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Time Series Models for Macroeconomic Analysis

Time Series Models for Macroeconomic Analysis

Learn with our expert faculty

facultyLuca Gambetti
PhD, Universitat Pompeu Fabra
UAB and BSE
Director Instructor

Course overview

Understanding the transmission mechanisms of macroeconomic shocks is key to design effective economic policies and assess macroeconomic theories. This course offers you the latest and most important techniques and methods to identify structural shocks and measure their effects. After this course, which include both theory sessions and hands-on exercises in practical sessions, you will be able to apply these methods to your own research.

The objective of this course is twofold:

  • First, we will look at some of the most popular time series models designed to analyze propagation mechanisms and measure the effects of economic shocks.
  • Second, we will discuss and present some recent empirical applications, focusing on monetary and fiscal policy shocks as well as non-policy shocks such as technology shocks, news shocks and uncertainty shocks.

The first part of the course will cover Structural Vector Autoregressive models, with special attention to shock identification strategies. It will also present a nonlinear extension of the standard model represented by the Smooth Transition SVAR.

In the second part of the course, we will discuss how to perform structural macroeconomic analysis with large-N structural models, namely Factor Augmented SVARs and Factor Models. Special emphasis will be put on how these models can cope with the problem of limited information sets typically arising in SVARs.

Learn state-of-the-art shock identification techniques for macroeconomic and policy analysis

The course includes theory sessions (10 hours) and practical sessions (10 hours). In the practical sessions, you will work in MATLAB to implement the techniques and methodologies covered in the theory lectures.

You will participate in an active and fruitful environment with international colleagues without incurring high costs, thanks to the online live delivery of this course.

Sessions will be recorded and videos will be available for a month once the course has finished.

Learn how to apply modern time series methods to your own research

This course will be taught online but it will be live and interactive.

INTENSIVE COURSE

Time Series Models for Macroeconomic Analysis

Applications will open soon!
  ONLINE
Regular Fee 1300 €
Reduced Fee 775 €

10% early-bird discount applies to payments made on or before January 5, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Early-bird payment deadline: January 5, 2024

  ONLINE
Regular Fee 1300 €
Reduced Fee 775 €

10% early-bird discount applies to payments made on or before January 5, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Last day to apply: February 1, 2024

  ONLINE
Regular Fee 1300 €
Reduced Fee 775 €

10% early-bird discount applies to payments made on or before January 5, 2024 at 23:59 (CET)

See below for reduced fee eligibility

This edition is closed. Next edition TBA.

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Schedule

Times listed are Central European Time (CET). Compare with your time zone on time.is

Schedule for Week 1
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Lecture Practical Lecture Practical Lecture
Schedule for Week 2
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Practical Lecture Practical Lecture Practical

Course content

Read an outline of topics and bibliography for this course

Course outline

1. Structural VAR (SVAR) models

Foundations of SVAR models

  1. Representation and estimation.
  2. Impulse response functions, variance decomposition and historical decomposition analysis
  3. Shocks identification: Cholesky, long-run restrictions, sign restrictions, penalty function approach, mixed restrictions, narrative approach.
  4. Applications: Monetary and fiscal policy shocks, technology shocks, news shocks, uncertainty shocks, financial shocks.

Advances in SVAR models

  1. External instruments SVAR (SVAR-IV): Monetary policy shocks.
  2. External instruments Local Projections (LP-IV).
  3. Smooth Transition SVAR: Government spending shocks in booms and recessions.
  4. Quantile SVARs: Asymmetries of financial and monetary policy shocks.

2.    Structural Factor Augmented VAR and Structural Factor Models 

  1. Limited information in SVAR: Nonfundamentalness and Noninvertibility.
  2. Representation and estimation of Dynamic Factor Models and and Factor Augmented VAR.
  3. Impulse response functions and variance decomposition analysis.
  4. Shocks identification: Cholesly, sign restrictions, long-run restrictiions, external instruments.
  5. Application: Monetary policy shocks.
  6. Testing for Nonfundmentalness and Noninvertibility in VAR models.

References

  • Amengual, D. and M.W. Watson (2007). Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel, Journal of Business and Economic Statistics 25, 91-96.
  • Auerbach, A. and Y. Gorodnichenko, 2012. "Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 1-27, May.
  • Bai, J. (2003). Inferential Theory for Factor Models of Large Dimensions, Econometrica 71, 135-171.
  • Bai, J., and S. Ng (2002). Determining the number of factors in approximate factor models, Econometrica 70, 191-221.
  • Bai, J., and S. Ng (2007). Determining the Number of Primitive Shocks in Factor Models, Journal of Business and Economic Statistics 25, 52-60.
  • Barsky, R. and E. Sims (2012), “Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence”, American Economic Review 102, 1343-77.
  • Barsky, R. and E. Sims (2011), “News shocks and business cycles”, Journal of Monetary Economics 58, 273-289.
  • Beaudry, P. and F. Portier (2004), “Exploring Pigou’s Theory of Cycles”, Journal of Monetary Economics 51, 1183-1216.
  • Beaudry, P. and F. Portier (2006), “Stock Prices, News, and Economic Fluctuations”, American Economic Review 96, 1293-1307.
  • Bernanke, B. S., Boivin, J. and Eliasz, P., 2005, Measuring monetary policy: a factor augmented autoregressive (FAVAR) approach, The Quarterly Journal of Economics 120, pp. 387-422.
  • Blanchard, O.J. and R. Perotti (2002). An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output, The Quarterly Journal of Economics: 1329-1368.
  • Boivin, J.,. M. P. Giannoni and I. Mihov, 2009. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," American Economic Review, American Economic Association, vol. 99(1), pages 350-84,
  • Boivin,J., M. P.  Giannoni,, and D.  Stevanovic, 2013. "Dynamic Effects of Credit Shocks in a Data-Rich Environment,"CEPR Discussion Papers 9470, C.E.P.R. Discussion Papers.
  • Buch, C. M., S. Eickmeier, and  E. Prieto, 2014. "Macroeconomic Factors and Microlevel Bank Behavior," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 715-751,
  • Burnside, C., M. Eichenbaum, and J. D. Fisher (2004). Fiscal Shocks and Their Consequences. Journal of Economic Theory 115, 89-117.
  • Chari, V.V., Kehoe, P.J. and E.R. McGrattan (2008), “Are structural VARs with long-run restrictions useful in developing business cycle theory?”, Journal of Monetary Economics 55, pp. 1337-1352.
  • Cochrane, John H. (1994), “Shocks”, Carnegie-Rochester Conference Series on Public Policy 41, 295-364.
  • Eichenbaum, M. and J. D. Fisher (2005). Fiscal Policy in the Aftermath of 9/11. Journal of Money, Credit and Banking 37 (1), 1-22.
  • Fatas, A. and I. Mihov (2001). The Effects of Fiscal Policy on Consumption and Employment: Theory and Evidence. CEPR Discussion Paper No. 2760.
  • Fernandez-Villaverde, J., J. F. Rubio, T. Sargent and M. Watson (2007), “A, B, C, (and D)’s for Understanding VARs”, American Economic Review 97, pp. 1021-1026.
  • Edelberg, W., M. Eichenbaum, and J. D. Fisher (1999). Understanding the Effects of a Shock to Government Purchases. Review of Economic Dynamics 2 (1), 166-206.
  • Fisher J.D.M. and R. Peters (2010). Using Stock Returns to Identify Government Spending Shocks. The Economic Journal 120, 414-436.
  • Feve, P., Matheron, J. and Sahuc, J.G. , 2009, On the dynamic implications of news shocks", Economics Letters 102, pp. 96-98.
  • Forni, M. and L. Gambetti, (2014), “Sufficient information in structural VARs”, Journal of Monetary Economics 66, pp. 124-136.
  • Forni, M., L. Gambetti and L. Sala (2014), “No News in Business Cycles”, The Economic Journal.
  • Forni, M., D. Giannone, M. Lippi and L. Reichlin (2009), “Opening the Black Box: Structural Factor Models with Large Cross-Sections”, Econometric Theory 25, pp. 1319-1347.
  • Gali, J., D. Lopez Salido, and J. Valles (2007). Understanding the Effects of Government Spending on Consumption. Journal of the European Economic Association 5, 227-270.
  • Gali J, and Gambetti L. (2009). On the sources of the Great Moderation. American Economic Journal: Macroeconomics, 1(1):26-57.
  • Gali J, and Gambetti L. The Effects of Monetary Policy on Stock Market Bubbles: Some Evidence. Forthcoming  American Economic Journal: Macroeconomics, 1(1):26-57.
  • Gambetti, L. (2020), ‘’Structural Vector Autoregressive Models’’, Oxford Research Encyclopedia of Economics and Finance, Oxford University Press, August.
  • Gambetti, L. (2021), ‘’Shocks, Information and Structural VARs’’ Oxford Research Encyclopedia of Economics and Finance, Oxford University Press, April.
  • Giannone, D., and L. Reichlin (2006), “Does Information Help Recovering Structural Shocks from Past Observations?”, Journal of the European Economic Association 4, pp. 455-465.
  • Gilchrist, S., V. Yankov, and E. Zakrajsek (2009): “Credit Market Shocks and Economic Fluctuations: Evidence From Corporate Bond and Stock Markets,” Journal of Monetary Economics, 56, 471–493.
  • Granger, Clive W., and Timo Terasvirta. 1993. Modelling Nonlinear Economic Relationships. New York: Oxford University Press.
  • Hamilton J. D. 1994, Time Series Analysis, Princeton University Press: Princeton.
  • Hansen, L.P., and T.J. Sargent (1991), “Two problems in interpreting vector autoregressions”, in Rational Expectations Econometrics, L.P. Hansen and T.J. Sargent, eds. Boulder: Westview, pp.77-119.
  • Koop, Gary, M. Hashem Pesaran, and Simon M. Potter. 1996. “Impulse Response Analysis in Nonlinear Multivariate Models.” Journal of Econometrics 74(1): 119-147.
  • Leeper, E. M., Walker, T. B. and Yang, S.-C. S. (2013), “Fiscal Foresight and Information Flows”, Econometrica, 81: 11151145
  • Lippi, M. and L. Reichlin (1993), “The Dynamic Effects of Aggregate Demand and Supply Disturbances: Comment”, American Economic Review 83, 644-652.
  • Lippi, M. and L. Reichlin (1994), “VAR analysis, non fundamental representation, Blaschke matrices”, Journal of Econometrics 63, 307-325.
  • Lutkepohl, H.  2005, New Introduction to Multiple Time Series, Springer-Verlag: Berlin.
  • Mertens and M. O. Ravn, (2010). "Measuring the Impact of Fiscal Policy in the Face of Anticipation: A Structural VAR Approach," Economic Journal, Royal Economic Society, vol. 120(544), pages 393-413, 05.
  • Mertens, K and M. O Ravn, (2011). "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 27-54,
  • Mertens, K. & M.O. Ravn, 2014. "A reconciliation of SVAR and narrative estimates of tax multipliers," Journal of Monetary Economics, Elsevier, vol. 68(S), pages S1-S19.
  • Mountford, A. and H. Uhlig (2002). What Are the Effects of Fiscal Policy Shocks. CEPR Discussion Paper No. 3380.
  • Perotti, R. (2007). In Search of the Transmission Mechanism of Fiscal Policy. NBER Macroeconomics Annual.
  • Perotti, R. (2011). Fiscal Policy and Expectations: An Empirical Investigation.
  • Ramey, V. (2011a), “Identifying Government Spending Shocks: It’s All in the Timing.” Quarterly Journal of Economics 126 (1): 1–50.
  • Ramey, V.A. (2011b), “Can Government Purchases Stimulate the Economy?” Journal of Economic Literature 49 (3): 673–85.
  • Ramey, V.A. (2016), Macroeconomic Shocks and Their Propagation, Handbook of Macroeconomics.
  • Ravenna, F. (2007). Vector autoregressions and reduced form representations of DSGE models, Journal of Monetary Economics 54(7): 2048-2064.
  • Rozanov, Yu. (1967). Stationary Random processes, San Francisco: Holden Day.
  • Romer, C.D., and D. H.. Romer. 2010. “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks.” American Economic Review 100:763–801.
  • Schmitt-Grohe, S. and Uribe, M., 2008, What's news in business cycles", NBER Working Paper no. 14215.
  • Sims, C. A. 1980. Macroeconomics and Reality. Econometrica, 48 (1), 148.
  • Sims, E., 2011, News, non-invertibility, and structural VARs, mimeo, University of Notre Dame.
  • Stock, J. H. and Watson, M. W., 2002, Macroeconomic forecasting using diffusion indexes", Journal of Business and Economic Statistics 20, pp. 147-162.
  • Stock, J. H. and Watson, M. W. , 2005, Implications of dynamic factor models for VAR analysis", NBER Working Paper no. 11467.
  • Stock, James H. and Mark W. Watson, 2012, “Disentangling the Channels of the 2007-2009 Recession”, Brookings Panel on Economic Activity, Conference Paper March 22-23.
  • Uhlig, H. (2005). What are the effects of monetary policy on output? Results from an agnostic identification procedure. Journal of Monetary Economics 52, 381-419.

Prerequisites

This is an intermediate-level course for students and practitioners who, in order to follow and get the most out of it, are expected to have some background and basic understanding of econometrics and time series econometrics.  

Course materials

Every participant will receive a time-limited personal free license of MATLAB several days before the start of the course. You will need to install the MATLAB software on your own computer for use during the practical sessions.

Additional class materials will be provided before and during the sessions. The instructors will also be available throughout the course to discuss your individual research ideas and projects.

Participant Profile

This course is specifically aimed at:

  • Researchers who want to use the latest advances in macroeconometrics

  • Masters and PhD students who want to extend their knowledge in macroeconometrics and learn more about frontier research topics

  • Practitioners at central banks as well as other private and public institutions who wish to update their knowledge and acquire the most recent techniques, tools, and methodologies

Fees

Discounts are available for participants with predefined criteria, please check the BSE Executive Education Courses Policies [pdf] for more information. 

 

* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.

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