Bounded Rationality in Macroeconomics: Thomas J. Sargent

Bounded Rationality in Macroeconomics: Thomas J. Sargent

Bounded Rationality in Macroeconomics: Thomas J. Sargent

Bounded Rationality in Macroeconomics: Thomas J. Sargent

Synopsis

This is a survey of bounded rationality, an area of theoretical macroeconomics which is receiving increased attention. The book is written by a leading macroeconomist who outlines the issues involved, describes some of the analytic tools that are being used, and shows how they can be appliedin a range of models. It points to further potential positive developments of the theory as well as some of its limitations.

Excerpt

Statistics studies methods of making inferences and decisions when we aren't sure what is happening. For economists, statistics has long been the place to go prospecting for hypotheses to understand how people behave under conditions of uncertainty and ignorance. It must be our starting place, because we propose to make our agents even more like statisticians or econometricians than they are in rational expectations models.

The purpose of this chapter is to review the workings of that centerpiece of econometrics, least squares regression, and how in many contexts it can be implemented recursively. This review will set the stage for the following chapter on neural networks and artificial intelligence, material that is less familiar to most economists, but which we shall see just implements recursive least squares in various ingenious contexts. In this chapter and the next we shall be looking for devices to hand over to the boundedly rational agents that will be created in Chapter 5.

Representation and estimation

From statistics, economists have borrowed and adapted a set of methods for describing and interpreting relationships within data sets. The task of description has fruitfully been subdivided into two logically distinct but interrelated pieces: representation and estimation. Representation of a relationship means positing a mathematical model that is assumed to have generated the data.

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