PDMIF (version 0.1.0)

PDMIFGLM: 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

# NOT RUN {
fit <- PDMIFGLM(data2X,data2Y,binomial(link=logit),2,20,0.5)
# }