Monetary Economics and Business Cycles (Econ 210D)

UCSD, Fall 2017

Course web page:

http://econweb.ucsd.edu/~jhamilto/Econ210D.html

Instructor:

James Hamilton

Econ 307 Tuesdays 11-12 (beginning Oct 10)

jhamilton@ucsd.edu

Themes:

(1) What do we know empirically about monetary policy and business cycles?

(2) How do we know it?

Course assignment:

Each student is asked to write a paper studying one of the questions or applying one of the methods discussed in the course. A simple replication and extension of a previous study would be adequate. The paper must include (in the student’s own well-crafted words with any quoted or paraphrased material very clearly noted and acknowledged) an introductory section motivating why the question is relevant and its relation to the literature, a second section providing a completely self-contained description of the method written for someone unfamiliar with the approach, a third section presenting results, a fourth section briefly concluding, and a reference section prepared consistent with American Economic Review or other major journal reference formatting conventions. Papers must be double-spaced with 1-1/2 inch right-hand margin and are due Wednesday December 6.

Lectures and replication files:

Links to slides with each day’s lectures as well as files for reproducing the material covered in class can be found at http://econweb.ucsd.edu/~jhamilto/Econ210D_slides.html

Daily reading list:

Starred items (*) are most important.

M Oct 2: No scheduled class

W Oct 4: Forecasts and vector autoregressions

(*) James D. Hamilton (1994), Time Series Analysis, Princeton University Press, Sections 10.1 and 11.1-11.4

Helmut Lütkepohl (2005), New Introduction to Multiple Time Series, Springer, Section 4.3.

Òscar Jordà (2005) “Estimation and Inference of Impulse Responses by Local Projections,” American Economic Review, 95(1), 161-182

Massimiliano Marcellino, James H. Stock, and Mark W. Watson (2006), “A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Series,” Journal of Econometrics 135, 499-526

James D. Hamilton (1994), Time Series Analysis, Princeton University Press, Sections 18.1-18.2

James D. Hamilton (2018), “Why You Should Never Use the Hodrick-Prescott Filter,” Review of Economics and Statistics, forthcoming

James D. Hamilton (2010), “Macroeconomics and ARCH,” in Festschrift in Honor of Robert F. Engle, pp. 79-96, edited by Tim Bollerslev, Jeffry R. Russell and Mark Watson, Oxford University Press

M Oct 9: Orthogonalization and structural VARs

(*) James D. Hamilton (1994), Time Series Analysis, Princeton University Press, Sections 11.4-11.6

Lawrence J. Christiano, Martin Eichenbaum, and Charles Evans (1996), “The Effects of Monetary Policy Shocks: Some Evidence from the Flow of Funds,” Review of Economics and Statistics, 78(1), 16-34

Hashem Pesaran and Yongcheol Shin (1998), “Generalized Impulse Response Analysis in Linear Multivariate Models”, Economics Letters, 58, 17-29

W Oct 11: Identification using long-run restrictions or heteroskedasticity

(*) Jordi Galí (1999), “Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?,” American Economic Review, 89(1), 249-271

V.V. Chari, Patrick J. Kehoe, and Ellen R. McGrattan (2008), “Are Structural VARs with Long-run Restrictions Useful in Developing Business Cycle Theory?,” Journal of Monetary Economics 55, 1337-1352

(*) Johathan Wright (2012), “What does Monetary Policy do to Long-term Interest Rates at the Zero Lower Bound?”, Economic Journal 122, pp.F447-F466

Ricardo Rigobon and Brian Sack (2004), “The Impact of Monetary Policy on Asset Prices,” Journal of Monetary Economics 51, 1553-1575

M Oct 16: Set identification using sign restrictions

Harald Uhlig (2005) “What are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure," Journal of Monetary Economics 52, 381-419

Juan F. Rubio-Ramírez, Daniel F. Waggoner, and Tao Zha (2010), “Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference,” Review of Economic Studies 77, pp. 665-696

Jonas E. Arias, Juan F. Rubio-Ramírez and Daniel F. Waggoner (2016), “Inference Based on SVARs Identified with Sign and Zero Restrictions: Theory and Applications,” working paper, Emory University

(*) Christiane Baumeister and James D. Hamilton (2015), “Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information,” Econometrica 83, 1963-1999

W Oct 18: No scheduled class

M Oct 23: Inference when identifying assumptions are doubted

(*) Christiane Baumeister and James D. Hamilton (2017), “Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role of Monetary Policy in Economic Fluctuations,” working paper, UCSD

Christiane Baumeister and James D. Hamilton (2015), “Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks,” working paper, UCSD

Bulat Gafarov, Matthias Meier, and José Luis Montiel Olea (2016), “Projection Inference for Set-Identifed SVARs,” working paper, Columbia University

Raffaella Giacomini and Toru Kitagawa (2015). “Robust Inference about Non-Identified SVARs,” working paper, University College London

W Oct 25: Event studies and high-frequency data

Kenneth N. Kuttner (2001), “Monetary Policy Surprises and Interest Rates: Evidence from the Fed Funds Futures Market,” Journal of Monetary Economics 47(3), 523-44

James D. Hamilton (2008), “Assessing Monetary Policy Effects Using Daily Federal Funds Futures Contracts,” Federal Reserve Bank of St. Louis Review July/August 2008, 377-393

James D. Hamilton (2008), “Daily Monetary Policy Shocks and New Home Sales,” Journal of Monetary Economics 55, 1171-1190

(*) Emi Nakamura and Jón Steinsson (2017), “High Frequency Identification of Monetary Non-Neutrality: The Information Effect,” Quarterly Journal of Economics, forthcoming

(*) Christina D. Romer and David H. Romer (2004), “A New Measure of Monetary Shocks: Derivation and Implications,” American Economic Review 94(4), 1055-1084

M Oct 30: IV estimation

(*) James H. Stock and Mark W. Watson (2012), “Disentangling the Channels of the 2007–09 Recession,” Brookings Papers on Economic Activity Spring 2012, pp. 81-130

Mark Gertler and Peter Karadi (2015), “Monetary Policy Surprises, Credit Costs, and Economic Activity”, with Mark Gertler, 2015, American Economic Journal: Macroeconomics 7(1): 44-76

Valerie Ramey (2016), “Macroeconomic Shocks and Their Propagation,” Handbook of Macroeconomics, Vol. 2, pp. 71-162 (edited by Harald Uhlig and John Taylor)

Nicola Fuchs-Schuendeln and Tarek Alexander Hassan (2015), “Natural Experiments in Macroeconomics,” Handbook of Macroeconomics vol. 2, pp. 923-1012 (edited by Harald Uhlig and John Taylor)

W Nov 1: Factor models

(*) James H. Stock, and Mark W. Watson (2016), “Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics,” Handbook of Macroeconomics, vol. 2, pp. 415-525 (edited by Harald Uhlig and John Taylor) Jushan Bai, and Serena Ng (2002), “Determining the number of factors in approximate factor models”, Econometrica 70, 191-221

Seung C. Ahn, and Alex R. Hornstein (2013), “Eigenvalue Ratio Test for the Number of Factors,” Econometrica 81, 1203-1227

James H. Stock, and Mark W. Watson (2002), “Forecasting Using Principal Components from a Large Number of Predictors,” Journal of the American Statistical Association 97, 1167-1179

Dominico Giannone, Lucrexzia Reichlin, and David Small (2008), “Nowcasting: The Real-Time Informational Content of Macroeconomic Data,” Journal of Monetary Economics 55, pp. 665-676

(*) Ben S. Bernanke, Jean Boivin and Piotr Eliasz (2005), “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach”, Quarterly Journal of Economics 120, 387–422

Bryan Kelly and Seth Pruitt (2015), “The Three-Pass Regression Filter: A New Approach to Forecasting Using Many Predictors,” Journal of Econometrics 186, 294-316

Hal R. Varian (2013), “Big Data: New Tricks for Econometrics,” http://people.ischool.berkeley.edu/~hal/Papers/2013/ml.pdf

M Nov 6: Introduction to term structure of interest rates

(*) John Y. Campbell, Andrew W. Lo, and A. Craig MacKinlay (1997), The Econometrics of Financial Markets, Princeton University Press, Chapter 10

Refet S. Gürkaynak, Brian Sack and Jonathan H. Wright (2007), “The U.S. Treasury Yield Curve: 1961 to the Present,” Journal of Monetary Economics 54(8), 2291-2304 (updated data at http://www.federalreserve.gov/econresdata/researchdata/feds200628.xls)

Refet S. Gürkaynak, Bryan Sack, and Eric T. Swanson (2005), “The Sensitivity of Long-Term Interest Rates to Economic News: Evidence and Implications for Macroeconomic Models,” American Economic Review 95, 425-436

W Nov 8: Affine term structure models

(*) Refet S. Gürkaynak and Jonathan H. Wright (2012), “Macroeconomics and the Term Structure,” Journal of Economic Literature 50, 331-367

James D. Hamilton and Jing Cynthia Wu (2012), “Identification and Estimation of Gaussian Affine Term Structure Models”, Journal of Econometrics 168, 315-331

Andrew Ang and Monika Piazzesi (2003), “A No-Arbitrage Vector Autoregression of Term Structure Dynamics with Macroeconomic and Latent Variables,” Journal of Monetary Economics 50, 745-787

Gregory R. Duffee (2011), “Information in (and Not in) the Term Structure,” Review of Financial Studies 24, 2895-2934

Michael D. Bauer and James D. Hamilton (2017), “Robust Bond Risk Premia,” Review of Financial Studies, forthcoming

Andrea Carriero and Raffaella Giacomini (2011), “How Useful are No-Arbitrage Restrictions for Forecasting the Term Structure of Interest Rates?,” Journal of Econometrics 164, 21-34

James D. Hamilton and Jing Cynthia Wu (2014), “Testable Implications of Affine-Term-Structure Models,” Journal of Econometrics 178, pp. 231-242

Jens H.E. Christensen, Francis X. Diebold, and Glenn D. Rudebusch (2011), “The Affine Arbitrage-free Class of Nelson-Siegel Term Structure Models,” Journal of Econometrics 164, 4-20

Michael D. Bauer, Glenn D. Rudebusch, and Jing Cynthia Wu (2012), “Correcting Estimation Bias in Dynamic Term Structure Models,” Journal of Business & Economic Statistics 30, 454-467

M Nov 13: Forward guidance

(*) Refet S. Gürkaynak, Brian Sack and Eric T. Swanson (2005), “Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements,” International Journal of Central Banking 1(1), 55-93

Jeffrey R. Campbell, et al. (2012), “Macroeconomic Effects of Federal Reserve Forward Guidance,” Brookings Papers on Economic Activity 2012:1, 1-54

Michael D. Bauer (2015), “Nominal Interest Rates and the News,” Journal of Money, Credit and Banking 47(2-3), 295-332

W Nov 15: Monetary policy at the zero lower bound: empirical evidence

James D. Hamilton (2013), “QE3 and Beyond,” http://www.econbrowser.com/archives/2013/01/qe3_and_beyond.html

Burcu Duygan-Bump, et al. (2013), “How Effective were the Federal Reserve Emergency Lending Facilities?,” Journal of Finance 68(2), 715-737

(*) Arvind Krishnamurthy and Annette Vissing-Jorgensen (2011), “The Effects of Quantitative Easing on Interest Rates: Channels and Implications for Policy,” Brookings Papers on Economic Activity 2011:2, 215-265

Eric T. Swanson (2017), “Measuring the Effects of Federal Reserve Forward Guidance and Asset Purchases on Financial Markets,” working paper UC Irvine

M Nov 20: Monetary policy at the zero lower bound: theory

Gauti Eggertsson and Michael Woodford (2003), “The Zero Bound on Interest Rates and Optimal Monetary Policy,” Brookings Papers on Economic Activity 2003:1, 139-211

James D. Hamilton and Jing Cynthia Wu (2012), “The Effectiveness of Alternative Monetary Policy Tools in a Zero Lower Bound Environment,” Journal of Money, Credit, and Banking 44(S1), 3-46

Jing Cynthia Wu and Fan Dora Xia (2016), “Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound,” Journal of Money, Credit and Banking 48(2-3), 253-291

James D. Hamilton (2017), “Comments on ‘Lower Bound Beliefs and Long-Term Interest Rates’,” International Journal of Central Banking, forthcoming

Michael D. Bauer and Glenn D. Rudebusch (2014), “The Signaling Channel for Federal Reserve Bond Purchases,” International Journal of Central Banking 10(3), 233-289

Jing Cynthia Wu and Dora Xia (2017), “Time-Varying Lower Bound of Interest Rates in Europe,” working paper, University of Chicago

Jing Cynthia Wu and Ji Zhang (2017), “A Shadow Rate New Keynesian Model,” working paper, University of Chicago

W Nov 22: Inflation: empirical evidence

James H. Stock and Mark W. Watson (1999), “Forecasting Inflation,” Journal of Monetary Economics 44, 293-335

Jon Faust and Jonathan H. Wright (2013), “Forecasting Inflation,” Handbook of Forecasting Vol. 2, 2-56

Judith A. Chevalier, Anil Kashyap and Peter E. Rossi (2003), “Why Don’t Prices Rise During Periods of Peak Demand? Evidence from Scanner Data” American Economic Review 93(1), 15-37

Mark J. Zbaracki, et al. (2004), “Managerial and Customer Costs of Price Adjustment: Direct Evidence from Industrial Markets,” Review of Economics and Statistics 86(2), 514–533

James D. Hamilton (2017), “Are We in a New Inflation Regime?”, http://econbrowser.com/archives/2017/07/are-we-in-a-new-inflation-regime

M Nov 27: Inflation: theoretical reconciliation

Olivier Coibion, Yuriy Gorodnichenko and Rupal Kamdar (2017), “The Formation of Expectations, Inflation, and the Phillips Curve,” NBER working paper 23304

Martin Eichenbaum, Nir Jaimovich and Sergio Rebelo (2011), “Reference Prices, Costs, and Nominal Rigidities,” American Economic Review 101(1), 234-262

Emi Nakamura and Jón Steinsson (2013), “Price Rigidity: Microeconomic Evidence and Macroeconomic Implications,” Annual Review of Economics 5, 133-163, 2013.

(*) Emi Nakamura, Jón Steinsson, Patrick Sun, and Daniel Villar (2017), “The Elusive Costs of Inflation: Price Dispersion During the Great Recession,” Quarterly Journal of Economics, forthcoming

Mikael Carlsson and Oskar Nordström Skans (2012), “Evaluating Microfoundations for Aggregate Price Rigidities: Evidence from Matched Firm-Level Data on Product Prices and Unit Labor Cost,” American Economic Review 102(4), 1571-1595

W Nov 29: Business cycles and changes in regime

(*) James D. Hamilton (2015), “Macroeconomics and Regime Shifts,” Handbook of Macroeconomics, Vol. 2, pp. 163-201 (edited by Harald Uhlig and John Taylor)

James D. Hamilton (1994), Time Series Analysis, Chapter 20

M Dec 4: Unemployment

(*) Hie Joo Ahn and James D. Hamilton (2017), “Heterogeneity and Unemployment Dynamics,” working paper, UCSD

Robert E. Hall (2017), “High Discounts and High Unemployment,” American Economic Review 107(2): 305–330

Stéphane Dupraz, Emi Nakamura, and Jón Steinsson (2017), “A Plucking Model of Business Cycles,” working paper, Columbia University

W Dec 6: Technology shocks and productivity

Robert M. Solow (1957), “Technical Change and the Aggregate Production Function,” Review of Economics and Statistics 39(3), 312-320

(*) Susanto Basu and John Fernald (2001), “Why Is Productivity Procyclical? Why Do We Care?” New Developments in Productivity Analysis, pp. 225-302, edited by Charles R. Hulten, Edwin R. Dean and Michael J. Harper, University of Chicago Press for NBER

Robert J. Gordon (2010), “Okun's Law and Productivity Innovations,” American Economic Review Papers and Proceedings 100(2), 11-15

Robert E. Hall (1988), “The Relation between Price and Marginal Cost in U.S. Industry,” Journal of Political Economy 96(5), 921-947

Chad Syverson (2004), “Market Structure and Productivity: A Concrete Example,” Journal of Political Economy 112(6), 1181-1222

Steven D. Levitt, John A. List and Chad Syverson (2013), “Toward an Understanding of Learning by Doing: Evidence from an Automobile Assembly Plant,” Journal of Political Economy 121(4), 643-681

Allan Collard-Wexler and Jan De Loecker (2015), “Reallocation and Technology: Evidence from the US Steel Industry,” American Economic Review 105(1), 131-71