Does OLS require stationarity?
Clarification: you can use non-stationary data with OLS if the series are cointegrated. However, when doing so you better show that the series are cointegrated indeed, then adjust the parameter covariance matrix accordingly if you need inference. The parameters themselves would be fine.
Is probit nonlinear?
The function is clearly nonlinear and flattens out for large and small values of P/I ratio P / I r a t i o . The functional form thus also ensures that the predicted conditional probabilities of a denial lie between 0 and 1 .
Why do probit / logit models with non stationary data make no sense?
In my opinion, probit/logit models with non stationary data make no sense because you want to fit the right hand side of your equation (that is non stationary) into the lefthand side that is a binary variable. The structure of the time dynamics of your independent variables must be coherent with the dependent ones.
When to use linear probability model vs probit?
1. Linear Probability Model vs. Logit (or Probit) We have often used binary (“dummy”) variables as explanatory variables in regressions. What about when we want to use binary variables as the dependent variable? It’s possible to use OLS: = + +⋯+ + where y is the dummy variable. This is called the linear probability model.
What does possible non-stationarity mean for OLS estimates?
Regarding non-stationarity, it is not covered under the OLS assumptions, so OLS estimates will no longer be BLUE if your data are non-stationary. In short, you do not want that.
Do you use robust standard errors for stationarity?
All are lagged (-1). I am using robust standard errors, which should be consistent with heteroskedasticity. However, for example loans to GDP or NFA/GDP are not stationarity (panel test). Does this matter? I have not seen any paper testing for stationarity performing logit/probit. For me it is also intuitive that it does not matter.