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Lecture notes for Monetary policy include all of the following: Traditional models of monetary policy, microfoundations of monetary policy models, looking into some recent models of monetary policy, solving linear expectational difference equations, a “simple” policy rule, optimal policy under commitment, simple rules with singular dynamic equations, discretionary solution, monetary policy in VAR systems. | Contents 1 Lecture Notes for Monetary Policy (PhD course at UNISG) Traditional Models of Monetary Policy 1.1 The IS-LM Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Barro-Gordon Model . . . . . . . . . . . . . . . . . . . . . . . . 2 Microfoundations of Monetary Policy Models 2.1 Money Demand . . . . . . . . . . . . . . . . . . . . 2.2 The Effect of Money vs the Effect of Price Stickiness 2.3 Dynamic Models of Sticky Prices . . . . . . . . . . 2.4 Aggregate Demand . . . . . . . . . . . . . . . . . . 2.5 Recent Models for Studying Monetary Policy . . . . Paul S¨ derlind1 o October 2003 3 4 5 1 University of St. Gallen and CEPR. Address: s/bf-HSG, Rosenbergstrasse 52, CH-9000 St. Gallen, Switzerland. E-mail: Paul.Soderlind@unisg.ch. Document name: MonAll.TeX. 4 4 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 14 18 21 27 28 Looking into Some Recent Models of Monetary Policy 3.1 A Baseline Model . . . . . . . . . . . . . . . . . . . . . 3.2 Model Extension 1: Predetermined Prices . . . . . . . . 3.3 Model Extension 2: More Output Dynamics . . . . . . . 3.4 Appendix: Derivation of the Aggregate Demand Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 36 43 46 48 Solving Linear Expectational Difference Equations 4.1 The Model . . . . . . . . . . . . . . . . . . . . . 4.2 Matrix Decompositions . . . . . . . . . . . . . . 4.3 Solving . . . . . . . . . . . . . . . . . . . . . . 4.4 Singular Dynamic Equations∗ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 50 51 53 58 A “Simple” Policy Rule 5.1 Model and Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Time Series Representation . . . . . . . . . . . . . . . . . . . . . . . 58 59 59 . . . . . . . . . . . . . . . . . . . . . 1 5.3 5.4 5.5 6 Value of Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . Optimal Simple Rule . . . . . . . . . .