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Finite-sample instrumental variables inference using an asymptotically pivotal statistic

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  • Bekker, Paul A.
  • Kleibergen, Frank

    (Groningen University)

Abstract

This paper is concerned with investigating the role of accounting practices in radical change processes. The institutional framework has been taken as a starting point in investigating these processes. The research has been carried out at the Dutch Railways. This company was forced by the Dutch government to change from a public company into a private company. This decision by the Dutch Government has had radical consequences for Dutch Railways’ position in the (rail) transport market and for the way of managing the company. The research focuses on the processes in which the company has changed its template as a public company into a profit-oriented template. This paper examines the interaction of accounting practices with the environmental and organisational context. Emphasis is placed on how these mutual processes of interaction change internal and external positioning, create new visibilities, transform perspectives on organisational activities and performance and modify conditions for organisational change. Existing institutional concepts regarding change processes are evaluated in the light of the case findings and building blocks are developed for a comprehensive change framework.

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File URL: http://irs.ub.rug.nl/ppn/228125065
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Bibliographic Info

Paper provided by University of Groningen, Research Institute SOM (Systems, Organisations and Management) in its series Research Report with number 01F38.

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Date of creation: 2001
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Handle: RePEc:dgr:rugsom:01f38

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References

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  1. Frank Kleibergen, 2002. "Pivotal Statistics for Testing Structural Parameters in Instrumental Variables Regression," Econometrica, Econometric Society, Econometric Society, vol. 70(5), pages 1781-1803, September.
  2. Douglas Staiger & James H. Stock, 1997. "Instrumental Variables Regression with Weak Instruments," Econometrica, Econometric Society, Econometric Society, vol. 65(3), pages 557-586, May.
  3. Nelson, C. & Startz, R., 1988. "Some Furthere Results On The Exact Small Sample Properties Of The Instrumental Variable Estimator," Discussion Papers in Economics at the University of Washington, Department of Economics at the University of Washington 88-06, Department of Economics at the University of Washington.
  4. Zivot, Eric & Startz, Richard & Nelson, Charles R, 1998. "Valid Confidence Intervals and Inference in the Presence of Weak Instruments," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1119-46, November.
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Cited by:
  1. Frank Kleibergen, 2004. "Expansions of GMM statistics that indicate their properties under weak and/or many instruments and the bootstrap," Econometric Society 2004 North American Summer Meetings, Econometric Society 408, Econometric Society.
  2. D.S. Poskitt & C.L. Skeels, 2005. "Small Concentration Asymptotics and Instrumental Variables Inference," Department of Economics - Working Papers Series, The University of Melbourne 948, The University of Melbourne.
  3. Bekker, Paul A. & Lawford, Steve, 2008. "Symmetry-based inference in an instrumental variable setting," Journal of Econometrics, Elsevier, Elsevier, vol. 142(1), pages 28-49, January.
  4. Dufour, Jean-Marie & Taamouti, Mohamed, 2007. "Further results on projection-based inference in IV regressions with weak, collinear or missing instruments," Journal of Econometrics, Elsevier, Elsevier, vol. 139(1), pages 133-153, July.
  5. Whitney Newey & Frank Windmeijer, 2005. "GMM with many weak moment conditions," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP18/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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