Probability theory and statistical inference : econometric modeling with observational data

Online-Ressource (xxvii, 815 p)

ill

25 cm

Includes bibliographical references (p. 787-805) and index

Cover; Half-title; Title; Copyright; Contents; Preface; 1 Intended audience and distinguishing features; 2 Origins and pedigree; 3 As a teaching and learning device; 4 Looking ahead: a bird's eye view; Acknowledgments; Symbols; Acronyms; 1 An introduction to empirical modeling; 1.1 Introduction; 1.2 Stochastic phenomena, a preliminary view; 1.3 Chance regularity and statistical models; 1.4 Statistical adequacy; 1.5 Statistical versus theory information; 1.6 Observed data; 1.7 Looking ahead; 1.8 Exercises; 2 Probability theory: a modeling framework; 2.1 Introduction

2.2 Simple statistical model: a preliminary view2.3 Probability theory: an introduction; 2.4 Random experiments; 2.5 Formalizing condition [a]: the outcomes set; 2.6 Formalizing condition [b ]: events and probabilities; 2.7 Formalizing condition [c]: random trials; 2.8 Statistical space; 2.9 A look forward; 2.10 Exercises; 3 The notion of a probability model; 3.1 Introduction; 3.2 The notion of a simple random variable; 3.3 The general notion of a random variable; 3.4 The cumulative distribution and density functions; 3.5 From a probability space to a probability model

3.6 Parameters and moments3.7 Moments; 3.8 Inequalities; 3.9 Summary; 3.10 Exercises; Appendix A Univariate probability models; 4 The notion of a random sample; 4.1 Introduction; 4.2 Joint distributions; 4.3 Marginal distributions; 4.4 Conditional distributions; 4.5 Independence; 4.6 Identical distributions; 4.7 A simple statistical model in empirical modeling: a preliminary view; 4.8 Ordered random samples*; 4.9 Summary; 4.10 Exercises; Appendix B Bivariate distributions; 5 Probabilistic concepts and real data; 5.1 Introduction; 5.2 Early developments; 5.3 Graphical displays: a t-plot

5.4 Assessing distribution assumptions5.5 Independence and the t-plot; 5.6 Homogeneity and the t-plot; 5.7 The empirical cdf and related graphs*; 5.8 Generating pseudo-random numbers*; 5.9 Summary; 5.10 Exercises; 6 The notion of a non-random sample; 6.1 Introduction; 6.2 Non-random sample: a preliminary view; 6.3 Dependence between two random variables: joint distributions; 6.4 Dependence between two random variables: moments; 6.5 Dependence and the measurement system; 6.6 Joint distributions and dependence; 6.7 From probabilistic concepts to observed data; 6.8 What comes next?

6.9 Exercises7 Regression and related notions; 7.1 Introduction; 7.2 Conditioning and regression; 7.3 Reduction and stochastic conditioning; 7.4 Weak exogeneity*; 7.5 The notion of a statistical generating mechanism (GM); 7.6 The biometric tradition in statistics; 7.7 Summary; 7.8 Exercises; 8 Stochastic processes; 8.1 Introduction; 8.2 The notion of a stochastic process; 8.3 Stochastic processes: a preliminary view; 8.4 Dependence restrictions; 8.5 Homogeneity restrictions; 8.6 "Building block" stochastic processes; 8.7 Markov processes; 8.8 Random walk processes; 8.9 Martingale processes

8.10 Gaussian processes

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ECONIS - Online Catalogue of the ZBW