CEMFI Summer School 2019


CEMFI SUMMER SCHOOL offers top-level training for practitioners, central bankers, and academics. Participants are exposed to the latest developments in each field. Courses are taught within a five-day period and provide an intensive, rigorous, and in-depth analysis of the topics covered.

CEMFI is an independent non-profit foundation created by the Bank of Spain. CEMFI’s faculty is committed to high-quality research and faculty members publish in the most prestigious international academic journals. Moreover, CEMFI is widely known for its excellence in teaching.

CEMFI occupies a beautiful 19th century building located in a quiet area in the center of Madrid, between the Retiro Park and the Prado Museum.


19 - 23 August 2019 (9:30 am to 1:00 pm)

Jan de Loecker (KU Leuven)

The objective of this course is to introduce the main questions and methodologies in the analysis of firm performance from the point of view of the Industrial Organization literature. Participants will learn how to critically evaluate empirical work in Industrial Organization, and related fields studying firm performance (such as trade, macro, and development economics) and develop tools for research. The empirical work covered will typically have a close tie to a theoretical model. The basic structure of the course will involve presentation and discussion of papers that should be read in advance. Problem sets will be made available to implement the estimation routines and analysis presented during the lectures.

19 - 23 August 2019 (9:30 am to 1:00 pm)

Marco del Negro (Federal Reserve Bank of New York)

The course will offer an overview of modern tools in macroeconometrics, ranging from VARs, state-space models (such as time-varying coefficients models, factor models and models with stochastic volatility), dynamic stochastic general equilibrium (DSGE) and DSGE-VARs models, pools, and model averaging. The course will strive to offer enough theory to understand the tools' theoretical underpinnings, why they work, how and when they should be used, and what their limitations are. At the same time, it will emphasize their practical use in macro applications. The course will take a Bayesian perspective, both because this approach has shown itself to be useful in applied macroeconomics and because of its computational advantages relative to the frequentist approach. Monte Carlo methods, which lie behind the recent surge in popularity of the Bayesian approach, will be reviewed.

19 - 23 August 2019 (3:30 pm to 7:00 pm)

Harrison Hong (Columbia University)

Neoclassical finance theory seeks to explain financial market valuations and fluctuations in terms of investors having rational expectations and being able to trade without costs. These assumptions gave rise to the efficient markets hypothesis (EMH) which has had an enormous influence over the past 50 years. However, there was very little in prevailing EMH models to suggest the instabilities associated with the Financial Crisis of 2008 and, indeed, with earlier crises in financial-market history.
This course seeks to develop a set of tools to build a more robust model of financial markets that can account for a wider range of outcomes. It is based on an ongoing research agenda loosely dubbed “Behavioral Finance”, which seeks to incorporate more realistic assumptions concerning human rationality and market imperfections into finance models. Broadly, we show in this course that deviations from rational forecasting can lead to bubbles and busts such as the Internet Bubble of the mid-1990s and the Housing Bubble of the mid-2000s; that imperfections of markets — such as the difficulty of short-selling assets — can cause financial markets to undergo sudden and unpredictable crashes.
The course proceeds as follows. First, we study mispricings. We then proceed to develop a model which emphasizes market imperfections and risks embedded in arbitrage activity to explain the persistence of mispricing. In the final part, we study speculative bubbles.

19 - 23 August 2019 (3:30 pm to 7:00 pm)

Jose Maria Liberti (Kellogg School of Management, Northwestern University and Kellstadt Graduate School of Business, DePaul University)

The course will provide participants with a toolbox and working knowledge of cross-sectional and panel data empirical methods for use in corporate finance research. This will be accomplished by exposing the participants to a variety of methods commonly employed in empirical research. Each topic will be illustrated with an application drawn in most cases from corporate finance, but also from financial development, financial intermediation, regulation corporate governance, political economy and/or household finance. The course is designed with the intention of understanding the methodology being discussed. Applications will be directly taken from academic papers.

26 - 30 August 2019 (9:30 am to 1:00 pm)

Russell Cooper (European University Institute)

This course focuses on the interaction of monetary and fiscal policy with an emphasis on the economics of a monetary union. The lectures begin with a presentation of the basic analytics of a monetary overlapping generations model. This model is then used to study the gains and losses associated with the formation and operation of a monetary union. This includes an evaluation of the costs associated with the delegation of monetary policy to a central bank and the resulting redesign of fiscal interventions. The course will then turn to the topic of bailout, both through fiscal and monetary measures. For some of this discussion, the need for a bailout will reflect the fragility of debt markets. This consideration will lead to models of the diabolic loop. A final topic will return to the role of monetary policy to offset debt fragility.

26 - 30 August 2019 (3:30 pm to 7:00 pm)

Ansgar Walther (Imperial College London)

This course shows how to apply modern statistical techniques to big financial data. The focus is on how machine learning can guide academic research in Finance, as well as decisions in the financial industry, including asset managers, hedge funds, and consumer finance companies. The course starts with techniques for handling, visualizing, and exploring big financial datasets. We then cover unsupervised and supervised machine learning techniques and their applications in asset pricing and credit scoring. We will also cover reinforcement learning, with applications to portfolio choice. The primary purpose of this course is not only to teach statistical methods, but also to facilitate the financial and economic interpretation of machine learning. Hence, we will pay special attention to the interpretability of machine-learning results, and to the distinction between correlation and causation.

2 - 6 September 2019 (9:30 am to 1:00 pm)

Steve Bond (Oxford University)

The purpose of this course is to provide an up-to-date coverage of the main methods and models used in the econometric analysis of panel data, with particular focus on panels where the cross-sectional dimension is large and the time-series dimension is short. The course will cover applications to production functions, investment models, empirical growth models, and the implementation of panel GMM estimators using Stata (xtabond2).

2 - 6 September 2019 (9:30 am to 13:00 pm)

Stephen Hansen (University of Oxford)

The course will begin with an overview of causal inference with high-dimensional data, and provide a brief review of some of the models (e.g. regularized linear regression, random forests) from supervised learning on which the causal inference literature builds. We will then turn to unsupervised learning, with a focus on extracting measures and information from text and other unstructured discrete data. The emphasis here will be on probabilistic models, and we cover both frequentist and Bayesian inference. We also discuss numerous existing and potential applications in economics and finance. Time permitting, the course will finish with a discussion of reinforcement learning and its use for approximately complex functions in economic models. Software implementations will be discussed for select algorithms, with illustrations in class.


2 - 6 September 2019 (3:30 pm to 7:00 pm)

John Hassler (IIES, Stockholm University) and Per Krusell (IIES, Stockholm University)

The course will cover the basic natural-science elements of climate change – the climate system and the carbon cycle – as well as the literature on damage measurements. The main focus, however, will be on developing integrated assessment models (IAMs) aimed at evaluating national and global policies both regarding the use of fossil fuel and technical change. These models are based on modern macroeconomic tools, i.e., they are dynamic general equilibrium models. They will be studied both in aggregate versions, i.e., modeling the world economy aggregates directly, and in disaggregated versions emphasizing a number of distributional aspects of climate change.

EMAIL: css@cemfi.es