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Suppose I have a 2-way unit and time fixed effects model: $$y_{it} = a_i + b_t +X_{it}\beta + \epsilon$$ I collect data on a set of units $A$ and $B$, where $A$ and $B$ are disjoint. The data was coll …

I would like to estimate a logistic regression model where the target variable $y_{it}$ is grouped. … continuous and the OLS model is transformed as: $$y_{it} - \bar y_i - \bar y_t + \bar y_{it} = (\bf x_{it} - \bar x_i - \bar x_t + \bar x_{it})\bf \beta$$ I don't see how this can be used in logistic regression …

Suppose I have this model: $$y = \beta_1x + \beta_2x^2 + \epsilon$$ I would like to fit it using OLS. In my data the correlation between $x$ and $x^2$ is $0.91$. After I rescale $x$ to zero mean and u …

How do I set up the dummies to fit a linear regression model? …

Is there any benefit in including these units in the regression? …

Can I still use fixed effects regression? …

I need to calculate the DFBETA statistics for a logistic regression. The number of columns in the $X$ matrix is $3000$ and the number of records is in the millions. …

Suppose I am building a linear regression model $$y = X + Z + \epsilon$$ where $X$ are covariates, $Z$ is a confounder, and $\epsilon$ is noise. …

I am running a logistic regression and one of my control variables is categorical with $100$ categories. This leads to problems because some categories have $3$ data points, out of tens of thousands. …

Suppose I run a regularized regression model such as Lasso. For simplicity let's say it's a linear model. …

I've noticed a phenomenon with logistic regression: when the probability of success is small and number of trials large the AIC consistently selects the wrong models. …

Take the log of $y$ and use a linear model, or use logistic regression. … Logistic regression is the more theoretically appropriate model but estimation can lead to problems (example) that the linear method doesn't have. …

The first is to take the log of $y$ and apply OLS regression. The second is to apply logistic regression to $y$ directly. … I noticed that one theoretically important coefficient, call it $x_0$, is highly significant in both models but in OLS it is estimated as a strong negative effect and in logistic regression it is a small …

Suppose over many years I collect data $X$ on a quantity of interest $y$ and some control variables $Z$. I fit an OLS model $$y = \beta X + \delta Z + \epsilon $$ and use the coefficients $\beta$ and …

Then I run OLS regression with some covariates that were not necessarily included in the propensity score model. Do I need to adjust the standard errors in some fashion? …