{"product_id":"an-introduction-to-econometric-theory-hardback-9781119484882","title":"An Introduction to Econometric Theory (Hardback) 9781119484882","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAn Introduction to Econometric Theory\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eJames Davidson (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119484882, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 5 October 2018\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e256 pages\u003cbr\u003e24.6 x 17.3 x 1.8 cm, 0.544 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\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\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\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\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Economics [\u003ca title=\"See our other books on Economics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Economics%20%5BKC%5D%22\"\u003eKC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52428605948184,"sku":"9781119484882","price":57.19,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119484882.jpg?v=1784681186","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/an-introduction-to-econometric-theory-hardback-9781119484882","provider":"Freshly Printed Books","version":"1.0","type":"link"}