I am trying to run a Fama Macbeth analysis in R, where I am using the 'pmg' function with the following code:
Fpmg1 <- pmg(ret ~ HML_OBS + SMB + Mktrf + HML, Analysis4_Weighted, index = c("permno")) summary(Fpmg1)
I currently have 1,354,623 entries and 11 total columns. I get the below output where the estimates for my coefficients are NA.
Mean Groups model Call: pmg(formula = ret ~ HML_OBS + SMB + Mktrf + HML, data = Analysis4_Weighted, index = c("date", "permno")) Unbalanced Panel: n = 295, T = 3567-6287, N = 1349058 Residuals: Min. 1st Qu. Median Mean 3rd Qu. Max. -1.065356 -0.077703 -0.008573 0.000000 0.060437 19.741368 Coefficients: Estimate Std. Error z-value Pr(>|z|) (Intercept) 0.0110395 0.0034105 3.237 0.001208 ** HML_OBS NA NA NA NA SMB NA NA NA NA Mktrf NA NA NA NA HML NA NA NA NA --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Total Sum of Squares: 50764 Residual Sum of Squares: 45906 Multiple R-squared: 0.0957
I have sorted on the following before running the model:
Analysis4_Weighted <- Analysis4_Weighted %>% dplyr::filter(!is.na(HML_OBS)) Analysis4_Weighted <- Analysis4_Weighted %>% dplyr::filter(!is.na(ret)) Analysis4_Weighted <- Analysis4_Weighted %>% group_by(date) %>% dplyr::filter(n() > 10)
Do you know why I do not get any coefficient estimates?
My data consists of various returns on different stocks in a long time period, and I trying to test the coefficients' ability to predict stock returns over the period across various stocks.