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Recursive Models of Dynamic Linear Economies$
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Lars Peter Hansen and Thomas J. Sargent

Print publication date: 2013

Print ISBN-13: 9780691042770

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691042770.001.0001

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Linear Stochastic Difference Equations

Linear Stochastic Difference Equations

(p.15) Chapter 2 Linear Stochastic Difference Equations
Recursive Models of Dynamic Linear Economies

Lars Peter Hansen

Thomas J. Sargent

Princeton University Press

This chapter describes the vector first-order linear stochastic difference equation. It is first used to represent information flowing to economic agents, then again to represent competitive equilibria. The vector first-order linear stochastic difference equation is associated with a tidy theory of prediction and a host of procedures for econometric application. Ease of analysis has prompted the adoption of economic specifications that cause competitive equilibria to have representations as vector first-order linear stochastic difference equations. Because it expresses next period's vector of state variables as a linear function of this period's state vector and a vector of random disturbances, a vector first-order vector stochastic difference equation is recursive. Disturbances that form a “martingale difference sequence” are basic building blocks used to construct time series. Martingale difference sequences are easy to forecast, a fact that delivers convenient recursive formulas for optimal predictions of time series.

Keywords:   vector first-order, linear stochastic difference equation, competitive equilibria, state variables, random disturbance, martingale difference sequence, time series

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