{"product_id":"introduction-to-econometrics-9780470032701","title":"Introduction to Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eIntroduction to Econometric Modelling  provides an introduction to econometrics for undergraduate students.   In this book, Gary Koop provides a broader set of models than is offered in existing textbooks and places greater focus on models (e.g. the regression model) than the methods that are used to analyze the models.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e“An introductory text offering econometric methodology for quantifying and managing this variety of risk, illustrated by empirical examples.” (\u003ci\u003eTimes Higher Education Supplement\u003c\/i\u003e, Thursday 28th February)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 An Overview of Econometrics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The importance of econometrics 1\u003c\/p\u003e \u003cp\u003e1.2 Types of economic data 2\u003c\/p\u003e \u003cp\u003e1.3 Working with data: graphical methods 6\u003c\/p\u003e \u003cp\u003e1.4 Working with data: descriptive statistics and correlation 11\u003c\/p\u003e \u003cp\u003e1.5 Chapter summary 26\u003c\/p\u003e \u003cp\u003eExercises 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 A Non-technical Introduction to Regression 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 29\u003c\/p\u003e \u003cp\u003e2.2 The simple regression model 30\u003c\/p\u003e \u003cp\u003e2.3 The multiple regression model 42\u003c\/p\u003e \u003cp\u003e2.4 Chapter summary 55\u003c\/p\u003e \u003cp\u003eExercises 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Econometrics of the Simple Regression Model 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 59\u003c\/p\u003e \u003cp\u003e3.2 A review of basic concepts in probability in the context of the regression model 60\u003c\/p\u003e \u003cp\u003e3.3 The classical assumptions for the regression model 64\u003c\/p\u003e \u003cp\u003e3.4 Properties of the ordinary least-squares estimator of \u003ci\u003eβ\u003c\/i\u003e 67\u003c\/p\u003e \u003cp\u003e3.5 Deriving a confidence interval for \u003ci\u003eβ\u003c\/i\u003e 75\u003c\/p\u003e \u003cp\u003e3.6 Hypothesis tests about \u003ci\u003eβ\u003c\/i\u003e 77\u003c\/p\u003e \u003cp\u003e3.7 Modifications to statistical procedures when \u003ci\u003eσ\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e is unknown 78\u003c\/p\u003e \u003cp\u003e3.8 Chapter summary 81\u003c\/p\u003e \u003cp\u003eExercises 82\u003c\/p\u003e \u003cp\u003eAppendix 1: Proof of the Gauss–Markov theorem 84\u003c\/p\u003e \u003cp\u003eAppendix 2: Using asymptotic theory in the simple regression model 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 The Econometrics of the Multiple Regression Model 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 91\u003c\/p\u003e \u003cp\u003e4.2 Basic results for the multiple regression model 92\u003c\/p\u003e \u003cp\u003e4.3 Issues relating to the choice of explanatory variables 96\u003c\/p\u003e \u003cp\u003e4.4 Hypothesis testing in the multiple regression model 102\u003c\/p\u003e \u003cp\u003e4.5 Choice of functional form in the multiple regression model 109\u003c\/p\u003e \u003cp\u003e4.6 Chapter summary 115\u003c\/p\u003e \u003cp\u003eExercises 116\u003c\/p\u003e \u003cp\u003eAppendix: Wald and Lagrange multiplier tests 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 The Multiple Regression Model: Freeing Up the Classical Assumptions 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 121\u003c\/p\u003e \u003cp\u003e5.2 Basic theoretical results 122\u003c\/p\u003e \u003cp\u003e5.3 Heteroskedasticity 124\u003c\/p\u003e \u003cp\u003e5.4 The regression model with autocorrelated errors 138\u003c\/p\u003e \u003cp\u003e5.5 The instrumental variables estimator 149\u003c\/p\u003e \u003cp\u003e5.6 Chapter summary 164\u003c\/p\u003e \u003cp\u003eExercises 165\u003c\/p\u003e \u003cp\u003eAppendix: Asymptotic results for the OLS and instrumental variables estimators 168\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Univariate Time Series Analysis 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 173\u003c\/p\u003e \u003cp\u003e6.2 Time series notation 175\u003c\/p\u003e \u003cp\u003e6.3 Trends in time series variables 177\u003c\/p\u003e \u003cp\u003e6.4 The autocorrelation function 179\u003c\/p\u003e \u003cp\u003e6.5 The autoregressive model 181\u003c\/p\u003e \u003cp\u003e6.6 Defining stationarity 195\u003c\/p\u003e \u003cp\u003e6.7 Modeling volatility 197\u003c\/p\u003e \u003cp\u003e6.8 Chapter summary 205\u003c\/p\u003e \u003cp\u003eExercises 207\u003c\/p\u003e \u003cp\u003eAppendix: MA and ARMA models 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Regression with Time Series Variables 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 213\u003c\/p\u003e \u003cp\u003e7.2 Time series regression when X and Yare stationary 214\u003c\/p\u003e \u003cp\u003e7.3 Time series regression when Y and X have unit roots 217\u003c\/p\u003e \u003cp\u003e7.4 Time series regression when Y and X have unit roots but are NOTcointegrated 227\u003c\/p\u003e \u003cp\u003e7.5 Granger causality 227\u003c\/p\u003e \u003cp\u003e7.6 Vector autoregressions 233\u003c\/p\u003e \u003cp\u003e7.7 Chapter summary 247\u003c\/p\u003e \u003cp\u003eExercises 248\u003c\/p\u003e \u003cp\u003eAppendix: The theory of forecasting 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Models for Panel Data 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 255\u003c\/p\u003e \u003cp\u003e8.2 The pooled model 256\u003c\/p\u003e \u003cp\u003e8.3 Individual effects models 256\u003c\/p\u003e \u003cp\u003e8.4 Chapter summary 271\u003c\/p\u003e \u003cp\u003eExercises 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Qualitative Choice and Limited Dependent Variable Models 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 277\u003c\/p\u003e \u003cp\u003e9.2 Qualitative choice models 278\u003c\/p\u003e \u003cp\u003e9.3 Limited dependent variable models 296\u003c\/p\u003e \u003cp\u003e9.4 Chapter summary 304\u003c\/p\u003e \u003cp\u003eExercises 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Bayesian Econometrics 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 An overview of Bayesian econometrics 309\u003c\/p\u003e \u003cp\u003e10.2 The normal linear regression model with natural conjugate prior and a single explanatory variable 315\u003c\/p\u003e \u003cp\u003e10.3 Chapter summary 326\u003c\/p\u003e \u003cp\u003eExercises 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Bayesian analysis of the simple regression model with unknown variance 328\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAppendix A: Mathematical Basics 333\u003c\/p\u003e \u003cp\u003eAppendix B: Probability Basics 338\u003c\/p\u003e \u003cp\u003eAppendix C: Basic Concepts in Asymptotic Theory 348\u003c\/p\u003e \u003cp\u003eAppendix D: Writing an Empirical Project 353\u003c\/p\u003e \u003cp\u003e\u003cb\u003eTables 359\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTable 1. Area under the standard normal distribution Pr(0 ≤ \u003ci\u003eZ\u003c\/i\u003e ≤ \u003ci\u003ez\u003c\/i\u003e) 359\u003c\/p\u003e \u003cp\u003eTable 2. Area under the Student \u003ci\u003et\u003c\/i\u003e distribution for different degrees of freedom (DF), Pr(\u003ci\u003eZ\u003c\/i\u003e ≥ \u003ci\u003ez\u003c\/i\u003e) = \u003ci\u003eα\u003c\/i\u003e 360\u003c\/p\u003e \u003cp\u003eTable 3. Percentiles of the chi-square distribution 361\u003c\/p\u003e \u003cp\u003eTable 4a. Area under the \u003ci\u003eF\u003c\/i\u003e-distribution for different degrees of freedom, \u003ci\u003eν\u003c\/i\u003e1 and \u003ci\u003eν\u003c\/i\u003e2, Pr(\u003ci\u003eZ\u003c\/i\u003e ≥ \u003ci\u003ez\u003c\/i\u003e) = 0.05 362\u003c\/p\u003e \u003cp\u003eTable 4b. Area under the \u003ci\u003eF\u003c\/i\u003e-distribution for different degrees of freedom, \u003ci\u003eν\u003c\/i\u003e1 and \u003ci\u003eν\u003c\/i\u003e2, Pr(\u003ci\u003eZ\u003c\/i\u003e ≥ \u003ci\u003ez\u003c\/i\u003e) = 0.01 363\u003c\/p\u003e \u003cp\u003eBibliography 364\u003c\/p\u003e \u003cp\u003eIndex 365\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402267107671,"sku":"9780470032701","price":45.55,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470032701.jpg?v=1730479890","url":"https:\/\/bookcurl.com\/products\/introduction-to-econometrics-9780470032701","provider":"Book Curl","version":"1.0","type":"link"}