Book contents
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Part I Fundamentals of Bayesian Inference
- 1 Introduction
- 2 Basic Concepts of Probability and Inference
- 3 Posterior Distributions and Inference
- 4 Prior Distributions
- Part II Simulation
- Part III Applications
- A Probability Distributions and Matrix Theorems
- B Computer Programs for MCMC Calculations
- Bibliography
- Author Index
- Subject Index
1 - Introduction
from Part I - Fundamentals of Bayesian Inference
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- List of Figures
- List of Tables
- Preface
- Part I Fundamentals of Bayesian Inference
- 1 Introduction
- 2 Basic Concepts of Probability and Inference
- 3 Posterior Distributions and Inference
- 4 Prior Distributions
- Part II Simulation
- Part III Applications
- A Probability Distributions and Matrix Theorems
- B Computer Programs for MCMC Calculations
- Bibliography
- Author Index
- Subject Index
Summary
THIS CHAPTER INTRODUCES several important concepts, provides a guide to the rest of the book, and offers some historical perspective and suggestions for further reading.
Econometrics
Econometrics is largely concerned with quantifying the relationship between one or more more variables y, called the response variables or the dependent variables, and one or more variables x, called the regressors, independent variables, or covariates. The response variable or variables may be continuous or discrete; the latter case includes binary, multinomial, and count data. For example, y might represent the quantities demanded of a set of goods, and x could include income and the prices of the goods; or y might represent investment in capital equipment, and x could include measures of expected sales, cash flows, and borrowing costs; or y might represent a decision to travel by public transportation rather than private, and x could include income, fares, and travel time under various alternatives.
In addition to the covariates, it is assumed that unobservable random variables affect y, so that y itself is a random variable. It is characterized either by a probability density function (p.d.f.) for continuous y or a probability mass function (p.m.f.) for discrete y. The p.d.f. or p.m.f. depends on the values of unknown parameters, denoted by θ. The notation y ~ f(y∣θ, x) means that y has the p.d.f. or p.m.f. f(y∣θ, x), where the function depends on the parameters and covariates.
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- Introduction to Bayesian Econometrics , pp. 3 - 6Publisher: Cambridge University PressPrint publication year: 2007
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