Description Usage Arguments Value
View source: R/cragg_errors3.R
The truncated multivariate normal errors can be generated in one of three ways. 1) we sample the first stage and probit errors first and then resample the linear regression's errors until we have positive values. 2) we resample the vector of all three errors until the linear value is positive. 3) we send all our variables to zero first then resample the full vector, accepting if it lies in Q1. This is the third method.
1 | cragg_errs3(cov, pi, x1, gamma, beta, n, z)
|
cov |
the covariance matrix. This should be untransformed, the terms will be multiplied by the coefficients within the resampling procedure. |
pi |
a vector of coefficients for the first stage regression |
x1 |
your exogenous variables (a dataframe) |
gamma |
a vector of coefficients for the second stage probit |
beta |
a vector of coefficients for the second stage linear regression |
n |
the number of errors to be generated |
z |
your instrument (a dataframe) |
returns a list of your errors and the three generated variables: the endogenous regressor, the censoring variable and the outcome variable
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