LEADER 05568oam 2200721I 450 001 9910969047003321 005 20251117084904.0 010 $a1-134-34044-3 010 $a1-283-60655-0 010 $a9786613919007 010 $a1-134-34045-1 010 $a0-203-18075-5 024 7 $a10.4324/9780203180754 035 $a(CKB)2670000000242434 035 $a(EBL)1024466 035 $a(OCoLC)811505523 035 $a(SSID)ssj0000747504 035 $a(PQKBManifestationID)11453726 035 $a(PQKBTitleCode)TC0000747504 035 $a(PQKBWorkID)10704627 035 $a(PQKB)11225638 035 $a(MiAaPQ)EBC1024466 035 $a(Au-PeEL)EBL1024466 035 $a(CaPaEBR)ebr10603870 035 $a(CaONFJC)MIL391900 035 $a(OCoLC)815477937 035 $a(EXLCZ)992670000000242434 100 $a20180706d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdvanced econometric theory /$fJohn S. Chipman 205 $a1st ed. 210 1$aAbingdon, Oxon :$cRoutledge,$d2011. 215 $a1 online resource (409 p.) 225 1 $aRoutledge advanced texts in economics and finance ;$v14 300 $aDescription based upon print version of record. 311 08$a0-415-32630-3 311 08$a0-415-32629-X 320 $aIncludes bibliographical references and index. 327 $aAdvanced Econometric Theory; Copyright; Contents; List of figures and tables; Preface; 1 Multivariate analysis and the linear regression model; 1.1 Introduction; 1.2 Existence of a solution to the normal equation; 1.3 The concept of wide-sense conditional expectation; 1.4 Conditional expectation with normal variables; 1.5 The relation between wide-sense and strict-sense conditional expectation; 1.6 Conditional means and minimum mean-square error; 1.7 Bayes estimation; 1.8 The relation between Bayes and Gauss-Markov estimation in the case of a single independent variable; 1.9 Exercises 327 $a2 Least-squares and Gauss-Markov theory2.1 Least-squares theory; 2.2 Principles of estimation; 2.3 The concept of a generalized inverse of a matrix; 2.4 The matrix Cauchy-Schwarz inequality and an extension; 2.5 Gauss-Markov theory; 2.6 The relation between Gauss-Markov and least-squares estimators; 2.7 Minimum-bias estimation; 2.8 Multicollinearity and the imposition of dummy linear restrictions; 2.9 Specification error; 2.10 Exercises; 3 Multicollinearity and reduced-rank estimation; 3.1 Introduction; 3.2 Singular-value decomposition of a matrix; 3.3 The condition number of a matrix 327 $a3.4 The Eckart-Young theorem3.5 Reduced-rank estimation; 3.6 Exercises; 4 The treatment of linear restrictions; 4.1 Estimation subject to linear restrictions; 4.2 Linear aggregation and duality; 4.3 Testing linear restrictions; 4.4 Reduction of mean-square error by imposition of linear restrictions; 4.5 Uncertain linear restrictions; 4.6 Properties of the generalized ridge estimator; 4.7 Comparison of restricted and generalized ridge estimators; 4A Appendix (to Section 4.4): Guide to the computation of percentage points of the noncentral F distribution; 4.8 Exercises; 5 Stein estimation 327 $a5.1 Stein's theorem and the regression model5.2 Lemmas underlying the James-Stein theorem; 5.3 Some further developments of Stein estimation; 5.4 Exercises; 6 Autocorrelation of residuals - 1; 6.1 The first-order autoregressive model; 6.2 Efficiency of trend estimation: the ordinary least-squares estimator; 6.3 Efficiency of trend estimation: the Cochrane-Orcutt estimator; 6.4 Efficiency of trend estimation: the Prais-Winsten weighted-difference estimator; 6.5 Efficiency of trend estimation: the Prais-Winsten first-difference estimator; 6.6 Discussion of the literature; 6.7 Exercises 327 $a7 Autocorrelation of residuals - 27.1 Anderson models; 7.2 Testing for autocorrelation: Anderson's theorem and the Durbin-Watson test; 7.3 Distribution and beta approximation of the Durbin-Watson statistic; 7.4 Bias in estimation of sampling variances; 7.5 Exercises; 8 Simultaneous-equations estimation; 8.1 The identification problem; 8.2 Anderson and Rubin's "limited-information maximum-likelihood" (LIML) method, 1: the handling of linear restrictions; 8.3 Anderson and Rubin's "limited-information maximum-likelihood" method, 2: constrained maximization of the likelihood function 327 $a8.4 The contributions of Basmann and Theil 330 $aWhen learning econometrics, what better way than to be taught by one of its masters. In this significant new volume, John Chipman, the eminence grise of econometrics, presents his classic lectures in econometric theory.Starting with the linear regression model, least squares, Gauss-Markov theory and the first principals of econometrics, this book guides the introductory student to an advanced stage of ability. The text covers multicollinearity and reduced-rank estimation, the treatment of linear restrictions and minimax estimation. Also included are chapters on the autocorr 410 0$aRoutledge advanced texts in economics and finance. 606 $aEconometrics 606 $aEconomics, Mathematical 615 0$aEconometrics. 615 0$aEconomics, Mathematical. 676 $a330.015195 700 $aChipman$b John Somerset$f1926-2022,$01439944 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910969047003321 996 $aAdvanced econometric theory$94498025 997 $aUNINA