| PDMIFCLUSTGLM {PDMIF} | R Documentation |
PDMIFCLUSTGLM
Description
Under a pre-specified number of groups and the number of common factors, this function implements clustering for N individual units by nonlinear heterogeneous panel data models with interactive effects. Exponential family of distributions are used Each of individuals in the group are subject to the group-specific unobserved common factors.
Usage
PDMIFCLUSTGLM(X, Y, FAMILY, NLfactors, 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. |
NLfactors |
A pre-specified number of factors in each groups (see example). |
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:
Label: The estimated group membership for each of the individuals.
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.
GroupFactors: The estimated group-specific factors.
GroupLoadings: The estimated factor loadings for each group.
pval: p-value for testing hypothesis on heterogeneous coefficients.
Se: Standard error of the estimated regression coefficients.
References
Ando, T. and Bai, J. (2016) Panel data models with grouped factor structure under unknown group membership Journal of Applied Econometrics, 31, 163-191.
Ando, T. and Bai, J. (2017) Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures. Journal of the American Statistical Association, 112, 1182-1198.
Examples
fit <- PDMIFCLUSTGLM(data6X,data6Y,binomial(link=logit),c(1,1,1),3,0.5)