ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions based on multiplicative or additive errors; within groups fixed effects Poisson for panel data; GMM est...

This study examined the nexus between capital structure and financial sustainability of 28 listed non-financial firms in Ghana. Panel data for period 2008 to 2019 was used analysis. From results, panel studied heterogeneous cross-sectionally dependent. In addition, variables investigated were first-differenced stationary cointegrated long term. The elasticities predictors explored via common co...

This paper investigates the effect of local property tax for businesses on firm performance by using a panel Italian manufacturing firms. To identify this effect, we implement pairwise spatial-differenced estimator and exploit exogenous variation in rates caused political alignment central governments. We find that business taxation has sizeable negative impact equipment, more volatile part tan...

The existing differenced estimators of error variance in nonparametric regression are interpreted as kernel estimators, and some requirements for a ‘‘good’’ estimator of error variance are specified. A new differenced method is then proposed that estimates the errors as the intercepts in a sequence of simple linear regressions and constructs a variance estimator based on estimated errors. The n...

It is widely believed that the inclusion of lagged dependent variables in a panel data model necessarily renders the Random Effects (RE) estimators, based on OLS applied to the quasi-differenced variables, inconsistent. It is shown here that this belief is incorrect under the usual assumption made in this context — i.e., that the other regressors are strictly exogenous. This result follows from...

Percival and Walden (2002) present a wavelet methodology of the least squaresestimation of the long memory parameter for fractionally differenced processes. Wesuggest that the general idea of using wavelets for estimating long memory could beused for the estimation of long memory in time series perturbed by noise. One prominentexample thereof is the time series of log-Garman-Kla...

In recent years, fractionally-differenced processes have received a great deal of attention due to their flexibility in financial applications with long-memory. This paper revisits the class of generalized fractionally-differenced processes generated by Gegenbauer polynomials and the ARMA structure (GARMA) with both the long-memory and time-dependent innovation variance. We establish the existe...

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first differenced model. One is composed of a semiparametric GMM esti...

First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling because differencing eliminates fixed effects and, in the case of a unit root, differencing transforms the data to stationarity, thereby addressing both incidental parameter problems and the possible effects of nonstationarity. This paper draws attention to certain pathologies th...

China has witnessed a fast economic growth in the recent two decades. However, the heavy energy exploitation seems to show a negative relation to regional economic growth. Thus, the issue is whether the energy production is a curse or blessing for the regional economic growth in China. The present study deploys a comprehensive approach to rigorously prove the validity of a proposed panel data m...