{"product_id":"an-introduction-to-econometric-theory-isbn-9781119484882","title":"An Introduction to Econometric Theory","description":"\u003cp\u003e\u003cb\u003eA GUIDE TO ECONOMICS, STATISTICS AND FINANCE THAT EXPLORES THE MATHEMATICAL FOUNDATIONS UNDERLING ECONOMETRIC METHODS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAn Introduction to Econometric Theory\u003c\/i\u003e offers a text to help in the mastery of the mathematics that underlie econometric methods and includes a detailed study of matrix algebra and distribution theory. Designed to be an accessible resource, the text explains in clear language why things are being done, and how previous material informs a current argument. The style is deliberately informal with numbered theorems and lemmas avoided. However, very few technical results are quoted without some form of explanation, demonstration or proof.\u003c\/p\u003e \u003cp\u003eThe author—a noted expert in the field—covers a wealth of topics including: simple regression, basic matrix algebra, the general linear model, distribution theory, the normal distribution, properties of least squares, unbiasedness and efficiency, eigenvalues, statistical inference in regression, t and F tests, the partitioned regression, specification analysis, random regressor theory, introduction to asymptotics and maximum likelihood. Each of the chapters is supplied with a collection of exercises, some of which are straightforward and others more challenging. This important text:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents a guide for teaching econometric methods to undergraduate and graduate students of economics, statistics or finance\u003c\/li\u003e \u003cli\u003eOffers proven classroom-tested material\u003c\/li\u003e \u003cli\u003eContains sets of exercises that accompany each chapter\u003c\/li\u003e \u003cli\u003eIncludes a companion website that hosts additional materials, a solution manual and lecture slides\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten for undergraduates and graduate students of economics, statistics or finance, \u003ci\u003eAn Introduction to Econometric Theory\u003c\/i\u003e is an essential beginner's guide to the underpinnings of econometrics.\u003c\/p\u003e \u003cp\u003eList of Figures ix\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAbout the CompanionWebsite xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Fitting \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Elementary Data Analysis \u003c\/b\u003e\u003cb\u003e3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Variables and Observations 3\u003c\/p\u003e \u003cp\u003e1.2 Summary Statistics 4\u003c\/p\u003e \u003cp\u003e1.3 Correlation 6\u003c\/p\u003e \u003cp\u003e1.4 Regression 10\u003c\/p\u003e \u003cp\u003e1.5 Computing the Regression Line 12\u003c\/p\u003e \u003cp\u003e1.6 Multiple Regression 16\u003c\/p\u003e \u003cp\u003e1.7 Exercises 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Matrix Representation \u003c\/b\u003e\u003cb\u003e21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Systems of Equations 21\u003c\/p\u003e \u003cp\u003e2.2 Matrix Algebra Basics 23\u003c\/p\u003e \u003cp\u003e2.3 Rules of Matrix Algebra 26\u003c\/p\u003e \u003cp\u003e2.4 Partitioned Matrices 27\u003c\/p\u003e \u003cp\u003e2.5 Exercises 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Solving the Matrix Equation \u003c\/b\u003e\u003cb\u003e31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Matrix Inversion 31\u003c\/p\u003e \u003cp\u003e3.2 Determinant and Adjoint 34\u003c\/p\u003e \u003cp\u003e3.3 Transposes and Products 37\u003c\/p\u003e \u003cp\u003e3.4 Cramer’s Rule 38\u003c\/p\u003e \u003cp\u003e3.5 Partitioning and Inversion 39\u003c\/p\u003e \u003cp\u003e3.6 A Note on Computation 41\u003c\/p\u003e \u003cp\u003e3.7 Exercises 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Least Squares Solution \u003c\/b\u003e\u003cb\u003e47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Linear Dependence and Rank 47\u003c\/p\u003e \u003cp\u003e4.2 The General Linear Regression 50\u003c\/p\u003e \u003cp\u003e4.3 Definite Matrices 52\u003c\/p\u003e \u003cp\u003e4.4 Matrix Calculus 56\u003c\/p\u003e \u003cp\u003e4.5 Goodness of Fit 57\u003c\/p\u003e \u003cp\u003e4.6 Exercises 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Modelling \u003c\/b\u003e\u003cb\u003e63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Probability Distributions \u003c\/b\u003e\u003cb\u003e65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 A Random Experiment 65\u003c\/p\u003e \u003cp\u003e5.2 Properties of the Normal Distribution 68\u003c\/p\u003e \u003cp\u003e5.3 Expected Values 72\u003c\/p\u003e \u003cp\u003e5.4 Discrete Random Variables 75\u003c\/p\u003e \u003cp\u003e5.5 Exercises 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 More on Distributions \u003c\/b\u003e\u003cb\u003e83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Random Vectors 83\u003c\/p\u003e \u003cp\u003e6.2 The Multivariate Normal Distribution 84\u003c\/p\u003e \u003cp\u003e6.3 Other Continuous Distributions 87\u003c\/p\u003e \u003cp\u003e6.4 Moments 90\u003c\/p\u003e \u003cp\u003e6.5 Conditional Distributions 92\u003c\/p\u003e \u003cp\u003e6.6 Exercises 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Classical RegressionModel \u003c\/b\u003e\u003cb\u003e97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The Classical Assumptions 97\u003c\/p\u003e \u003cp\u003e7.2 The Model 99\u003c\/p\u003e \u003cp\u003e7.3 Properties of Least Squares 101\u003c\/p\u003e \u003cp\u003e7.4 The Projection Matrices 103\u003c\/p\u003e \u003cp\u003e7.5 The Trace 104\u003c\/p\u003e \u003cp\u003e7.6 Exercises 106\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Gauss-Markov Theorem \u003c\/b\u003e\u003cb\u003e109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 A Simple Example 109\u003c\/p\u003e \u003cp\u003e8.2 Efficiency in the General Model 111\u003c\/p\u003e \u003cp\u003e8.3 Failure of the Assumptions 113\u003c\/p\u003e \u003cp\u003e8.4 Generalized Least Squares 114\u003c\/p\u003e \u003cp\u003e8.5 Weighted Least Squares 116\u003c\/p\u003e \u003cp\u003e8.6 Exercises 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Testing \u003c\/b\u003e\u003cb\u003e121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Eigenvalues and Eigenvectors \u003c\/b\u003e\u003cb\u003e123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Characteristic Equation 123\u003c\/p\u003e \u003cp\u003e9.2 Complex Roots 124\u003c\/p\u003e \u003cp\u003e9.3 Eigenvectors 126\u003c\/p\u003e \u003cp\u003e9.4 Diagonalization 128\u003c\/p\u003e \u003cp\u003e9.5 Other Properties 130\u003c\/p\u003e \u003cp\u003e9.6 An Interesting Result 131\u003c\/p\u003e \u003cp\u003e9.7 Exercises 133\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 The Gaussian RegressionModel \u003c\/b\u003e\u003cb\u003e135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Testing Hypotheses 135\u003c\/p\u003e \u003cp\u003e10.2 Idempotent Quadratic Forms 137\u003c\/p\u003e \u003cp\u003e10.3 Confidence Regions 140\u003c\/p\u003e \u003cp\u003e10.4 t Statistics 141\u003c\/p\u003e \u003cp\u003e10.5 Tests of Linear Restrictions 144\u003c\/p\u003e \u003cp\u003e10.6 Constrained Least Squares 146\u003c\/p\u003e \u003cp\u003e10.7 Exercises 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Partitioning and Specification \u003c\/b\u003e\u003cb\u003e153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Partitioned Regression 153\u003c\/p\u003e \u003cp\u003e11.2 Frisch-Waugh-Lovell Theorem 155\u003c\/p\u003e \u003cp\u003e11.3 Misspecification Analysis 156\u003c\/p\u003e \u003cp\u003e11.4 Specification Testing 159\u003c\/p\u003e \u003cp\u003e11.5 Stability Analysis 160\u003c\/p\u003e \u003cp\u003e11.6 Prediction Tests 162\u003c\/p\u003e \u003cp\u003e11.7 Exercises 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Extensions \u003c\/b\u003e\u003cb\u003e167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Random Regressors \u003c\/b\u003e\u003cb\u003e169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Conditional Probability 169\u003c\/p\u003e \u003cp\u003e12.2 Conditional Expectations 170\u003c\/p\u003e \u003cp\u003e12.3 StatisticalModels Contrasted 174\u003c\/p\u003e \u003cp\u003e12.4 The Statistical Assumptions 176\u003c\/p\u003e \u003cp\u003e12.5 Properties of OLS 178\u003c\/p\u003e \u003cp\u003e12.6 The Gaussian Model 182\u003c\/p\u003e \u003cp\u003e12.7 Exercises 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Introduction to Asymptotics \u003c\/b\u003e\u003cb\u003e187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 The Lawof Large Numbers 187\u003c\/p\u003e \u003cp\u003e13.2 Consistent Estimation 192\u003c\/p\u003e \u003cp\u003e13.3 The Central LimitTheorem 195\u003c\/p\u003e \u003cp\u003e13.4 Asymptotic Normality 198\u003c\/p\u003e \u003cp\u003e13.5 Multiple Regression 201\u003c\/p\u003e \u003cp\u003e13.6 Exercises 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Asymptotic Estimation Theory \u003c\/b\u003e\u003cb\u003e207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Large Sample Efficiency 207\u003c\/p\u003e \u003cp\u003e14.2 Instrumental Variables 208\u003c\/p\u003e \u003cp\u003e14.3 Maximum Likelihood 210\u003c\/p\u003e \u003cp\u003e14.4 Gaussian ML 213\u003c\/p\u003e \u003cp\u003e14.5 Properties of ML Estimators 214\u003c\/p\u003e \u003cp\u003e14.6 Likelihood Inference 216\u003c\/p\u003e \u003cp\u003e14.7 Exercises 218\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Appendices \u003c\/b\u003e\u003cb\u003e221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA The Binomial Coefficients 223\u003c\/p\u003e \u003cp\u003eB The Exponential Function 225\u003c\/p\u003e \u003cp\u003eC Essential Calculus 227\u003c\/p\u003e \u003cp\u003eD The Generalized Inverse 229\u003c\/p\u003e \u003cp\u003eRecommended Reading 233\u003c\/p\u003e \u003cp\u003eIndex 235\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJAMES DAVIDSON\u003c\/b\u003e is Professor of Econometrics at the University of Exeter. He has also held teaching posts at the University of Warwick, the London School of Economics, the University of Wales Aberystwyth and Cardiff University, as well as visiting positions at the University of California, Berkeley, the University of California, San Diego, and Central European University, Budapest.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA GUIDE TO ECONOMICS, STATISTICS AND FINANCE THAT EXPLORES THE MATHEMATICAL FOUNDATIONS UNDERLING ECONOMETRIC METHODS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAn Introduction to Econometric Theory\u003c\/i\u003e offers a text to help in the mastery of the mathematics that underlie econometric methods and includes a detailed study of matrix algebra and distribution theory. Designed to be an accessible resource, the text explains in clear language why things are being done, and how previous material informs a current argument. The style is deliberately informal with numbered theorems and lemmas avoided. However, very few technical results are quoted without some form of explanation, demonstration or proof.\u003c\/p\u003e \u003cp\u003eThe author—a noted expert in the field—covers a wealth of topics including: simple regression, basic matrix algebra, the general linear model, distribution theory, the normal distribution, properties of least squares, unbiasedness and efficiency, eigenvalues, statistical inference in regression, t and F tests, the partitioned regression, specification analysis, random regressor theory, introduction to asymptotics and maximum likelihood. Each of the chapters is supplied with a collection of exercises, some of which are straightforward and others more challenging. This important text:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents a guide for teaching econometric methods to undergraduate and graduate students of economics, statistics or finance\u003c\/li\u003e \u003cli\u003eOffers proven classroom-tested material\u003c\/li\u003e \u003cli\u003eContains sets of exercises that accompany each chapter\u003c\/li\u003e \u003cli\u003eIncludes a companion website that hosts additional materials, a solution manual and lecture slides\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten for undergraduates and graduate students of economics, statistics or finance, \u003ci\u003eAn Introduction to Econometric Theory\u003c\/i\u003e is an essential beginner's guide to the underpinnings of econometrics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988725481701,"sku":"NP9781119484882","price":69.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119484882.jpg?v=1761781343","url":"https:\/\/k12savings.com\/es\/products\/an-introduction-to-econometric-theory-isbn-9781119484882","provider":"K12savings","version":"1.0","type":"link"}