TY  - BOOK
AU  - Chipman,John S.
TI  - Advanced econometric theory 
T2  - Routledge advanced texts in economics and finance
SN  - 9780415326292 (hbk.)
PY  - 2011///
CY  - New York 
PB  - Routledge
KW  - Econometrics
N1  - Includes bibliographical references and index; List of figures and tables	xii
 	Preface	xiii
1.	Multivariate analysis and the linear regression model	1
1.1.	Introduction	1
1.2.	Existence of a solution to the normal equation	7
1.3.	The concept of wide-sense conditional expectation	10
1.4.	Conditional expectation with normal variables	14
1.5.	The relation between wide-sense and strict-sense conditional expectation	15
1.6.	Conditional means and minimum mean-square error	17
1.7.	Bayes estimation	20
1.8.	The relation between Bayes and Gauss---Markov estimation in the case of a single independent variable	23
1.9.	Exercises	27
2.	Least-squares and Gauss---Markov theory	30
2.1.	Least-squares theory	30
2.2.	Principles of estimation	31
2.3.	The concept of a generalized inverse of a matrix	33
2.4.	The matrix Cauchy---Schwarz inequality and an extension	35
2.5.	Gauss---Markov theory	37
2.6.	The relation between Gauss---Markov and least-squares estimators	41
2.7.	Minimum-bias estimation	49
2.8.	Multicollinearity and the imposition of dummy linear restrictions	51
2.9.	Specification error	55
2.10.	Exercises	60
3.	Multicollinearity and reduced-rank estimation	65
3.1.	Introduction	65
3.2.	Singular-value decomposition of a matrix	65
3.3.	The condition number of a matrix	68
3.4.	The Eckart---Young theorem	70
3.5.	Reduced-rank estimation	81
3.6.	Exercises	86
4.	The treatment of linear restrictions	88
4.1.	Estimation subject to linear restrictions	88
4.2.	Linear aggregation and duality	92
4.3.	Testing linear restrictions	101
4.4.	Reduction of mean-square error by imposition of linear restrictions	106
4.5.	Uncertain linear restrictions	108
4.6.	Properties of the generalized ridge estimator	109
4.7.	Comparison of restricted and generalized ridge estimators	112
4A.	Appendix (to Section 4.4): Guide to the computation of percentage points of the noncentral F distribution	115
4.8.	Exercises	122
5.	Stein estimation	126
5.1.	Stein's theorem and the regression model	126
5.2.	Lemmas underlying the James---Stein theorem	132
5.3.	Some further developments of Stein estimation	138
5.4.	Exercises	141
6.	Autocorrelation of residuals - 1	143
6.1.	The first-order autoregressive model	143
6.2.	Efficiency of trend estimation: the ordinary least-squares estimator	147
6.3.	Efficiency of trend estimation: the Cochrane---Orcutt estimator	154
6.4.	Efficiency of trend estimation: the Prais---Winsten weighted-difference estimator	157
6.5.	Efficiency of trend estimation: the Prais---Winsten first-difference estimator	161
6.6.	Discussion of the literature	162
6.7.	Exercises	165
7.	Autocorrelation of residuals - 2	167
7.1.	Anderson models	167
7.2.	Testing for autocorrelation: Anderson's theorem and the Durbin---Watson test	177
7.3.	Distribution and beta approximation of the Durbin---Watson statistic	189
7.4.	Bias in estimation of sampling variances	196
7.5.	Exercises	200
8.	Simultaneous-equations estimation	202
8.1.	The identification problem	202
8.2.	Anderson and Rubin's "limited-information maximum-likelihood" (LIML) method, 1: the handling of linear restrictions	210
8.3.	Anderson and Rubin's "limited-information maximum-likelihood" method, 2: constrained maximization of the likelihood function	215
8.4.	The contributions of Basmann and Theil	223
8.5.	Exact properties of simultaneous-equations estimators	238
8.6.	Approximations to finite-sample distributions	251
8.7.	Recursive models	268
8.8.	Exercises	283
9.	Solutions to the exercises	287
9.1.	Chapter 1	287
9.2.	Chapter 2	294
9.3.	Chapter 3	304
9.4.	Chapter 4	309
9.5.	Chapter 5	318
9.6.	Chapter 6	323
9.7.	Chapter 7	329
9.8.	Chapter 8	334
 	Notes	349
 	Bibliography	357
 	Index
N2  - When learning econometrics, what better way than to be taught by one of its masters. 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
ER  -