{"product_id":"bayesian-econometrics-9780470845677","title":"Bayesian Econometrics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eResearchers in many fields are increasingly finding the Bayesian approach to statistics to be an attractive one. This book introduces the reader to the use of Bayesian methods in the field of econometrics at the advanced undergraduate or graduate level.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An Overview of Bayesian Econometrics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Bayesian Theory 1\u003c\/p\u003e \u003cp\u003e1.2 Bayesian Computation 6\u003c\/p\u003e \u003cp\u003e1.3 Bayesian Computer Software 10\u003c\/p\u003e \u003cp\u003e1.4 Summary 11\u003c\/p\u003e \u003cp\u003e1.5 Exercises 11\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Normal Linear Regression Model with Natural Conjugate Prior and a Single Explanatory Variable 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 15\u003c\/p\u003e \u003cp\u003e2.2 The Likelihood Function 16\u003c\/p\u003e \u003cp\u003e2.3 The Prior 18\u003c\/p\u003e \u003cp\u003e2.4 The Posterior 19\u003c\/p\u003e \u003cp\u003e2.5 Model Comparison 23\u003c\/p\u003e \u003cp\u003e2.6 Prediction 26\u003c\/p\u003e \u003cp\u003e2.7 Empirical Illustration 28\u003c\/p\u003e \u003cp\u003e2.8 Summary 31\u003c\/p\u003e \u003cp\u003e2.9 Exercises 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 The Normal Linear Regression Model with Natural Conjugate Prior and Many Explanatory Variables 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 33\u003c\/p\u003e \u003cp\u003e3.2 The Linear Regression Model in Matrix Notation 34\u003c\/p\u003e \u003cp\u003e3.3 The Likelihood Function 35\u003c\/p\u003e \u003cp\u003e3.4 The Prior 36\u003c\/p\u003e \u003cp\u003e3.5 The Posterior 36\u003c\/p\u003e \u003cp\u003e3.6 Model Comparison 38\u003c\/p\u003e \u003cp\u003e3.7 Prediction 45\u003c\/p\u003e \u003cp\u003e3.8 Computational Methods: Monte Carlo Integration 46\u003c\/p\u003e \u003cp\u003e3.9 Empirical Illustration 47\u003c\/p\u003e \u003cp\u003e3.10 Summary 54\u003c\/p\u003e \u003cp\u003e3.11 Exercises 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Normal Linear Regression Model with Other Priors 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 59\u003c\/p\u003e \u003cp\u003e4.2 The Normal Linear Regression Model with Independent Normal-Gamma Prior 60\u003c\/p\u003e \u003cp\u003e4.3 The Normal Linear Regression Model Subject to Inequality Constraints 77\u003c\/p\u003e \u003cp\u003e4.4 Summary 85\u003c\/p\u003e \u003cp\u003e4.5 Exercises 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The Nonlinear Regression Model 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 89\u003c\/p\u003e \u003cp\u003e5.2 The Likelihood Function 91\u003c\/p\u003e \u003cp\u003e5.3 The Prior 91\u003c\/p\u003e \u003cp\u003e5.4 The Posterior 91\u003c\/p\u003e \u003cp\u003e5.5 Bayesian Computation: The Metropolis–Hastings Algorithm 92\u003c\/p\u003e \u003cp\u003e5.6 A Measure of Model Fit: The Posterior Predictive P-Value 100\u003c\/p\u003e \u003cp\u003e5.7 Model Comparison: The Gelfand–Dey Method 104\u003c\/p\u003e \u003cp\u003e5.8 Prediction 106\u003c\/p\u003e \u003cp\u003e5.9 Empirical Illustration 107\u003c\/p\u003e \u003cp\u003e5.10 Summary 112\u003c\/p\u003e \u003cp\u003e5.11 Exercises 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 The Linear Regression Model with General Error Covariance Matrix 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 117\u003c\/p\u003e \u003cp\u003e6.2 The Model with General 118\u003c\/p\u003e \u003cp\u003e6.3 Heteroskedasticity of Known Form 121\u003c\/p\u003e \u003cp\u003e6.4 Heteroskedasticity of an Unknown Form: Student-t Errors 124\u003c\/p\u003e \u003cp\u003e6.5 Autocorrelated Errors 130\u003c\/p\u003e \u003cp\u003e6.6 The Seemingly Unrelated Regressions Model 137\u003c\/p\u003e \u003cp\u003e6.7 Summary 143\u003c\/p\u003e \u003cp\u003e6.8 Exercises 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The Linear Regression Model with Panel Data 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 147\u003c\/p\u003e \u003cp\u003e7.2 The Pooled Model 148\u003c\/p\u003e \u003cp\u003e7.3 Individual Effects Models 149\u003c\/p\u003e \u003cp\u003e7.4 The Random Coefficients Model 155\u003c\/p\u003e \u003cp\u003e7.5 Model Comparison: The Chib Method of Marginal Likelihood Calculation 157\u003c\/p\u003e \u003cp\u003e7.6 Empirical Illustration 162\u003c\/p\u003e \u003cp\u003e7.7 Efficiency Analysis and the Stochastic Frontier Model 168\u003c\/p\u003e \u003cp\u003e7.8 Extensions 176\u003c\/p\u003e \u003cp\u003e7.9 Summary 177\u003c\/p\u003e \u003cp\u003e7.10 Exercises 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Introduction to Time Series: State Space Models 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 181\u003c\/p\u003e \u003cp\u003e8.2 The Local Level Model 183\u003c\/p\u003e \u003cp\u003e8.3 A General State Space Model 194\u003c\/p\u003e \u003cp\u003e8.4 Extensions 202\u003c\/p\u003e \u003cp\u003e8.5 Summary 205\u003c\/p\u003e \u003cp\u003e8.6 Exercises 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Qualitative and Limited Dependent Variable Models 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 209\u003c\/p\u003e \u003cp\u003e9.2 Overview: Univariate Models for Qualitative and Limited Dependent Variables 211\u003c\/p\u003e \u003cp\u003e9.3 The Tobit Model 212\u003c\/p\u003e \u003cp\u003e9.4 The Probit Model 214\u003c\/p\u003e \u003cp\u003e9.5 The Ordered Probit Model 218\u003c\/p\u003e \u003cp\u003e9.6 The Multinomial Probit Model 221\u003c\/p\u003e \u003cp\u003e9.7 Extensions of the Probit Models 229\u003c\/p\u003e \u003cp\u003e9.8 Other Extensions 230\u003c\/p\u003e \u003cp\u003e9.9 Summary 232\u003c\/p\u003e \u003cp\u003e9.10 Exercises 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Flexible Models: Nonparametric and Semiparametric Methods 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 235\u003c\/p\u003e \u003cp\u003e10.2 Bayesian Non- and Semiparametric Regression 236\u003c\/p\u003e \u003cp\u003e10.3 Mixtures of Normals Models 252\u003c\/p\u003e \u003cp\u003e10.4 Extensions and Alternative Approaches 262\u003c\/p\u003e \u003cp\u003e10.5 Summary 263\u003c\/p\u003e \u003cp\u003e10.6 Exercises 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Bayesian Model Averaging 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 265\u003c\/p\u003e \u003cp\u003e11.2 Bayesian Model Averaging in the Normal Linear Regression Model 266\u003c\/p\u003e \u003cp\u003e11.3 Extensions 278\u003c\/p\u003e \u003cp\u003e11.4 Summary 280\u003c\/p\u003e \u003cp\u003e11.5 Exercises 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Other Models, Methods and Issues 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 283\u003c\/p\u003e \u003cp\u003e12.2 Other Methods 284\u003c\/p\u003e \u003cp\u003e12.3 Other Issues 288\u003c\/p\u003e \u003cp\u003e12.4 Other Models 292\u003c\/p\u003e \u003cp\u003e12.5 Summary 308\u003c\/p\u003e \u003cp\u003eAppendix A: Introduction to Matrix Algebra 311\u003c\/p\u003e \u003cp\u003eAppendix B: Introduction to Probability and Statistics 317\u003c\/p\u003e \u003cp\u003eB.1 Basic Concepts of Probability 317\u003c\/p\u003e \u003cp\u003eB.2 Common Probability Distributions 324\u003c\/p\u003e \u003cp\u003eB.3 Introduction to Some Concepts in Sampling Theory 330\u003c\/p\u003e \u003cp\u003eB.4 Other Useful Theorems 333\u003c\/p\u003e \u003cp\u003eBibliography 335\u003c\/p\u003e \u003cp\u003eIndex 347\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402437632343,"sku":"9780470845677","price":54.1,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470845677.jpg?v=1730480396","url":"https:\/\/bookcurl.com\/products\/bayesian-econometrics-9780470845677","provider":"Book Curl","version":"1.0","type":"link"}