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ExpEnd, A Gauss programme for non-linear GMM estimation of exponential models with endogenous regressors for cross section and panel data

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  • Frank Windmeijer

    () (Institute for Fiscal Studies and University of Bristol)

Abstract

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 estimation using quasi-differenced moment conditions eliminating unobserved heterogeneity and allowing for predetermined or endogenous regressors; and quasi-differenced GMM for a dynamic linear feedback model. This manual describes in detail the various estimators, the data and software requirements, and the programme commands. The programme can be downloaded here [zip file, 435KB].

Suggested Citation

  • Frank Windmeijer, 2002. "ExpEnd, A Gauss programme for non-linear GMM estimation of exponential models with endogenous regressors for cross section and panel data," CeMMAP working papers CWP14/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:14/02
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    File URL: http://cemmap.ifs.org.uk/wps/cwp0214.pdf
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    Cited by:

    1. Hyytinen, Ari & Takalo, Tuomas, 2004. "Multihoming in the market for payment media : evidence from young Finnish consumers," Research Discussion Papers 25/2004, Bank of Finland.
    2. Kaiser, Ulrich & Kongsted, Hans Christian & Rønde, Thomas, 2015. "Does the mobility of R&D labor increase innovation?," Journal of Economic Behavior & Organization, Elsevier, vol. 110(C), pages 91-105.
    3. Andrés Romeu, 2004. "ExpEnd: GAUSS code for panel count-data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(3), pages 429-434.
    4. Davis, James C. & Henderson, J. Vernon, 2008. "The agglomeration of headquarters," Regional Science and Urban Economics, Elsevier, vol. 38(5), pages 445-460, September.
    5. Hyytinen Ari & Takalo Tuomas, 2009. "Consumer Awareness and the Use of Payment Media: Evidence from Young Finnish Consumers," Review of Network Economics, De Gruyter, vol. 8(2), pages 1-25, June.
    6. Shiferaw Gurmu & Fidel Pérez-Sebastián, 2008. "Patents, R&D and lag effects: evidence from flexible methods for count panel data on manufacturing firms," Empirical Economics, Springer, vol. 35(3), pages 507-526, November.
    7. Frank Windmeijer, 2006. "GMM for panel count data models," Bristol Economics Discussion Papers 06/591, Department of Economics, University of Bristol, UK.
    8. Carrión-Flores, Carmen E. & Innes, Robert & Sam, Abdoul G., 2013. "Do voluntary pollution reduction programs (VPRs) spur or deter environmental innovation? Evidence from 33/50," Journal of Environmental Economics and Management, Elsevier, vol. 66(3), pages 444-459.
    9. Jože P. Damijan & Sašo Polanec & Janez Prašnikar, 2007. "Outward FDI and Productivity: Micro-evidence from Slovenia," The World Economy, Wiley Blackwell, vol. 30(1), pages 135-155, January.
    10. Wang, Ning & Hagedoorn, John, 2014. "The lag structure of the relationship between patenting and internal R&D revisited," Research Policy, Elsevier, vol. 43(8), pages 1275-1285.
    11. Basu, Sandip & Phelps, Corey & Kotha, Suresh, 2011. "Towards understanding who makes corporate venture capital investments and why," Journal of Business Venturing, Elsevier, vol. 26(2), pages 153-171, March.
    12. Grönqvist, Erik, 2006. "(M)oral Hazard?," SSE/EFI Working Paper Series in Economics and Finance 642, Stockholm School of Economics.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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