| PDMIFGLM {PDMIF} | R Documentation |
PDMIFGLM
Description
This function estimates heterogeneous panel data models with interactive effects through generalised linear models.
Usage
PDMIFGLM(X, Y, FAMILY, Nfactors, Maxit = 100, tol = 0.001)
Arguments
X |
The (NT) times p design matrix, without an intercept where N=number of individuals, T=length of time series, p=number of explanatory variables. |
Y |
The T times N panel of response where N=number of individuals, T=length of time series. |
FAMILY |
A description of the error distribution and link function to be used in the model just like in glm functions. |
Nfactors |
A pre-specified number of common factors. |
Maxit |
A maximum number of iterations in optimization. Default is 100. |
tol |
Tolerance level of convergence. Default is 0.001. |
Value
A list with the following components:
Coefficients: The estimated heterogeneous coefficients.
Lower05: Lower end (5%) of the 90% confidence interval of the regression coefficients.
Upper95: Upper end (95%) of the 90% confidence interval of the regression coefficients.
Factors: The estimated common factors across groups.
Loadings: The estimated factor loadings for the common factors.
Predict: The conditional expectation of response variable.
pval: p-value for testing hypothesis on heterogeneous coefficients.
Se: Standard error of the estimated regression coefficients.
References
Ando, T., Bai, J. and Li, K. (2021) Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity, Journal of Econometrics.
Examples
fit <- PDMIFGLM(data2X,data2Y,binomial(link=logit),2,20,0.5)