|Date:||08:30 14 March 2016 - 13:00 15 March 2016|
|Organiser:||Morten Ravn University College London|
|Venue:||Institute for Fiscal Studies|
This series of lectures will introduce students to the recent literature on the macroeconomics of uncertainty shocks (also known as volatility shocks).
Macroeconomics is concerned with the dynamic effects of shocks. For instance, the real business cycle research program originated with an investigation of the consequences of changes in productivity. Later, the new generation of monetary models of the late 1990s and early 2000s was particularly focused on shocks to monetary policy. In open macroeconomics, considerable attention has been devoted to shocks to the interest rate (Mendoza, 1991) or to the terms of trade. Similar examples can be cited from dozens of other subfields of macroeconomics, from asset pricing to macro public finance: researchers postulate an exogenous stochastic process and explore the consequences for prices and quantities of innovations to it.
Traditionally, one key feature of these stochastic processes was the assumption of homoscedasticity. More recently, however, economists have started to relax this assumption. In particular, they have started considering shocks to the variance of the innovations of the processes.
A first motivation for this new research comes from the realization that time series have a strong time-varying variance component. The most famous of those episodes is the great moderation of aggregate fluctuations in the U.S. between 1984 and 2007, when real aggregate volatility fell by around one third and nominal volatility by more than half. A natural mechanism to generate these changes is to have shocks that also have themselves a time-varying volatility and to trace the effects of changes in volatility on aggregate dynamics.
In these lectures, we want to study time-varying volatility with the help of DSGE models, the workhorse of modern macroeconomics and the most common laboratory for policy evaluation.
How do we incorporate time-varying volatility in the models? How do we solve models with this time-varying volatility? How do we take them to the data? What are the policy implications of volatility?
Examples of papers in the area include Bloom (2009), Fernandez-Villaverde, Guerron-Quintana, Rubio-Ramirez, and Uribe (2011), and Fernandez-Villaverde, Guerron-Quintana, Kuester, and Rubio-Ramirez (2015). Some more methodological papers are Caldara, Fernandez-Villaverde, Rubio-Ramirez, and Yao (2012) and Fernandez-Villaverde, Guerr_on-Quintana, and Rubio-Ramirez (2015).
A familiarity with a first-year graduate school sequence in macroeconomics is required. Some experience with DSGE models can be helpful, but it is not crucial.
Bloom, N. (2009): “The Impact of Uncertainty Shocks," Econometrica, 77, 623-685.
Caldara, D., J. Fern_andez-Villaverde, J. F. Rubio-Ramirez, and W. Yao (2012):
“Computing DSGE Models with Recursive Preferences and Stochastic Volatility," Review of Economic Dynamics, 15, 188-206.
Fernandez-Villaverde, J., P. Guerron-Quintana, K. Kuester, and J. Rubio-Ramirez (2015): “Fiscal Volatility Shocks and Economic Activity," American Economic
Review, 105(11), 3352-84.
Fernandez-Villaverde, J., P. A. Guerron-Quintana, and J. F. Rubio-Ramirez
(2015): “Estimating Dynamic Equilibrium Models with Stochastic Volatility," Journal of
Econometrics, 185, 216-229.
Fernandez-Villaverde, J., P. A. Guerron-Quintana, J. F. Rubio-Ramirez, and
M. Uribe (2011): “Risk Matters: The Real Effects of Volatility Shocks," American
Economic Review, 101, 2530-2561.
If you have any further queries, please contact the Event Manager, Nirusha Vigi