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Topics in Macroeconometrics (ECO-AD-MACROMETR)

ECO-AD-MACROMETR


Department ECO
Course category ECO Advanced courses
Course type Course
Academic year 2024-2025
Term BLOCK 3
Credits 1 (EUI Economics Department)
Professors
Contact Simonsen, Sarah
Sessions

Purpose


 

Description

A course in Macroeconometrics. 20 hours.
The course will offer an overview of the econometric techniques used in the empirical analysis of monetary and fiscal policy as well as their empirical results.
The course has three specific objectives. The first is to equip students with the tools they need for empirical research on monetary and fiscal policy. The second objective is to lay out the econometric theory used in estimating the effects of economic policies, with an emphasis on recent developments. The third objective is to analyze selected recent empirical works.
  1. Overview of econometric techniques to estimate the effects of economic policies
  2. Selected works on monetary policy: identification, estimation and empirical results
  3. Selected works on fiscal policy: identification, estimation and empirical results
 

Selected bibliography


General reference: Kilian and Lutkepohl (2017), Structural Vector Autoregressive Anal- ysis, Cambridge University Press.
 
    1. Motivation and Empirical applications (to be discussed throughout the lectures)
  • Ramey, V. (2011). Identifying government spending shocks: it’s all in the timing. Quar- terly Journal of Economics, 126(1), pp. 1-50.
  • Ramey, V. (2011). Can government purchases stimulate the economy? Journal of Eco- nomic Literature, 49(3), 673-85.
  • Ramey, V. and Zubairy, S. (2018). Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data. Journal of Political Economy 126(2).
  • Lawrence J. Christiano, Martin Eichenbaum, Charles L. Evans (1999). Monetary policy shocks: What have we learned and to what end?, Handbook of Macroeconomics, Elsevier, Volume 1, Part A, Chapter 2, 65-148.
  • Nakamura, Emi, and Jon Steinsson (2018). Identification in Macroeconomics. Journal of Economic Perspectives 32(3), 59-86.
  • Rossi, B. (2019), Identifying and Estimating the Effects of Unconventional Monetary Pol- icy in the Data: How to Do It And What Have We Learned?, Economic Journal.
 
    1. Impulse response functions, variance decompositions, inference
  • Stock, J. and Watson, M. (2001). Vector autoregressions. Journal of Economic Perspec- tives, 15(4), pp. 101-15.
  • Watson, M. Vector autoregressions and cointegration. In: R.F. Engle and D.L. McFadden, eds. 1994. Handbook of Econometrics, Volume 4. Amsterdam: Elsevier-North Holland. Ch. 47, Section 3.
 
    1. Long-run restrictions
  • Blanchard, O., and Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79(4), 655-73.
  • GalA˜, J. (1999). Technology, employment, and the business cycle: do technology shocks
 
explain aggregate fluctuations? American Economic Review, 89(1), 249-71.
  • Pesavento, E. and Rossi, B. (2005). Do technology shocks drive hours up or down? Macroe- conomic Dynamics 9(4), 478-88.
 
    1. Other identification procedures
  • Rigobon, R., 2003. Identification through heteroskedasticity. Review of Economics and Statistics, 85(4), pp. 777-92.
  • Wright, J. (2012), What does Monetary Policy do to Long-Term Interest Rates at the Zero Lower Bound?, Economic Journal.
  • Uhlig, H. (2005). What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics 52(2), 381-419.
  • Montiel-Olea, Stock and Watson (2018). Inference in Structural VARs with External In- struments, Economic Journal.
  • Rigobon (2003). Identification through Heteroskedasticity. The Review of Economics and Statistics 85- 4, 777-792
 
    1. Inference for impulse responses
  • Kilian, L. (1999). Finite-sample properties of percentile and percentile-t bootstrap confi- dence intervals for impulse responses. The Review of Economics and Statistics 81(4), 652-60.
  • Pesavento, E. and Rossi, R. (2007). Impulse response confidence intervals for persistent data: what have we learned? Journal of Economic Dynamics and Control 31(1), 2398-2412.
  • Wright, J. (2000). Confidence intervals for univariate impulse responses with a near unit root. Journal of Business and Economic Statistics 18(3), 368-73.
 
    1. Local projections
  • Jorda’, Oscar (2005). Estimation and Inference of Impulse Responses by Local Projections. American Economic Review, 95(1), 161-182.
  • Stock, J. And M. Watson (2018). Identification and Estimation of Dynamic Causal Effects in Macroeconomics, Economic Journal, 28(610), 917-948.
  • Plagborg-Moller, M. and Wolf, C.K. (2021). Local Projections and VARs Estimate the Same Impulse Responses. Econometrica, 89, 955-980.
  • Plagborg-Moller, M. and Jose’ Luis Montiel Olea (2021). Local Projection Inference is Simpler and More Robust Than You Think. Econometrica 89(4), 1789-1823.

Teaching material

    • Journal articles and other assigned readings.
 

Evaluation

The evaluation will be based on in-class presentations (10%), as well as 3 homeworks (10%) and a final exam (80%).
Back to Overview
 

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Page last updated on 05 September 2023