| PDMIFLING {PDMIF} | R Documentation |
PDMIFLING
Description
Under a known group membership, this function estimates heterogeneous panel data models with interactive effects. Together with the regression coefficients, this function estimates the unobserved common factor structures both for across/within groups.
Usage
PDMIFLING(X, Y, Membership, NGfactors, 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. |
Membership |
A pre-specified group membership. |
NGfactors |
A pre-specified number of common factors across groups (see example). |
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:
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.
GlobalFactors: The estimated common factors across groups.
GlobalLoadings: The estimated factor loadings for the common factors.
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. (2015) Asset Pricing with a General Multifactor Structure Journal of Financial Econometrics, 13, 556-604.
Examples
fit <- PDMIFLING(data4X,data4Y,data4LAB,2,c(2,2,2),30,0.1)