estimateMod: Log-t test for convergence

In ConvergenceClubs: Finding Convergence Clubs

Log-t test for convergence

Description

Estimates the log-t regression model proposed by Phillips and Sul (2007, 2009) in order to investigate the presence of convergence by adopting the Andrews estimator of long-run variance (fixed or adaptive bandwidth of the kernel).

Usage

estimateMod(H, time_trim = 1/3, HACmethod = c("FQSB", "AQSB"))

Arguments

H	vector of H values
time_trim	a numeric value between 0 and 1, representing the portion of time periods to trim when running log t regression model. Phillips and Sul (2007, 2009) suggest to discard the first third of the period.
HACmethod	string indicating whether a Fixed Quadratic Spectral Bandwidth (HACmethod="FQSB") or an Adaptive Quadratic Spectral Bandwidth (HACmethod="AQSB") should be used for the truncation of the Quadratic Spectral kernel in estimating the log t regression model with heteroskedasticity and autocorrelation consistent standard errors. The default method is "FQSB".

Details

The following linear model is estimated:

log[H(1)/H(t)] - 2log[log(t)] = α + β log(t) + u(t)

Heteroskedasticity and autocorrelation consistent (HAC) standard errors are used with Quadratic Spectral kernel (Andrews, 1991), If HACmethod="FQSB", a fixed bandwidth parameter is applied, while with HACmethod="AQSB" an adaptive bandwidth parameter is employed.

Value

A named vector containing information about the model used to run the t-test on the units in the club: beta coefficient, standard deviation, t-statistics and p-value.

References

Andrews, D. W., 1991. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica: Journal of the Econometric Society, 817-858.

Phillips, P. C.; Sul, D., 2007. Transition modeling and econometric convergence tests. Econometrica 75 (6), 1771-1855.

Phillips, P. C.; Sul, D., 2009. Economic transition and growth. Journal of Applied Econometrics 24 (7), 1153-1185.

ConvergenceClubs documentation built on June 14, 2022, 1:06 a.m.