Magnus katyshev peresetsky econometrics introductory course. Econometrics. Initial course. Magnus Ya.R., Katyshev P.K., Peresetsky A.A.

6th ed., revised. and additional - M.: Delo, 2004. - 576 p.

The textbook contains a systematic presentation of the foundations of econometrics and is written on the basis of lectures that the authors gave for a number of years in the Russian economic school And high school economy. Linear regression models (least squares, hypothesis testing, heteroscedasticity, error autocorrelation, model specification) are studied in detail. Separate chapters are devoted to systems of simultaneous equations, the maximum likelihood method in regression models, models with discrete and limited dependent variables.

Three new chapters have been added to the sixth edition of the book. The Panel Data chapter completes the book to complete list topics traditionally included in modern basic econometrics courses. The chapters "Preliminary testing" and "Econometrics of financial markets" have also been added, which will be useful for those who are interested in theoretical and applied aspects of econometrics, respectively. The number of exercises has been significantly increased. Included are exercises with real data available to the reader on the book's website.

For students, graduate students, teachers, as well as specialists in applied economics and finance

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Table of contents
Opening remarks 10
Preface to the first edition 13
Preface to the third edition 18
Preface to the sixth edition 23
1. Introduction 26
1.1. Models 26
1.2. Model types 28
1.3. Data types 30
2. Paired regression model 32
2.1. Curve Fitting 32
2.2. Least Squares (OLS) 34
2.3. Linear regression model with two variables 38
2.4. Gauss-Markov theorem. Estimation of error variance a2 41
2.5. Statistical Properties of LSM-Estimates of Regression Parameters. Hypothesis testing b = bo- Confidence intervals for regression coefficients 46
2.6. Analysis of the variation of the dependent variable in the regression. Determination coefficient R2 51
2.7. Maximum Likelihood Estimation of Regression Coefficients 55
Exercise 58
3. Multiple regression model 67
3.1. Main hypotheses 68
3.2. Least square method. Gauss-Markov theorem 69
3.3. Statistical Properties of OLS Estimates 72
3.4. Analysis of the variation of the dependent variable in the regression. R2 coefficients and adjusted R^, 74
3.5. Hypothesis testing. Confidence intervals and confidence regions 78"
Exercise 88
4. Various aspects of multiple regression 108
4.1. Multicollinearity 109;
4.2. Dummy variables 112
4.3. Partial correlation 118
4.4. Model 124 specification
Exercise 135
5. Some Generalizations of Multiple Regression 148
5.1. Stochastic regressors 149
5.2. Generalized Least Squares.... 154
5.3. Affordable Generalized Least Squares 160
Exercises 163
6. Heteroskedasticity and time correlation 167
6.1. Heteroskedasticity 168
6.2. Time correlation 184
Exercises 192
7. Forecasting in regression models 204
7.1. Unconditional Forecasting 205
7.2. Conditional Prediction 208
7.3. Forecasting in the presence of autoregressive errors 209
Exercises 211
8 . Instrumental variables 212
8.1. Consistency of estimates obtained using instrumental variables 213
8.2. Influence of measurement errors 214
8.3. Two-Step Least Squares.... 215
8.4. Houseman test 217
Exercise 218
9. Systems of regression equations 220
3.1. Externally unrelated equations 221
9.1. Systems of Simultaneous Equations 224
Exercise 241
10. Maximum likelihood method in regression models 244
10.1. Introduction 245
10.2. Mathematical apparatus 246
10.3. Maximum Likelihood Estimation of the Parameters of a Multivariate Normal Distribution. . 248
10.4. Properties of maximum likelihood estimates. 249
10.5. Maximum Likelihood Estimation in a Linear Model 250
10.6. Hypothesis testing in a linear model, I 253
10.7. Hypothesis testing in a linear model, II 257
10.8. Nonlinear Constraints 258
Exercises 260
11. Time series 264
11.1. Distributed lag models 266
11.2. Dynamic Models 268
11.3. Unit roots and cointegration 276
11.4 Box-Jenkins Models (ARIMA) 28
11.5. GARCH models 3
3J exercises
12. Discrete dependent variables and censored samples 3
12.1. Binary and multiple choice models... 3!
12.2. Models with truncated and censored samples 3.
Exercise 3;
13. Panel data 31
13.1 Introduction 3
13.2. Designations and basic models 3
13.3. Fixed effect model 3
13.4. Model with random effect 31
13.5. Z1 fit quality
13.6. Model selection 3"
13.7. Dynamic models 3
13.8. Binary Choice Models with Panel Data 3
13.9. Generalized method of moments 3
Exercise 39
14. Pretesting: Introduction 39
14.1. Introduction 3!
14.2. Problem Statement 40
14.3. Main result 40"
14.4. Pretest estimate $4
14.5. WALS-score 40
14.6. Equivalence Theorem 4
14.7. Pre-Testing and the Understatement Effect 407
14.8. The effect of "understatement". One auxiliary parameter 412
14.9. Model selection: from general to particular and from particular to general 415
14.10. The effect of "understatement". Two auxiliary parameters 419
11. Forecasting and pre-testing 425
.12. Generalizations 429
13. Other matters 432
Exercises 434
15. Econometrics of financial markets 435
11.5.1. Introduction 436
15.2. Hypothesis of the efficiency of the financial market. . . 438
15.3. Securities portfolio optimization 446
15.4. Test for the inclusion of new assets in an effective portfolio 450
15.5. Optimal portfolio in the presence of a risk-free asset 456
15.6. Financial asset valuation models 461
Exercises 471
16. Perspectives on econometrics 472
1.6.1. Introduction 472
16.2. What exactly does an econometrician do? .... 473
16.3. Econometrics and Physics 474
16.4. econometrics and math statistics. . . 475
16.5. Theory and practice 476
16.6. Econometric method 477
16.7. Weak link 480
1.6.8. Aggregation 481
16.9. How to use other 481 works
16.10. Conclusion 482
LA application. Linear Algebra 484
1. Vector space 484
2. Vector space Lp 485
3. Linear dependency 485
4. Linear subspace 486
5. Basis. Dimension 486
6. Linear operators 487
7. Matrices 488
8. Operations with matrices 489
9. Matrix invariants: trace, determinant 492
10. Matrix rank 494
11. inverse matrix 495
12. Systems linear equations 496
13. Eigenvalues and vectors 496
14. Symmetric matrices 498
15. Positive definite matrices 500
16 Idempotent Matrices 502
17. Block matrices 503
18. Kronecker Product 504
19. Differentiation with respect to a vector argument. . 505
Exercises 507
MS application. Probability Theory and Mathematical Statistics 509
1. random variables, random vectors 509
2. Conditional distributions 516
3. Some special distributions 518
4. Multidimensional normal distribution 524
5. Law big numbers. Central limit theorem 528
6 Basic concepts and tasks of mathematical statistics 531
7. Parameter Estimation 533
8. Hypothesis Testing 539
EP application. Overview of econometric packages 542
1. The origin of the packages. Windows version. Graphics 543
2. About some packages 544
3. Experience practical work 546
Application ST. Brief English-Russian dictionary terms 547
TA application. Tables 555
Literature 561
Index 570

Table of contents Foreword Preface to the first edition Preface to the third edition Preface to the sixth edition 1. Introduction 1.1. Models 1.2. Types of models 1.3. Data types 2. Pair regression model 2.1. Curve fitting 2.2. Method of least squares (LSM) 2.3. Linear regression model with two variables 2.4. Gauss-Markov theorem. Estimation of error dispersion a2 2.5. Statistical Properties of LSM-Estimates of Regression Parameters. Hypothesis testing b = bo- Confidence intervals for regression coefficients 2.6. Analysis of the variation of the dependent variable in the regression. R2 determination coefficient 2.7. Maximum Likelihood Estimation of Regression Coefficients Exercises 3. Multiple Regression Model 3.1. Main hypotheses 3.2. Least square method. Gauss-Markov theorem 3.3. Statistical properties of LSM estimates 3.4. Analysis of the variation of the dependent variable in the regression. Coefficients R2 and adjusted R 3.5. Hypothesis testing. Confidence intervals and confidence regions Exercises 4. Various aspects of multiple regression 4.1. Multicollinearity 4.2. Dummy variables 4.3. Partial correlation 4.4. Model specification Exercises 5. Some generalizations of multiple regression 5.1. Stochastic regressors 5.2. Generalized least squares method 5.3. Accessible generalized least squares Exercises 6. Heteroskedasticity and time correlation 6.1. Heteroskedasticity 6.2. Time Correlation Exercises 7. Forecasting in Regression Models 7.1. Unconditional Prediction 7.2. Conditional forecasting 7.3. Forecasting in the presence of autoregressive errors Exercises 8. Instrumental variables 8.1. Consistency of estimates obtained using instrumental variables 8.2. Effect of measurement errors 8.3. Two-Step Least Squares 8.4. Hausman test Exercises 9. Systems of regression equations 3.1. Externally unrelated equations 9.1. Systems of Simultaneous Equations Exercises 10. Maximum Likelihood Method in Regression Models 10.1. Introduction 10.2. Mathematical apparatus 246 10.3. Maximum Likelihood Estimation of Multivariate Normal Distribution Parameters 10.4. Properties of maximum likelihood estimates 10.5. Maximum Likelihood Estimation in a Linear Model 10.6. Hypothesis testing in a linear model, I 10.7. Hypothesis testing in a linear model, II 10.8. Nonlinear constraints Exercises 11. Time series 11.1. Distributed lag models 11.2. Dynamic models 11. 3 Unit roots and cointegration 11.4 Box-Jenkins models (ARIMA) 11.5. GARCH models Exercises 12. Discrete dependent variables and censored samples 12.1. Binary and Multiple Choice Models 12.2. Clipped and censored models Exercises 13. Panel data 13.1 Introduction 13.2. Designations and basic models 13.3. Fixed Effect Model Section 13.4. Random effect model 13.5. Quality of fit 13.6. Model selection 13.7. Dynamic models 13.8. Binary Choice Models with Panel Data 13.9. Generalized method of moments Exercises 14. Preliminary testing: introduction 14.1. Introduction 14.2. Problem statement 14.3. Main result 14.4. Pretest evaluation 14.5. WALS Score 14.6. Equivalence theorem 14.7. Pre-Testing and the Understatement Effect 14.8. The effect of "understatement". One auxiliary parameter 14.9. Choice of model: from general to particular and from particular to general 14.10. The effect of "understatement". Two auxiliary parameters 14.11. Forecasting and preliminary testing 14.12. Generalizations 14.13. Other questions Exercises 15. Econometrics of financial markets 15.1. Introduction 15.2. Hypothesis of financial market efficiency 15.3. Optimization of a portfolio of securities 15.4. Test for inclusion of new assets in an effective portfolio 15.5. Optimal portfolio in the presence of a risk-free asset 15.6. Financial Asset Valuation Models Exercise 16. Econometric Perspectives 1.6.1. Introduction 16.2. What exactly does an econometrician do? 16.3. Econometrics and physics 16.4. Econometrics and mathematical statistics 16.5. Theory and practice 16.6. Econometric method 16.7. Weak link 16.8. Aggregation 16.9. How to use other works 16.10. Conclusion Appendix LA. Linear algebra 1. Vector space 2. Vector space Ln 3. Linear dependence 4. Linear subspace 5. Basis. Dimension 6. Linear operators 7. Matrices 8. Matrix operations 9. Matrix invariants: trace, determinant 10. Matrix rank 11. Inverse matrix 12. Systems of linear equations 13. Eigenvalues and vectors 14. Symmetric matrices 15. Positive definite matrices 16 Idempotent matrices 17. Block matrices 18. Kronecker product 19. Differentiation with respect to a vector argument Exercises Appendix MS. Probability theory and mathematical statistics 1. Random variables, random vectors 2. Conditional distributions 3. Some special distributions 4. Multidimensional normal distribution 5. The law of large numbers. Central limit theorem 6 Basic concepts and problems of mathematical statistics 7. Parameter estimation 8. Hypothesis testing Appendix EP. Overview of econometric packages 1. The origin of packages. Windows version. Graphics 2. About some packages 3. Practical work experience Appendix CT. Brief English-Russian Dictionary of Terms Appendix TA. Tables Literature Index

Name: Econometrics - Beginner course.

The textbook contains a systematic presentation of the foundations of econometrics and is written on the basis of lectures that the authors have given for a number of years at the Russian School of Economics and the Higher School of Economics. Linear regression models (least squares, hypothesis testing, heteroscedasticity, error autocorrelation, model specification) are studied in detail. Separate chapters are devoted to systems of simultaneous equations, the maximum likelihood method in regression models, models with discrete and limited dependent variables.
Three new chapters have been added to the sixth edition of the book. The Panel Data chapter expands the book to a complete list of topics traditionally included in modern basic econometrics courses. The chapters "Preliminary testing" and "Econometrics of financial markets" have also been added, which will be useful for those who are interested in theoretical and applied aspects of econometrics, respectively. The number of exercises has been significantly increased. Included are exercises with real data available to the reader on the book's website.
For students, graduate students, teachers, as well as specialists in applied economics and finance.

Econometrics (along with microeconomics and macroeconomics) is one of the basic disciplines of modern economic education. What is econometrics? When dealing with a living developing science, there is always a difficulty when trying to give short description its subject matter and methods. Can we say that econometrics is the science of economic measurements, as its name suggests? Of course, it is possible, but then the question arises, what is the meaning of the term “economic dimensions”. This is analogous to defining mathematics as the science of numbers. Therefore, without trying to develop this problem in more detail, we will quote the statements of recognized authorities in economics and econometrics.

1. Introduction
1.1. Models
1.2. Model types
1.3. Data types
2. Paired regression model
2.1. Curve fitting
2.2. Least squares method (LSM)
2.3. Linear regression model with two variables
2.4. Gauss-Markov theorem. Estimation of error variance a2
2.5. Statistical Properties of LSM-Estimates of Regression Parameters. Hypothesis test b = bo- Confidence intervals for regression coefficients
2.6. Analysis of the variation of the dependent variable in the regression. R2 determination coefficient
2.7. Maximum Likelihood Estimation of Regression Coefficients
Exercises
3. Multiple regression model
3.1. Main hypotheses
3.2. Least square method. Gauss-Markov theorem
3.3. Statistical Properties of OLS Estimates
3.4. Analysis of the variation of the dependent variable in the regression. R2 coefficients and adjusted R
3.5. Hypothesis testing. Confidence intervals and confidence regions
Exercises
4. Various aspects of multiple regression
4.1. Multicollinearity
4.2. Dummy variables
4.3. Partial Correlation
4.4. Model specification
Exercises
5. Some Generalizations of Multiple Regression
5.1. Stochastic regressors
5.2. Generalized least squares
5.3. Affordable generalized least squares
Exercises
6. Heteroscedasticity and time correlation
6.1. Heteroskedasticity
6.2. Time correlation
Exercises
7. Forecasting in regression models
7.1. Unconditional Prediction
7.2. Conditional Prediction
7.3. Forecasting in the presence of autoregressive errors
Exercises
8. Instrumental variables
8.1. Consistency of estimates obtained using instrumental variables
8.2. Influence of measurement errors
8.3. Two-Step Least Squares
8.4. Houseman test
Exercises
9. Systems of regression equations
3.1. Externally unrelated equations
9.1. Systems of simultaneous equations
Exercises
10. Maximum likelihood method in regression models
10.1. Introduction
10.2. Mathematical apparatus 246
10.3. Maximum Likelihood Estimation of Multivariate Normal Distribution Parameters
10.4. Properties of Maximum Likelihood Estimates
10.5. Maximum Likelihood Estimation in a Linear Model
10.6. Hypothesis testing in a linear model, I
10.7. Hypothesis testing in a linear model, II
10.8. Nonlinear Constraints
Exercises
11. Time series
11.1. Distributed lag models
11.2. Dynamic Models
11.3. Unit roots and cointegration
11.4 Box-Jenkins Models (ARIMA)
11.5. GARCH Models
Exercises
12. Discrete dependent variables and censored samples
12.1. Binary and Multiple Choice Models
12.2. Models with truncated and censored samples
Exercises
13. Panel data
13.1 Introduction
13.2. Designations and basic models
13.3. Fixed effect model
13.4. Model with random effect
13.5. Fit Quality
13.6. Model selection
13.7. Dynamic Models
13.8. Binary Choice Models with Panel Data
13.9. Generalized method of moments
Exercises
14. Pretesting: Introduction
14.1. Introduction
14.2. Formulation of the problem
14.3. Main result
14.4. Pretest evaluation
14.5. WALS score
14.6. Equivalence theorem
14.7. Pre-Testing and the Understatement Effect
14.8. The effect of "understatement". One auxiliary parameter
14.9. Model selection: from general to particular and from particular to general
14.10. The effect of "understatement". Two auxiliary parameters
14.11. Prediction and pre-testing
14.12. Generalizations
14.13. Other questions
Exercises
15. Econometrics of financial markets
15.1. Introduction
15.2. Financial Market Efficiency Hypothesis
15.3. Securities portfolio optimization
15.4. Test for inclusion of new assets in an efficient portfolio
15.5. Optimal portfolio in the presence of a risk-free asset
15.6. Financial Asset Valuation Models
Exercises
16. Perspectives on econometrics
1.6.1. Introduction
16.2. What exactly does an econometrician do?
16.3. Econometrics and physics
16.4. Econometrics and mathematical statistics
16.5. Theory and practice
16.6. Econometric Method
16.7. Weak link
16.8. Aggregation
16.9. How to use other works
16.10. Conclusion
LA application. Linear algebra
1. Vector space
2. The vector space Ln
3. Linear dependence
4. Linear subspace
5. Basis. Dimension
6. Linear operators
7. Matrices
8. Operations with matrices
9. Matrix invariants: trace, determinant
10. Matrix rank
11. Inverse matrix
12. Systems of linear equations
13. Eigenvalues and vectors
14. Symmetric matrices
15. Positive definite matrices
16. Idempotent matrices
17. Block matrices
18. Kronecker Product
19. Differentiation with respect to a vector argument
Exercises
MS application. Theory of Probability and Mathematical Statistics
1. Random variables, random vectors
2. Conditional distributions
3. Some special distributions
4. Multivariate normal distribution
5. The law of large numbers. Central limit theorem
6 Basic concepts and tasks of mathematical statistics
7. Parameter Estimation
8. Hypothesis testing
EP application. Overview of econometric packages
1. The origin of the packages. Windows version. Graphic arts
2. About some packages
3. Practical work experience
Application ST. Brief English-Russian Dictionary of Terms
TA application. tables
Literature
Subject index

UDC 330.43(075.8)
BBK 65v6ya73

Magnus Ya.R., Katyshev P.K., Peresetsky A.A.
Econometrics. Initial course: Proc. - 8th ed., Rev. — M.:, 2007. — 504 p.

ISBN 978-5-7749-0473-0

The Panel Data chapter expands the book to a complete list of topics traditionally included in modern basic econometrics courses. The chapters "Preliminary testing" and "Econometrics of financial markets" will be useful to those who are interested in theoretical and applied aspects of econometrics, respectively. The number of exercises has been significantly increased. Included are exercises with real data available to the reader on the book's website.

For students, graduate students, teachers, as well as specialists in applied economics and finance.

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