Economics 784, Advanced Macroeconomics: Financial Frictions in General Equilibrium.

I am teaching this course during the Spring 2017 semester in the Department of Economics, NC State University.  The goal of the course is to study the several mechanisms financial and macro economists have developed to map financial market disturbances into aggregate fluctuations.

This is a link to the course outline for the Spring 2017 semester.

Lecture notes are also available for the course.  However, lecture notes for Economics 784 are a work in process and therefore incomplete.

1. Financial Frictions before the Flood:  The first part of the course starts with the financial frictions studied by James Tobin and Karl Brunner and Allan Meltzer, next reviews the liquidity effect model of Robert Lucas and Tim Fuerst, studies a somewhat similar model by David Gordon and Eric Leeper that includes fiscal policy, analyzes DSGE models with costly state verification constructed by Stephen Williamson and Ben Bernanke, Mark Gertler, and Simon Gilchrist, presents the collateral constraints model of Nobu Kiyotaki and John Moore, and concludes with the liquidity constraint and credit shock model of Bengt Holmström and Jean Tirole.

2. Financial Frictions and the Real Economy:  The second set of lecture notes begins with the liquidity preference model of Franklin Allen and Douglas Gale.  The liquidity preference model is used to motivate models of bank runs, liquidity constraints, and financial fragility.  With this background, students are asked to study the impact of different classes of financial frictions on the dynamics of real business cycle models.  The RBC models presented to students are developed by Urban Jermann and Vincenzo Quadrini, Saki Bigio, Shouyong Shi, and Markus Brunnermeier and Yuliy Sannikov.

3. After the Flood: Financial Frictions and Central Banks:  The third section of the course integrates interbank markets, private banks, and central banks into the theories and models of parts 1 and 2.  Relevant papers include ones by Franklin Allen, Elena Carletti, and Douglas Gale, Stephen Williamson, Marvin Goodfriend and Ben McCallum, Mark Gertler and Peter Karadi, Emmanuel Farhri and Jean Tirole, Javier Bianchi and Saki Bigio, and Frederic Boissay, Fabrice Collard, and Frank Smets.   [These lecture notes are not available at the moment, but will be soon.]

Economics 785, Monetary Economics: Empirical and Computational Methods.

I am teaching this course during the Spring 2017 semester in the Department of Economics, NC State University.  The goal of the course is to provide students with tools to study and evaluate monetary policy.

This is a link to the course outline for the Spring 2017 semester.

Lecture notes are also available for the course.  However, lecture notes for Economics 785 are a work in progress and therefore incomplete.

Part I: Bayesian Methods to Estimate Linearized New Keynesian DSGE Models                         

The course begins with a review of a medium scale new Keynesian (NK-)DSGE models.  Relevant papers are by Lawrence Christiano, Marty Eichenbaum, and Charles Evans and Marco Del Negro, Frank Schorfheide, Frank Smets, and Rafael Wouters.  The review covers the optimality and equilibrium conditions of a NK-DSGE model, stochastically detrends these conditions, and building the model's steady state using the stochastically detrended optimality and equilibrium conditions.  The stochastically detrended optimality and equilibrium conditions are the basis for constructing a linearized approximate solution of the NK-DSGE model, which concludes the first part of these notes.  The next section of these notes reviews the Kalman filter and Kalman smoother drawing on material from James Hamilton's time series textbook.  The Kalman filter engages the solution of the linear approximate solution of the NK-DSGE model to construct its likelihood.  Next, the lecture notes discuss the choices often made for the priors of the parameters of the NK-DSGE model.  A short introduction to Markov chain Monte Carlo (MCMC) methods is followed by a similar brief review of the Metropolis-Hasting (MH) simulator.  The lecture notes conclude by applying a generic MH-MCMC sampler to the linear approximate solution of the NK-DSGE model and its likelihood.

Part II: VARs, Bayesian VARs, and Structural VARs

1. Introduction to Vector Autoregressions:  These lecture notes introduce student to VARs, begin a discussion of VARs and fundamentals, describe several methods to compute impulse response functions (IRFs) and forecast error variance decomposition (FEVDs), and presents a Bayesian algorithm to calculate error bands for IRFs and FEVDs conditional on a just-identified recursive ordering.  Directions are also provided for computing symmetric and asymmetric errors bands corrected for serial correlation using advice found in the classic 1999 paper by Chris Sims and Tao Zha.

2. Bayesian VARs, Priors, and Identification:  The second part of the course discusses priors for BVARs,  short and long run restrictions to construct structural VARs, and critiques of SVARs.  The discussion of short run identifying restrictions includes the classic 1998 paper by Chris Sims and Tao Zha on priors and estimation of BVARs given short run non-recursive restrictions.  The Blanchard-Quah (BQ) decomposition is the source of the long run restrictions.  There is a short digression on the Beveridge-Nelson decomposition to motivate the BQ decomposition.  Jon Faust and Eric Leeper develop a critique of long run identifying restrictions.  These notes also ground critiques of SVARs in recent papers by Jesús Fernández-Villaverde, Juan Francisco Rubio-Ramírez, Tom Sargent, and Mark Watson, Juan Francisco Rubio-Ramírez, Dan Waggoner, and Tao Zha, Raffaella Giacomini and Toru Kitagawa, and Christiane Baumeister and James Hamilton.

3. BVARs and Monetary Policy Evaluation:  Monetary policy evaluation is the last section of these lectures.  Several approaches are presented to identify monetary policy VARs.  Among these are papers by Alan Blinder and Ben Bernanke, Chris Sims, Steve Strongin, Ben Bernanke and Ilian Mihov, Lawrence Christiano, Marty Eichenbaum, and Charles Evans, Markku Lanne and Helmut Lutkepohl, Dave Gordan and Eric Leeper, Eric Leeper and Jennifer Roush, Jon Faust, Eric Swanson, and Jonathan Wright, and Eric Leeper and Tao Zha.  The focus is on the connection between SVAR identifying restrictions and economic models and monetary theory.  The notes conclude by presenting students with tools to estimate Markov-switching and time-varying parameter VARs with stochastic volatility.