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Introduction to the Mathematical and Statistical Foundations of Econometrics

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  • Bierens,Herman J.

Abstract

This book is intended for use in a rigorous introductory PhD level course in econometrics, or in a field course in econometric theory. It covers the measure-theoretical foundation of probability theory, the multivariate normal distribution with its application to classical linear regression analysis, various laws of large numbers, central limit theorems and related results for independent random variables as well as for stationary time series, with applications to asymptotic inference of M-estimators, and maximum likelihood theory. Some chapters have their own appendices containing the more advanced topics and/or difficult proofs. Moreover, there are three appendices with material that is supposed to be known. Appendix I contains a comprehensive review of linear algebra, including all the proofs. Appendix II reviews a variety of mathematical topics and concepts that are used throughout the main text, and Appendix III reviews complex analysis. Therefore, this book is uniquely self-contained.

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Bibliographic Info

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This book is provided by Cambridge University Press in its series Cambridge Books with number 9780521834315 and published in 2005.

Order: http://www.cambridge.org/uk/catalogue/catalogue.asp?isbn=9780521834315
Handle: RePEc:cup:cbooks:9780521834315

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Web page: http://www.cambridge.org

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Cited by:
  1. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2005. "Online Appendix to "Firm-Specific Capital, Nominal Rigidities and the Business Cycle"," Technical Appendices 09-191, Review of Economic Dynamics.
  2. Altig, David & Christiano, Lawrence & Eichenbaum, Martin & Lindé, Jesper, 2004. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," Working Paper Series 176, Sveriges Riksbank (Central Bank of Sweden).
  3. Dahl, Christian M. & Levine, Michael, 2006. "Nonparametric estimation of volatility models with serially dependent innovations," Statistics & Probability Letters, Elsevier, vol. 76(18), pages 2007-2016, December.
  4. Boris E. Bravo-Ureta & Horacio Cocchi & Daniel Solís, 2006. "Output Diversification among Small-Scale Hillside Farmers in El Salvador," IDB Publications 25778, Inter-American Development Bank.
  5. Hanck, Christoph, 2006. "The Error-in-Rejection Probability of Meta-Analytic Panel Tests," Technical Reports 2006,46, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  6. Hanck, Christoph, 2006. "Cross-Sectional Correlation Robust Tests for Panel Cointegration," Technical Reports 2006,44, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  7. Marcin Owczarczuk, 2009. "Maximum Score Type Estimators," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 1(1), pages 7-34, March.
  8. Huang, Bin & Wang, Qihua, 2010. "Semiparametric analysis based on weighted estimating equations for transformation models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2078-2090, October.
  9. Kenneth L. Sørensen & Rune Vejlin, 2012. "Return to Experience and Initial Wage Level: Do Low Wage Workers Catch Up?," Economics Working Papers 2012-02, School of Economics and Management, University of Aarhus.

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