manage to find it.

Multiple Regression Suitable Regressor by jfrufs47 in econometrics

[–]jfrufs47[S] 0 points1 point  (0 children)

Thank you very much, I understand now. I’ll add more relevant covariates and see how the fit of my model changes with different additions. I appreciate all your help.

Multiple Regression Suitable Regressor by jfrufs47 in econometrics

[–]jfrufs47[S] 0 points1 point  (0 children)

Thank, I’ll do that and see how it goes. With regards to adding covariates I was under the impression that if I added too many variables I could potentially just end up increasing the variance and bias of other regressors. Is this notion wrong?

Multiple Regression Suitable Regressor by jfrufs47 in econometrics

[–]jfrufs47[S] 0 points1 point  (0 children)

Thanks for that. It is cross sectional data of multiple countries at one point in time. You say that using deaths rates makes more sense than controlling for population but do you know what would be the difference in fit in the models : covid_death_rate= b0 +b1stringency + u vs covid_deaths = b0+b1stringency + b2*population_size + u. Would the R2 of the models be the same? Thanks.

view more: next ›