Vadim Marmer

Weak Identification in Fuzzy Regression Discontinuity Designs

In fuzzy regression discontinuity (FRD) designs, the treatment effect is identified through a discontinuity in the conditional probability of treatment assignment. We show that when identification is weak (i.e., when the discontinuity is of a small magnitude), the usual t-test based on the FRD estimator and its standard error suffers from asymptotic size distortions as in a standard instrumental variables setting. This problem can be especially severe in the FRD setting since only observations close to the discontinuity are useful for estimating the treatment effect. To eliminate those size distortions, we propose a modified t-statistic that uses a null-restricted version of the standard error of the FRD estimator. Simple and asymptotically valid confidence sets for the treatment effect can be also constructed using this null-restricted standard error. An extension to testing for constancy of the regression discontinuity effect across covariates is also discussed. Supplementary materials for this article are available online.

Investment and Uncertainty With Time to Build: Evidence from U.S. Copper Mining

The standard real-options model predicts that increased uncertainty discourages investment. When projects are large and take time to build, however, this prediction can be reversed. We investigate the investment/uncertainty relationship empirically using historical data on opening dates of new U.S. copper mines - large, irreversible projects with substantial construction lags. Both the timing of the decision to go forward and the price thresholds that trigger that decision are assessed. We find that, in this market, greater uncertainty encourages investment and lowers the price thresholds for many mines.

Limited participation in international business cycle models: A formal evaluation

In this paper, we argue that limited asset market participation (LAMP) plays an important role in explaining international business cycles. We show that when LAMP is introduced into an otherwise standard model of international business cycles, the performance of the model improves significantly, especially in matching cross-country correlations. To perform formal evaluation of the models we develop a novel statistical procedure that adapts the statistical framework of Vuong (1989) to DSGE models. Using this methodology, we show that the improvements brought out by LAMP are statistically significant, leading a model with LAMP to outperform a representative agent model. Furthermore, when LAMP is introduced, a model with complete markets is found to do as well as a model with no trade in financial assets -- a well-known favorite in the literature. Our results remain robust to the inclusion of investment specific technology shocks.

Instrumental Variables Estimation and Weak-Identification-Robust Inference Based on a Conditional Quantile Restriction

Extending the L1-IV approach proposed by Sakata (1997, 2007), we develop a new method, named the

Identifying Collusion in English Auctions

rho_{tau}

Inference for First-Price Auctions with Guerre, Perrigne, and Vuong's Estimator

-IV estimation, to estimate structural equations based on the conditional quantile restriction imposed on the error terms. We study the asymptotic behavior of the proposed estimator and show how to make statistical inferences on the regression parameters. Given practical importance of weak identification, a highlight of the paper is a proposal of a test robust to the weak identification. The statistics used in our method can be viewed as a natural counterpart of the Anderson and Rubins (1949) statistic in the

Supplement to \Quantile-Based Nonparametric Inference for First-Price Auctions"

rho_{tau}

Natural Gas Networks Performance After Partial Deregulation:Five Quantitative Studies

-IV estimation.

What Model for Entry in First-Price Auctions? A Nonparametric Approach

We develop a fully nonparametric identification framework and a test of collusion in ascending bid auctions. Assuming efficient collusion, we show that the underlying distributions of values can be identified despite collusive behaviour when there is at least one bidder outside the cartel. We propose a nonparametric estimation procedure for the distributions of values and a bootstrap test of the null hypothesis of competitive behaviour against the alternative of collusion. Our framework allows for asymmetric bidders, and the test can be performed on individual bidders. The test is applied to the Guaranteed Investment Certificate auctions conducted by US municipalities over the Internet. Despite the fact that there have been allegations of collusion in this market, our test does not detect deviations from competition. A plausible explanation of this finding is that the Internet auction design involves very limited information disclosure.

Nonlinearity, Nonstationarity, and Spurious Forecasts

In this paper, we focus on inference on the probability density function (PDF) of the valuations in the first-price sealed-bid auction models within the independent private value paradigm in the presence of auction-specific heterogeneity. We show the asymptotic normality of the two-step nonparametric estimator of Guerre et al. (2000, GPV), and propose an easily implementable and consistent estimator of the asymptotic variance of the two-step estimator. In addition, we prove the validity of the percentile bootstrap inference with the GPV estimator.