Job Market Paper

  • A Sieve-SMM Estimator for Dynamic Models
    Click for Abstract
    Abstract: This paper proposes a Sieve Simulated Method of Moments (Sieve-SMM) estimator for 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 samples moments with simulated moments, is that economic quantities such as welfare and asset-prices can be sensitive to imposing Gaussian shocks. The Sieve-SMM estimator addresses this issue by estimating both the parameters of the model and the distribution of the shocks using a Gaussian and tails mixture sieve. Asymptotic results are derived under low-level conditions using new results that extend the existing sieve literature to more general dynamics. Monte-Carlo simulations illustrate the finite sample properties of the estimator. Two empirical applications highlight the importance of estimating the distribution of the shocks. The first application provides evidence of non-Gaussian shocks in macroeconomic data and highlights their importance on welfare and the risk-free rate. The second highlights the large misspecification bias in stochastic volatility estimates in exchange rate data under fat tails.

Work in Progress

  • Assessing the Sensitivity of Structural VAR models.


  • The ABC of Simulation Estimation with Auxiliary Statistics (pdf)
    (with Serena Ng, 2017) Revision accepted at the Journal of Econometrics, last revision August 2016.
    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 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.


I was a teaching assistant for the following courses at Columbia University:

  • 2017
    • Introduction to Econometrics - Seyhan Erden
  • 2016
    • Advanced Econometrics - Serena Ng
    • Introduction to Econometrics - Christopher Conlon
  • 2015
    • Advanced Econometrics - Serena Ng
    • Introduction to Econometrics II (Ph.D. Level) - Christoph Rothe
  • 2014
    • Introduction to Econometrics - Seyhan Erden & Miikka Rokkanen
    • Introduction to Econometrics - Jushan Bai
  • 2013
    • Introduction to Econometrics - Seyhan Erden