ExpEnd, A Gauss Programme for Non-Linear GMM Estimation of Exponential Models with Endogenous Regressors for Cross Section and Panel Data

By F. Windmeijer. 2002

55 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/55680e67ba515ca2b35c81c16e70b15bc9c1381f

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Citations in the corpus

Towards Understanding Who Makes Corporate Venture Capital Investments and Why (2011, unknown): “The likelihood of a significant Sargan test increases with the number of instrumented variables (Windmeijer, 2002).”
GMM for Panel Count Data Models (2006, methodology): “For the patents-R&D model, Blundell, Griffith and Windmeijer (2002) consider the economic model Pit = k ³ Rβit + (1− δ)Rβit−1 + (1− δ)2Rβit−2... ´ νi + εit (7) where Pit and Rit are the number of patents and R&D expenditures for firm i at time t respectively, k is a positive constant and R&D…”
Environmental innovation and environmental performance (2010, methodology): “This gives us the multiplicative error Poisson panel model, with endogenous regressors, of Blundell, Griffith and Windmeijer (2002) (see also Windmeijer (2002) and Windmeijer and Santos Silva (1997)).”
Regional Branching and Key Enabling Technologies: Evidence from European Patent Data (2017, methodology): “Windmeijer (2002) has developed a routine working with the Gauss software, to run estimates drawing upon Chamberlain and Wooldridge moment conditions, which instead reports the full set of validation tests.”
Reputations for toughness in patent enforcement: implications for knowledge spillovers via inventor mobility (2009, unknown): “We estimate the GMM models using the GAUSS routine ‘ExpEnd’ of Windmeijer (2002) and moment conditions based on quasi-differencing (Wooldridge, 1997).17 To correct for time-invariant heterogeneity in each dyad, we define the panel using the dyad-level fixed effects.”
The agglomeration of headquarters (2008, methodology): “Covariates are predetermined and orthogonal to uks ,s ≥ t. To estimate the model with instrumental variables, we follow Chamberlain (1992) and Wooldridge (1997) (see also Windmeijer (2002) and Blundell, Griffith and Windmeijer (2000)); and we use a quasi-differenced transformation to eliminate the”