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.