Jean-Jacques Forneron

Assistant Professor of Economics

Boston University

I am an Assistant Professor in Economics at Boston University. My research focuses on Econometrics. Curriculum Vitae


  • Econometrics
  • Macroeconometrics
  • Industrial Organization


  • PhD in Economics, 2018

    Columbia University, USA

  • MA in Economics and Statistics, 2012

    ENSAE Paris, France

  • MSc in Management, 2012

    HEC Paris, France


Working Papers

  • A Scrambled Method of Moments (pdf)
    Click for Abstract
    Abstract: Quasi-Monte Carlo (qMC) methods are a powerful alternative to classical Monte-Carlo (MC) integration. Under certain conditions, they can approximate the desired integral at a faster rate than the usual Central Limit Theorem, resulting in more accurate estimates. This paper explores these methods in a simulation-based estimation setting with an emphasis on the scramble of Owen (1995). For cross-sections and short-panels, the resulting Scrambled Method of Moments simply replaces the random number generator with the scramble (available in most softwares) to reduce simulation noise. Scrambled Indirect Inference estimation is also considered. For time series, qMC may not apply directly because of a curse of dimensionality on the time dimension. A simple algorithm and a class of moments which circumvent this issue are described. Asymptotic results are given for each algorithm. Monte-Carlo examples illustrate these results in finite samples, including an income process with “lots of heterogeneity.”
  • Detecting Identification Failure in Moment Condition Models (pdf)
    Click for Abstract
    Abstract: This paper develops an approach to detect identification failures in a large class of moment condition models. This is achieved by introducing a quasi-Jacobian matrix which is asymptotically singular under higher-order local identification as well as weak/set identification; in these settings, standard asymptotics are not valid. Under (semi)-strong identification, where standard asymptotics are valid, this matrix is asymptotically equivalent to the usual Jacobian matrix. After re-scaling, it is thus asymptotically non-singular. Together, these results imply that the eigenvalues of the quasi-Jacobian can detect potential local and global identification failures. Furthermore, the quasi-Jacobian is informative about the span of the identification failure. This information permits two-step identification robust subvector inference without any a priori knowledge of the underlying identification structure. Monte-Carlo simulations and empirical applications illustrate the results.
  • A Sieve-SMM Estimator for Dynamic Models (pdf |supplement)
    Click for Abstract
    Abstract: This paper proposes a Sieve Simulated Method of Moments (Sieve-SMM) estimator for the parameters and the distribution of the shocks in nonlinear dynamic models where the likelihood and the moments are not tractable. An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required but economic quantities that depend on this distribution, such as welfare and asset-prices, can be sensitive to misspecification. The Sieve-SMM estimator addresses this issue by flexibly approximating the distribution of the shocks with a Gaussian and tails mixture sieve. The asymptotic framework provides consistency, rate of convergence and asymptotic normality results, extending sieve theory to more general dynamics with latent variables. Monte-Carlo simulations illustrate the finite sample properties of the estimator. Two empirical applications highlight the importance of the distribution of the shocks. The first provides evidence of non-Gaussian shocks in macroeconomic data and their implications on welfare and the risk-free rate. The second finds that Gaussian estimates of stochastic volatility are significantly biased in exchange rate data because of fat tails.


  • The ABC of Simulation Estimation with Auxiliary Statistics (pdf)
    (with Serena Ng, 2018) in the Journal of Econometrics, vol. 205, no 1, p. 112-139.
    Click for Abstract
    The frequentist method of simulated minimum distance (SMD) is widely used in economics to estimate complex models with an intractable likelihood. In other disciplines, a Bayesian approach known as Approximate Bayesian Computation (ABC) is far more popular. This paper connects these two seemingly related approaches to likelihood-free estimation by means of a Reverse Sampler that uses both optimization and importance weighting to target the posterior distribution. Its hybrid features enable us to analyze an ABC estimate from the perspective of SMD. We show that an ideal ABC estimate can be obtained as a weighted average of a sequence of SMD modes, each being the minimizer of the deviations between the data and the model. This contrasts with the SMD, which is the mode of the average deviations. Using stochastic expansions, we provide a general characterization of frequentist estimators and those based on Bayesian computations including Laplace-type estimators. Their differences are illustrated using analytical examples and a simulation study of the dynamic panel model.
  • A Likelihood-Free Reverse Sampler of the Posterior Distribution (pdf)
    (with Serena Ng, 2016) in Advances in Econometrics Vol 36, p.389-415.
    Click for Abstract
    This paper considers properties of an optimization-based sampler for targeting the posterior distribution when the likelihood is intractable. It uses auxiliary statistics to summarize information in the data and does not directly evaluate the likelihood associated with the specified parametric model. Our reverse sampler approximates the desired posterior distribution by first solving a sequence of simulated minimum distance problems. The solutions are then reweighted by an importance ratio that depends on the prior and the volume of the Jacobian matrix. By a change of variable argument, the output consists of draws from the desired posterior distribution. Optimization always results in acceptable draws. Hence, when the minimum distance problem is not too difficult to solve, combining importance sampling with optimization can be much faster than the method of Approximate Bayesian Computation that by-passes optimization.



  • EC508: Econometrics (MA), spring 2019-2020
  • EC711: Advanced Topics in Econometrics (PhD, co-instructor), fall 2018, spring 2020

Office Hours

  • Location: Office 415D, 270 Bay State Road
  • Day/Time:


  • jjmf@bu.edu
  • 270 Bay State Road, Boston, MA, 02215, United States