For how many factors am i allowed to adjust in linear regression? 

In linear regression, the number of factors that can be adjusted depends on the specific model and the assumptions made. There are different approaches to regression analysis that allow for adjustment of multiple factors. For example, Lin [2013] proposed an ordinary least squares adjusted estimator for the average treatment effect in completely randomized experiments, which holds for a fixed dimension p of the covariates. Moon and Weidner studied the least squares estimator in a linear panel regression model with unknown number of factors, showing that the limiting distribution of the estimator is independent of the number of factors used in estimation as long as this number is not underestimated. Therefore, the number of factors that can be adjusted in linear regression depends on the specific model and estimation method used .

Linear regression for panel with unknown number of factors as interactive fixed effects

The paper does not explicitly state the maximum number of factors that can be adjusted in linear regression.

Common Adjustments to Multiple Regressions

The paper does not explicitly state the maximum number of factors that can be adjusted in linear regression.

Linear Regression

The paper does not explicitly mention the maximum number of factors that can be adjusted in linear regression. The paper discusses multiple linear regression with multiple predictor variables, indicating that more than one factor can be adjusted.

Regression adjustment in randomized experiments with a diverging number of covariates.

The paper does not explicitly state the maximum number of factors that can be adjusted in linear regression.