My research focuses on heterogeneous agents models and frictional markets. I am also interested in efficiency analysis and applied asset pricing. Prior to starting my PhD, I worked at the Economics Department of the OECD. You can find my resume here.
Content: OLS, GLS, IV, qualitative response models, limited dependent variables, Stata
Applied Statistics for Business and Economics (graduate)
Content: introduction to probability theory and regression analysis with a focus on applications in finance and economics
Previous Teaching Positions
2016-2017
Money and Banking (undergraduate)
Lecturer: Johannes Boehm, Assistant Professor Sciences Po
Content: inter-temporal consumption and saving decisions, quantity theory of money, neo-keynesian models, central bank’s inflation bias, commitment vs discretion in monetary policy, bank runs
Introduction to Econometrics and Statistics (graduate)
Lecturer: Matteo Mogliani, Senior Economist at the Banque de France
Content: introduction to probability theory, univariate and multivariate regression models, inference and hypothesis testing
This paper explores some of the potential determinants of efficiency and contestability in the banking systems of major emerging countries, using a sample of 24 countries over the period 2004 -2013. Efficiency is estimated using both stochastic frontier and data envelopment analyses. Market contestability is measured with the Panzar-Rosse H-statistic. Potential drivers of efficiency and market contestability are then discussed.
In this post, my goal is to briefly explain how to create an unregistered Julia package, how to synchronize it with your Github account, and how to start testing your code automatically using TRAVIS CI. I started writing this post as a reminder to myself. I am posting it here in the hope that it may be useful for someone else. More on this topic can be found by reading the official Julia’s manual.
In my previous post, I discussed how the the simulated method of moments could be used to estimate parameters without using the likelihood function. This method is useful because many “real-life” applications result in untractable likelihood functions. In this post, I use the same toy example (estimation of the mean of a mutlivariate normal random variable) and show how to use the parallel computing capabilities of julia and MomentOpt to speed-up the estimation.
As Thomas Sargent said:
“A rational expectations equilibrium model is a likelihood function”
However in many cases, the likelihood function is too complicated to be written down in closed form. To estimate the structural parameters of the model, one can still use Monte-Carlo methods. In this post, I would like to describe the simulated method of moments (SMM), which is a widely used simulation-based estimation technique.
A Simple Setting I want to illustrate the SMM in one of the simplest settings you could think of: the estimation of the mean of a normal density.
A large class of economic models involves solving for functional equations of the form:
A well known example is the stochastic optimal growth model. An agent owns a consumption good $y$ at time $t$, which can be consumed or invested. Next period output depends on how much is invested at time $t$ and on a shock $z$ realized at the end of the current period. One can think of a farmer deciding the quantity of seeds to be planted during the spring, taking into account weather forecast for the growing season.
Dynare is a rich software to solve, estimate and analyse rational expectation models. While it was originally designed to solve and estimate DSGE models, Dynare has also recently been used to solve and simulate heterogeneous agents models (see Winberry and Ragot for two very different approaches). Below is a simple example on how to solve and simulate a simple RBC model using Dynare.
A simple model The economy is composed of a representative agent who maximizes his expected discounted sum of utility by choosing consumption $C_t$ and labor $L_t$ for $t=1,…,\infty$ $$ \sum_{t=1}^{+\infty}\big(\frac{1}{1+\rho}\big)^{t-1} E_t\Big[log(C_t)-\frac{L_t^{1+\gamma}}{1+\gamma}\Big] $$