PDMIFLING: PDMIFLING

In PDMIF: Fits Heterogeneous Panel Data Models

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)

PDMIF documentation built on March 18, 2022, 7:15 p.m.