Description Usage Arguments Value Author(s) References Examples
This function estimates measurement stochastic matrices of discrete proxy variables.
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dat |
A proxy variable data frame list. |
sbar |
A number of discrete types. Default is 2. |
initvar |
A column index of a proxy variable to initialize the EM algorithm. Default is 1. That is, the proxy variable in the first column of "dat" is used for initialization. |
initvec |
This vector defines how to group the initvar to initialize the EM algorithm. |
seed |
Seed. Default is 210313 (birthday of this package). |
tol |
A tolerance for EM algorithm. Default is 0.005. |
maxiter |
A maximum number of iterations for EM algorithm. Default is 200. |
miniter |
A minimum number of iterations for EM algorithm. Default is 10. |
minobs |
Compute likelihood of a proxy variable only if there are more than "minobs" observations. Default is 100. |
maxiter2 |
Maximum number of iterations for "multinom". Default is 1000. |
trace |
Whether to trace EM algorithm progress. Default is FALSE. |
weights |
An optional weight vector |
Returns a list of 5 components :
This is a list of estimated measurement (stochastic) matrices. The k-th matrix is a measurement matrix of a proxy variable saved in the kth column of dat data frame (or matrix). The ij-th element in a measurement matrix is the conditional probability of observing j-th (largest) proxy response value conditional on that the latent type is i.
This is a list of column labels of 'M_param' matrices
This is a list of row labels of 'M_param' matrices. It is simply c(1:sbar).
This is a list of multinomial logit coefficients which were used to compute 'M_param' matrices. These coefficients are useful to compute the likelihood of proxy responses.
This is a type probability matrix of size N-by-sbar. The ij-th entry of this matrix gives the probability of observation i to have type j.
Yujung Hwang, yujungghwang@gmail.com
"Maximum likelihood from incomplete data via the EM algorithm." Journal of the Royal Statistical Society: Series B (Methodological) 39.1 : 1-22. doi: 10.1111/j.2517-6161.1977.tb01600.x
Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution. Journal of Econometrics, 144(1), 27-61. doi: 10.1016/j.jeconom.2007.12.001
The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics. Journal of Econometrics, 200(2), 154-168. doi: 10.1016/j.jeconom.2017.06.002
Identification and Estimation of a Dynamic Discrete Choice Models with Endogenous Time-Varying Unobservable States Using Proxies. Working Paper.
Bounding Omitted Variable Bias Using Auxiliary Data. Working Paper.
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