Vadim Marmer

Associate Professor

I am an Associate Professor in the Vancouver School of Economics at UBC. I came to UBC in 2005 after completing my Ph.D. at Yale University. My main area of research is Econometrics, where I have been working on topics of misspecification, weak identification, estimation and inference in auctions, and nonlinear and non-stationary time series. I teach graduate and undergraduate courses in Econometrics.

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PUBLISHED PAPERS

We develop a nonparametric approach that allows for discrimination among alternative models of entry in first-price auctions. Three models of entry are considered: those of Levin and Smith (1994), Samuelson (1985), and a new model in which the information received at the entry stage is imperfectly correlated with bidder valuations. We derive testable restrictions of these models based on how the pro-competitive selection effect shifts bidder valuation quantiles in response to an increase in the number of potential bidders.

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Suppose that the econometrician is interested in comparing two misspecified moment restriction models, where the comparison is performed in terms of some chosen measure of fit. This paper is concerned with describing an optimal test of the Vuong (1989) and Rivers and Vuong (2002) type null hypothesis that the two models are equivalent under the given measure of fit (the ranking may vary for different measures). We adopt the generalized Neyman–Pearson optimality criterion, which focuses on the decay rates of the type I and II error probabilities under fixed non-local alternatives, and derive an optimal but practically infeasible test. Then, as an illustration, by considering the model comparison hypothesis defined by the weighted Euclidean norm of moment restrictions, we propose a feasible approximate test statistic to the optimal one and study its asymptotic properties. Local power properties, one-sided test, and comparison under the generalized empirical likelihood-based measure of fit are also investigated. A simulation study illustrates that our approximate test is more powerful than the Rivers–Vuong test.

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This paper proposes several testing procedures for comparison of misspecified calibrated models. The proposed tests are of the Vuong-type ( and ). In our framework, the econometrician selects values for model’s parameters in order to match some characteristics of data with those implied by the theoretical model. We assume that all competing models are misspecified, and suggest a test for the null hypothesis that they provide equivalent fit to data characteristics, against the alternative that one of the models is a better approximation. We consider both nested and non-nested cases. We also relax the dependence of models’ ranking on the choice of a weight matrix by suggesting averaged and sup-norm procedures. The methods are illustrated by comparing the cash-in-advance and portfolio adjustment cost models in their ability to match the impulse responses of output and inflation to money growth shocks.

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We propose a quantile-based nonparametric approach to inference on the probability density function (PDF) of the private values in first-price sealed-bid auctions with independent private values. Our method of inference is based on a fully nonparametric kernel-based estimator of the quantiles and PDF of observable bids. Our estimator attains the optimal rate of Guerre et al. (2000), and is also asymptotically normal with an appropriate choice of the bandwidth.

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This paper introduces a rank-based test for the instrumental variables regression model that dominates the Anderson–Rubin test in terms of finite sample size and asymptotic power in certain circumstances. The test has correct size for any distribution of the errors with weak or strong instruments. The test has noticeably higher power than the Anderson–Rubin test when the error distribution has thick tails and comparable power otherwise. Like the Anderson–Rubin test, the rank tests considered here perform best, relative to other available tests, in exactly identified models.

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Implications of nonlinearity, nonstationarity, and misspecification are considered from a forecasting perspective. Our model allows for small departures from the martingale difference sequence hypothesis by including a nonlinear component, formulated as a general, integrable transformation of the I(1) predictor. We assume that the true generating mechanism is unknown to the econometrician and he is therefore forced to use some approximating functions. It is shown that in this framework the linear regression techniques lead to spurious forecasts. Improvements of the forecast accuracy are possible with properly chosen nonlinear transformations of the predictor. The paper derives the limiting distribution of the forecasts’ mean squared error (MSE). In the case of square integrable approximants, it depends on the L2-distance between the nonlinear component and approximating function. Optimal forecasts are available for a given class of approximants.

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This paper presents tests for the null hypothesis of no regime switching in Hamilton’s (Econometrica 57:357–384, 1989) regime switching model. The test procedures exploit similarities between regime switching models, autoregressions with measurement errors, and finite mixture models. The proposed tests are computationally simple and, contrary to likelihood based tests, have a standard distribution under the null. When the methodology is applied to US GDP growth rates, no strong evidence of regime switching is found.

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In this paper, we suggest a simple econometric procedure for identification of bottlenecks in the US natural gas pipelines network. We claim that there is a bottleneck between two nodes of the network if their spot gas prices are not co-integrated. Existence of bottlenecks is attributed to insufficient pipeline capacity between two such nodes. We find that the network is separated into three local markets: Northeast, Midwest and California.

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This paper determines coverage probability errors of both delta method and parametric bootstrap confidence intervals (CIs) for the covariance parameters of stationary long-memory Gaussian time series. CIs for the long-memory parameter d0 are included. The results establish that the bootstrap provides higher-order improvements over the delta method. Analogous results are given for tests. The CIs and tests are based on one or other of two approximate maximum likelihood estimators. The first estimator solves the first-order conditions with respect to the covariance parameters of a “plug-in” log-likelihood function that has the unknown mean replaced by the sample mean. The second estimator does likewise for a plug-in Whittle log-likelihood.

The magnitudes of the coverage probability errors for one-sided bootstrap CIs for covariance parameters for long-memory time series are shown to be essentially the same as they are with iid data. This occurs even though the mean of the time series cannot be estimated at the usual n1/2 rate.

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WORKING PAPERS

In this paper we study the role of limited asset market participation (LAMP) for international business cycles. We show that when limited participation is introduced into an otherwise standard model of international business cycles, the performance of the model improves signiÖcantly, especially in matching cross-country correlations. To perform formal evaluation of the models we develop a novel statistical procedure that adapts the test of Vuong (1989) to DSGE models and accounts for the possibility that models are misspeciÖed. Based on this test we show that the improvements brought out by LAMP are statistically signiÖcant, 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 better than a model with no trade in Önancial assets ña well-known favorite in the literature. Our results remain robust to the inclusion of investment speciÖc technical change.

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In fuzzy regression discontinuity (FRD) designs, the treatment effect is identified through a discontinuity in the conditional probability of treatment assignment. As in a standard instrumental variables setting, 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. This finite-sample 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 the FRD estimator and its null-restricted standard error. An extension to testing for constancy of the regression discontinuity effect across covariates is also discussed.

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Winter 2017

ECON527 Econometric Methods of Economic Research Sections

Winter 2017

ECON627 Econometric Theory II Sections