PDMIFQUANTILE: PDMIFQUANTILE

In PDMIF: Fits Heterogeneous Panel Data Models

PDMIFQUANTILE

Description

This function estimates heterogeneous quantile panel data models with interactive effects.

Usage

PDMIFQUANTILE(X, Y, TAU, 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.
TAU	A pre-specified quantile point.
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 quantile point under a given tau.

• 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. and Bai, J. (2020) Quantile co-movement in financial markets Journal of the American Statistical Association.

Examples

fit <- PDMIFQUANTILE(data7X,data7Y,0.95,2,10,0.8)

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