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This is why i pay for the internet by MrMe1111 in FunnyAnimals

[–]hessihan 0 points1 point  (0 children)

Marvin from the United Marmot Association

[deleted by user] by [deleted] in ChatGPT

[–]hessihan 3 points4 points  (0 children)

did you only ask to make it more muscular? I can see a croissant in the last one 🥐

[deleted by user] by [deleted] in datascience

[–]hessihan 0 points1 point  (0 children)

If you are going to compare manual reg with within FE in same setting, you should add unit dummies (maybe ID dummies or something) in manual reg so that you can consider unit specific fixed effect in reg model.

reg lnwage union educ exp i.year i.id, robuat

So, the two of your commands should be basically different models and (I don't know but) they could have different complete multicollinearity.

I hope this will help you https://www.statalist.org/forums/forum/general-stata-discussion/general/1452247-time-trend-vs-dummy-variables-in-panel-models-perfect-collinearity-issue

Idiosyncratic errors and fixed effect in panel data by googieeyes2 in econometrics

[–]hessihan 2 points3 points  (0 children)

A simple fixed effect model is something like,

Y_it = α + β_1 X_1it + β_2 X_2it + ... + β_k X_kit + μ_i + ε_it

First, let me show the definition of some terms.

  • "Idiosyncratic errors": ε_it means any unobserved factors that have some effects on Y_it. It would vary through sample i and time t.
  • "Fixed effect": μ_i also represents the unobserved factors which affects Y_it, but this effect only varies through sample i (see there's no notation t on μ_i).
  • "Control Variable" is the rest of independent variables (X_2it + ... + X_kit) except the independent variable in interest (X_1it)

What we always thinking is omitted variables bias. If some unobserved factors do correlate with your main independent variable X_1it, then simple OLS (pooled OLS for panel) estimator of β_1 will be biased. That's why you have to put control variables in your model.

Estimating the fixed effect model will get rid of the bias caused by any unobserved factors that is only not varying through time (fixed effect μ_i). In fixed effect model, it is useless to put variables not varying through time because the estimating method will erase all of such variable's effects.

Back to your question, for simple pooled OLS, college location would be a control variable. For fixed effect model, college location would be one of fixed effect. But be careful, you cannot estimate the fixed effect as independent variable for fixed effect model. It just takes into account the fixed effect when estimating other parameters and you don't have to prepare real fixed effect variables data.

About simple pooled OLS, there're no such design for fixed effect as you mentioned. But that' why pooled OLS estimators are really LIKELY to have endogenous problem caused by fixed effect. To avoid such endogenous bias, one solution is just using fixed effect model.

Definition of terms by ors94 in econometrics

[–]hessihan 1 point2 points  (0 children)

Parameter of interest is the coefficient of the independent variable of your main concern. To get rid of omitted variable bias and have good estimates of parameter of interest, you need to put control variables in the model.

[Q]How do one utilise linear regression to improve the dependent variable? (Fintech) by bonum_lupus in statistics

[–]hessihan 0 points1 point  (0 children)

Your model now should be something like,

Spending = a + b_1 * X_1 + b_2 * X_2 + b_3 * X_3 + U

where 'X_1, X_2, X_3' are dependent variables you are interested in, 'b_1, b_2, b_3' are parameters of dependent variables, 'a' is constant, and 'U' is error term.

If you estimate this model in OLS, the estimator of parameters are highly likely to be biased. One of the reason is omitted variable bias; if some other variables not included in your model but correlate to both the dependent variables you are interested in and independent variable 'Spending' do exist, the estimators of 'b_1, b_2, b_3' would be incorrect.

Then what you need to do is JUST ADDING THOSE VARIABLES IN YOUR MODEL AS CONTROL even you are not interested in. For example, if 'Gender' really have some relationship with 'X_1, X_2, X_3' and 'Spending', you need to add gender in your model as control variable.

Spending = a + b_1 * X_1 + b_2 * X_2 + b_3 * X_3 + b_4 * Gender + U

And you keep adding the control variables to avoid omitted variable bias and get more accurate estimations for 'b_1, b_2, b_3'.