Uncertainty and Macroeconomics 101
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About the Course

This course provides an introduction to Uncertainty and Macroeconomics. It is a prerequisite for 2 hands-on courses that will be scheduled in first half of 2022.  The first hands-on course (Uncertainty and Macroeconomics 201P and 201R) uses Python or R to develop measures of uncertainty based on text scraping.  The second (Uncertainty and Macroeconomics 301) is a short course about a toolbox that is used for developing and solving macroeconomic models where uncertainty is an important source of shocks and propagation mechanisms in the economy.     

 

Uncertainty and Macroeconomics 101. This course provides an introduction to the methods used to solve macroeconomic models with uncertainty shocks and discusses most of the recent contributions in the literature.  This course is intended for people that want to have a general understanding of this literature as well as for people that are interested in doing technically-demanding research in the area.  As an introduction to some basic tools, the course covers Python or R basics and some simple applications such as regression analysis examining the behavior of U.S. consumer expenditures in response to COVID19-related shocks. Participants must choose either Python (or R0. The objective here would be to devote 2 hours to getting participants up and running in Python (or R) and to replicate some basic analysis of US consumer expenditures using examples of data, regression, and text analysis. This should provide a flavor for people that are interested in Python (or R) and would like to start with some very basic applications that they might typically do in non-open-source software. Example of R report on US consumer expenditures can be found here.

Part 1 of the Course: Participants will study the empirical regularities behind uncertainty shocks. Questions to be answered include what is the difference between risk and uncertainty? What are the different types of uncertainty shocks? How much do uncertainty shocks matter for the economy?

Part 2 of the Course: Participants will be introduced to the quantitative tools used to analyse models with uncertainty shocks. Topics include, but not limited to: value function iteration; projection methods; generalized impulse response functions; and filtering.

Part 3 of the Course: Participants will be exposed to the different types of macro models in the literature that feature uncertainty. Participants will study selected topics such as the interaction between uncertainty and: 1) precautionary savings; 2) pricing decisions; 3) investment decisions; 4) fiscal and monetary policies; and 5) financial frictions.

 

The course will last 4 days. Participants will have access to 4 hours of Zoom training each day. There will be a total of 4 hours that will be devoted to hands-on work in either Python or R. Participants will have to choose if they would like to be in either the Python group or the R group. It is the responsibility of the participants to ensure that they have Python or R running on their computers. People that need additional support installing and managing open-source software  like Python and R, can contact Asya Kostanyan at the Better Policy Project here.

 

Reading Material

 

Topics

Uncertainty Shocks – Theory. In this part of the course, we will study theoretical aspects of uncertainty.

  • Precautionary behavior: (Fernandez-Villaverde and Guerron-Quintana, 2020), (Bloom, 2014), (Basu and Bundick, 2017), (Bayer, Luetticke, Pham-Dao, and Tjaden, 2019).

  • Oi-Harman-Abel effect: (Fernandez-Villaverde and Guerron-Quintana, 2020), (Bloom, 2014), (Fernandez-Villaverde et al., 2015).

  • Real frictions: (Abel and Eberly, 1993), (Fernandez-Villaverde and Guerron-Quintana, 2020), (Bloom, 2009).

Quantitative Methods for Models with Uncertainty Shocks. In this part of the course, participants will learn some of the tools that macroeconomists use to study uncertainty. The list of topics includes:

  • Integration and optimization (Fernandez-Villaverde, Guerron-Quintana, and Valencia, 2021), (Judd, 1998)

  • Value function iteration (Fernandez-Villaverde et al., 2021), (Judd, 1998), (Hatchondo, Martinez, and Sapriza, 2010), (Gordon, 2019).

  • Pertubation & projection methods, (Fernandez-Villaverde, Rubio-Ramirez, and Schorfheide, 2016), (Fernandez-Villaverde and Guerron-Quintana, 2020), (Judd, 1998), (Heer and Maussner, 2009).

 

Uncertainty Shocks – Empirical Facts. In this part of the course, participants will study some empirical regularities behind uncertainty.

  • Defining risk and uncertainty (uncertainty and Knightian uncertainty; objective and subjective uncertainty): (Bloom, 2014), (Guerron-Quintana, 2012), (Fernandez-Villaverde and Guerron-Quintana, 2020), (Cascaldi-Garcia, Sarisoy, Londono, Rogers, Datta, Ferreira, Grishchenko, Jahan-Parvar, Loria, Ma, Rodriguez, and Zer, 2020), (Knight, 1921).

  • How do we measure uncertainty?

  • Econometric Models (Fernandez-Villaverde and Guerron-Quintana, 2020), (Fernandez-Villaverde, Guerron-Quintana, Rubio-Ramirez, and Uribe, 2011), (Fernandez-Villaverde, Guerron-Quintana, Kuester, and Rubio-Ramirez, 2015), (Jurado, Ludvigson, and Ng, 2015).

  • Proxies of uncertainty (Fernandez-Villaverde and Guerron-Quintana, 2020), (Bloom, 2009), (Bloom, 2014).

  • Counting occurrences (Fernandez-Villaverde and Guerron-Quintana, 2020), (Baker, Bloom, and Davis, 2016).

  • Surveys of subjective uncertainty (Fernandez-Villaverde and Guerron-Quintana, 2020), (Altig, Barrero, Bloom, Davis, Meyer, and Parker, 2020).

  • Is uncertainty endogenous or exogenous? (Ludvigson, Ma, and Ng), (Bachman and Moscarini, 2012).

  • Does uncertainty matter for economic activity? (Bloom, 2009), (Fernandez-Villaverde et al., 2011), (Fernandez-Villaverde et al., 2015), (Jurado et al., 2015).

Some Applications:

  • Financial frictions & uncertainty (Fernandez-Villaverde and Guerron-Quintana, 2020) and (Arellano, Bai, and Kehoe, 2019).

  • Fiscal policy & uncertainty (Fernandez-Villaverde et al., 2015).

  • Monetary policy & uncertainty (Vavra, 2013).

  • Business cycles & uncertainty (Bloom, Floetotto, Jaimovich, Saporta-Eksten, and Terry, 2018), (Bachmann and Bayer, 2013), (Fernandez-Villaverde, Guerron-Quintana, and Rubio-Ramirez, 2010) and (Justiniano and Primiceri, 2008).

  • Labor markets & uncertainty (Schaal, 2017), (Leduc and Liu, 2016), and (Cacciatore and Ravenna, 2020).

  • Sovereign risk & uncertainty (Seoane, 2019) and (Johri, Khan, and Sosa-Padilla, 2020).

  • Households & uncertainty (Bayer et al., 2019) and (Harmenberg and Oberg, 2021).

Special Presentation by Felix Delbrück, Director of Economist's Intelligence Unit (EIU) Country Risk Service. EIU is dedicated to helping businesses, financial firms and governments to navigate the ever-changing global landscape, by providing independent, impartial research that passes through rigorous checks to ensure the highest level of accuracy. In this capacity, Felix has been developing plans to close the vast gap between modelling used to support Forecasting and Policy Analysis Systems in central banks and the modelling, data and forecasting tools currently used in the private sector. He is also keen to exploit the explosion in new research opportunities created by the move to open source software and data.  

References:

  • A. B. Abel and J. C. Eberly. A unified model of investment under uncertainty. Working Paper 4296, National Bureau of Economic Research, March 1993.

  • D. Altig, J. M. Barrero, N. Bloom, S. J. Davis, B. Meyer, and N. Parker. Surveying business uncertainty. Journal of Econometrics, 2020.

  • C. Arellano, Y. Bai, and P. J. Kehoe.Financial frictions and fluctuations in volatility.Journal of Political Economy, 127(5):2049–2103, 2019.

  • R. Bachman and G. Moscarini. Business cyckes and endogenous uncertainty. Technical report, University of Notre Dame, 2012.

  • R. Bachmann and C. Bayer. Wait-and-see business cycles? Journal of Monetary Economics, 60(6):704–719, 2013. ISSN 0304-3932.

  • S. R. Baker, N. Bloom, and S. J. Davis. Measuring Economic Policy Uncertainty*. The Quarterly Journal of Economics, 131(4):1593–1636, 07 2016.

  • S. Basu and B. Bundick. Uncertainty shocks in a model of effective demand. Econometrica, 85(3):937–958, 2017.

  • C. Bayer, R. Luetticke, L. Pham-Dao, and V. Tjaden. Precautionary savings, illiquid assets, and the aggregate consequences of shocks to income risk. Econometrica, 87(1), 2019.

  • N. Bloom. The impact of uncertainty shocks. Econometrica, 77(3):623–685, 2009.

  • N. Bloom. Fluctuations in uncertainty. Journal of Economic Perspectives, 28(2):153–76, May 2014.

  • N. Bloom, M. Floetotto, N. Jaimovich, I. Saporta-Eksten, and S. J. Terry. Really uncertain business cycles. Econometrica, 86(3):1031–1065, 2018.

  • M. Cacciatore and F. Ravenna. Uncertainty, wages, and the business cycle. Technical Report DP14715, CEPR, 2020.

  • D. Cascaldi-Garcia, C. Sarisoy, J. Londono, J. Rogers, D. Datta, T. Ferreira, O. Grishchenko, M. R. Jahan-Parvar, F. Loria, S. Ma, M. Rodriguez, and I. Zer. What is certain about uncertainty? Working paper International Finance Discussion Papers 2020.1294, 2020.

  • J. Fernandez-Villaverde and P. A. Guerron-Quintana. Uncertainty shocks and business cycle research. Review of Economic Dynamics, 37:S118–S146, 2020.ISSN 1094-2025. The twenty-fifth anniversary of “Frontiers of Business Cycle Research”.

  • J. Fernandez-Villaverde, P. Guerron-Quintana, and J. F. Rubio-Ramirez. Fortune or virtue: Time-variant volatilities versus parameter drifting in u.s. data. Working Paper 15928, National Bureau of Economic Research, April 2010.

  • J. Fernandez-Villaverde, P. Guerron-Quintana, J. Rubio-Ramirez, and M. Uribe. Risk matters: The real effects of volatility shocks. American Economic Review, 101(6):2530–2561, 2011.

  • J. Fernandez-Villaverde, P. Guerron-Quintana, K. Kuester, and J. Rubio-Ramirez. Fiscal volatility shocks and economic activity. American Economic Review, 105(11):3352–84, 2015.

  • J. Fernandez-Villaverde, J. Rubio-Ramirez, and F. Schorfheide. Chapter 9 - solution and estimation methods for dsge models. volume 2 of Handbook of Macroeconomics, pages 527–724. Elsevier, 2016.

  • J. Fernandez-Villaverde, P. Guerron-Quintana, and D. Z. Valencia. Lectures on quantitative methods in macroeconomics. 2021.

  • G. Gordon. Efficient computation with taste shocks. Working paper no. 19-15, Federal Reserve Bank of Richmond, 2019.

  • P. Guerron-Quintana. Risk and uncertainty. Business Review, (Q1):9–18, 2012.

  • K. Harmenberg and E. Oberg. Consumption dynamics under time-varying unemployment risk. Journal of Monetary Economics, 118:350–365, 2021.

  • J. Hatchondo, L. Martinez, and H. Sapriza. Quantitative properties of sovereign default models: Solution methods matter. Review of Economic Dynamics, 13(4):919–933, 2010.

  • B. Heer and A. Maussner. Dynamic general equilibrium modeling. Springer, 2009.

  • A. Johri, S. Khan, and C. Sosa-Padilla. Interest rate uncertainty and sovereign default risk. Technical report, University of Notre Dame, 2020.

  • K. Judd. Numerical methods in economics. Mit press, 1998.

  • K. Jurado, S. C. Ludvigson, and S. Ng. Measuring uncertainty. American Economic Review, 105(3):1177–1216, March 2015.

  • A. Justiniano and G. E. Primiceri. The time-varying volatility of macroeconomic fluctuations. American Economic Review, 98(3):604–41, June 2008.

  • F. H. Knight. The Measurement of Durable Goods Prices. Hart, Schaffner & Marx, Boston,

  • 1921.

  • S. Leduc and Z. Liu. Uncertainty shocks are aggregate demand shocks. Journal of Monetary Economics, 82:20–35, 2016.

  • S. Ludvigson, S. Ma, and S. Ng. Uncertainty and business cycles: Exogenous impulse or endogenous response? Forthcoming American Economic Journal: Macroeconomics.

  • E. Schaal. Uncertainty and unemployment. Econometrica, 85(6):1675–1721, 2017. Introduction to

  • H. D. Seoane. Time-varying volatility, default, and the sovereign risk premium. International Economic Review, 60(1):283–301, 2019.

  • J. Vavra. Inflation Dynamics and Time-Varying Volatility: New Evidence and an Ss Interpretation *. The Quarterly Journal of Economics, 129(1):215–258, 09 2013.

Prerequisites

Graduate/Undergraduate degree in Economics and curiosity about the subject matter 
 

The Outcomes from the Course
 

  • Certificate of completion.

  • Prerequisite for Uncertainty and Macroeconomics 201 and 301.

  • Possible coaching time on research projects

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Pablo Guerrón-Quintana

Boston College

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Douglas Laxton

The Better Policy Project

Price : 1000 Euros*

Dates: February 15 - 18, 2022

Time: 13:00 - 17:00 Lisbon Time (GMT +0)

*Discounts available. Contact us to learn more.

Price : 1000 Euros*

Dates: February 15 - 18, 2022

Time: 13:00 - 17:00 Lisbon Time (GMT +0)

*Discounts available. Contact us to learn more.