Demian

Published and Accepted Papers (for links see my CV { pdf }):

"On nonlinear ill-posed inverse problems with applications to pricing of defaultable bonds and option pricing," with X. Chen. Science in China Series A: Mathematics . Volume 52, No. 6, June 2009, pp 1157-1168.

"Efficient Estimation of Semiparametric Conditional Moment Models with Possibly Nonsmooth Residuals," with X. Chen. Journal of Econometrics . Volume 152, Issue 1, September 2009, Pages 46-60.

"Estimation and model selection of semiparametric copula-based multivariate survival functions under general censorship," with X. Chen, Y. Fan and Z. Ying. Journal of Econometrics . Volume 157, Issue 1, July 2010, Pages 129-142.

"Estimation of Nonparametric Conditional Moment Models with Possibly Nonsmooth Moments," with X. Chen. Econometrica . Volume 80, No. 1, January 2012, Pages 277-322.

"Sieve Quasi Likelihood Ratio Inference on Semi/nonparametric Conditional Moment Models," with X. Chen. Econometrica . Volume 83, No. 3, May 2015, Pages 1013-1079.

"Bootstrap Consistency for Quadratic Forms of Sample Averages with Increasing Dimension." Electronic Journal of Statistics . Volume 9, No. 2, 2015, Pages 3046-3097.

"Berk-Nash Equilibrium: A Framework for Modeling Agents with Misspecified Models,'' with I. Esponda. Econometrica . Volume 84, No. 3, May 2016, Pages 1093-1130.

"Sovereign Default Risk and Uncertainty Premia,'' with I. Presno. American Economic Journal: Macroeconomics . Volume 8, No. 3, July 2016, pages 230-66.

"Conditional Retrospective Voting in Large Elections,'' with I. Esponda. American Economic Journal: Microeconomics. . Volume 9, No 2, May 2017, pp 54-75.

"Retrospective Voting and Party Polarization,'' with I. Esponda. Conditionally Accepted at International Economic Review.

Working Papers:

“Some Large Sample Results for the Method of Regularized Estimators”,
with M. Jansson. Updated: December 21st, 2017. { pdf }

Abstract: We present a general framework for studying regularized estimators; i.e., estimation problems wherein "plug-in" type estimators are either ill-defined or ill-behaved. We derive primitive conditions that imply consistency and asymptotic linear representation for regularized estimators, allowing for slower than square-root n estimators as well as infinite dimensional parameters. We also provide data-driven methods for choosing tuning parameters that, under some conditions, achieve the aforementioned results. We illustrate the scope of our approach by studying a wide range of applications, revisiting known results and deriving new ones.

“The Industry Supply Function and the Long-Run Competitive Equilibrium with Heterogeneous Firms”,
with I. Esponda. Updated: January 2nd, 2017 (submitted). { pdf }

Abstract: In developing the theory of long-run competitive equilibrium (LRCE), Marshall (1890) used the notion of a representative firm. The identity of this firm, however, remained unclear, and subsequent theory focused on the case where all firms are identical. Using Hopenhayn's (1992) model of competitive industry dynamics, we extend the theory of LRCE to account for heterogeneous firms and show that the long-run supply function can indeed be characterized as the solution to the minimization of a representative average cost function. We also highlight that famous principles of competitive markets, such as efficiency of the LRCE allocation, are not robust to heterogeneity.

“Maximum Likelihood Estimation in Possibly Misspecified Dynamic Models with Time-Inhomogeneous Markov Regimes”,
with Z. Psaradakis and M. Sola. Updated: December 15th, 2016 (submitted). { pdf }

Abstract: This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency and local asymptotic normality of the ML estimator under general conditions which allow for autoregressive dynamics in the observable process, time-inhomogeneous Markov regime sequences, and possible model misspecification. A Monte Carlo study examines the finite-sample properties of the ML estimator. An empirical application is also discussed.

“A Theory of Experience Effects”,
with V. Vanasco and U. Malmendier. Updated: January 2nd, 2017 (submitted). { pdf }

Abstract: How do financial crises and stock-market fluctuations affect investor behavior and the dynamics of financial markets in the long run? Recent evidence suggests that individuals overweight personal experiences of macroeconomic shocks when forming beliefs and making investment decisions. We propose a theoretical foundation for such experience-based learning and derive its dynamic implications in a simple OLG model. Risk averse agents invest in a risky and a risk-free asset. They form beliefs about the payoff of the risky asset based on the two key components of experience effects: (1) they overweight data observed during their lifetimes so far, and (2) they exhibit recency bias. In equilibrium, prices depend on past dividends, but only on those observed by the generations that are alive, and they are more sensitive to more recent dividends. Younger generations react more strongly to recent experiences than older generations, and hence have higher demand for the risky asset in good times, but lower demand in bad times. As a result, a crisis increases the average age of stock market participants, while booms have the opposite effect. The stronger the disagreement across generations (e.g., after a recent shock), the higher is the trade volume. We also show that, vice versa, the demographic composition of markets significantly influences the response to aggregate shocks. We generate empirical results on stock-market participation, stock-market investment, and trade volume from the Survey of Consumer Finances, merged with CRSP and historical data on stock-market performance, that are consistent with the model predictions.

“On the Non-Asymptotic Properties of Regularized M-estimators”,
Updated: May 3rd, 2016 (submitted). { pdf }

Abstract: We propose a general framework for regularization in M-estimation problems under time dependent (absolutely regular-mixing) data which encompasses many of the existing estimators. We derive non-asymptotic concentration bounds for the regularized M-estimator. Our results exhibit a "variance-bias" trade-off, with the "variance" term being governed by a novel measure of the "size" of the parameter set. We also show that the mixing structure affect the variance term by scaling the number of observations; depending on the decay rate of the mixing coefficients, this scaling can even affect the asymptotic behavior. Finally, we propose a data-driven method for choosing the tuning parameters of the regularized estimator which yield the same (up to constants) concentration bound as one that optimally balances the "(squared) bias" and "variance" terms. We illustrate the results with several canonical examples of, both, non-parametric and high-dimensional models.

“Equilibrium in Misspecified Markov Decision Process”,
with Ignacio Esponda (Revision requested at Econometrica). Updated: May 14th, 2016. { pdf }

Abstract: We study Markov decision problems where the agent does not know the transition probability function mapping current states and actions to future states. The agent has a prior belief over a set of possible transition functions and updates beliefs using Bayes' rule. We allow her to be misspecified in the sense that the true transition probability function is not in the support of her prior. This problem is relevant in many economic settings but is usually not amenable to analysis by the researcher. We make the problem tractable by studying asymptotic behavior. We propose an equilibrium notion and provide conditions under which it characterizes steady state behavior. In the special case where the problem is static, equilibrium coincides with the single-agent version of Berk-Nash equilibrium (Esponda and Pouzo (2016)). We also discuss subtle issues that arise exclusively in dynamic settings due to the possibility of a negative value of experimentation.

“Optimal Taxation with Endogenous Default under Incomplete Markets”,
with Ignacio Presno (Revision requested at AEJ: Macro). Updated: May 7th, 2016. { pdf }

Abstract: In a dynamic economy, we characterize the fiscal policy of the government when it levies distortionary taxes and issues defaultable bonds to finance its stochastic expenditure. Default may occur in equilibrium as it prevents the government from incurring in future tax distortions that would come along with the service of the debt. Households anticipate the possibility of default generating endogenous credit limits. These limits hinder the government's ability to smooth taxes using debt, implying more volatile and less serially correlated fiscal policies, higher borrowing costs and lower levels of indebtedness. In order to exit temporary financial autarky following a default event, the government has to repay a random fraction of the defaulted debt. We show that the optimal fiscal and renegotiation policies have implications aligned with the data.



Non-Active Working Papers:

“Learning foundation and equilibrium selection in voting environments with private information”,
with Ignacio Esponda. Updated: January 25, 2012. { pdf } This paper is obsolete, mostly incorporated in the papers "Conditional Retrospective Voting in Large Elections" and "Retrospective Voting and Party Polarization"

Abstract: We use a dynamic learning model to investigate different behavioral assumptions in voting environments with private information. We show that a simple rule, where players learn based on the outcomes of past elections in which they were pivotal but requires no prior knowledge of the payoff structure or of the rules followed by other players, provides a foundation for Nash equilibrium. In contrast, a rule where voters learn from all past elections provides a foundation for a new notion of naive voting where players vote sincerely but have endogenously-determined beliefs. Finally, we use the model to select among multiple equilibria in the jury model. We find that the well-known result that elections aggregate information under Nash equilibrium relies on the selection of symmetric equilibria which are unstable. Nevertheless, we show that there exist (possibly asymmetric) Nash equilibria that are asymptotically stable and aggregate information.