Research methods

This paper proposes a bootstrap-based procedure to build confidence intervals for single components of a partially identified parameter vector, and for smooth functions of such components, in moment (in)equality models.

We provide general compactness results for many commonly used parameter spaces in nonparametric estimation.

Multivalued treatment models have only been studied so far under restrictive assumptions: ordered choice, or more recently unordered monotonicity. We show how marginal treatment effects can be identified in a more general class of models.

Call an economic model incomplete if it does not generate a probabilistic prediction even given knowledge of all parameter values. The authors propose a method of inference about unknown parameters for such models that is robust to heterogeneity and dependence of unknown form

This paper develops characterizations of identified sets of structures and structural features for complete and incomplete models involving continuous and/or discrete variables.

In this paper, the authors consider semiparametric model averaging of the nonlinear dynamic time series system where the number of exogenous regressors is ultra large and the number of autoregressors is moderately large.

This paper uses the frequentist statistical decision theory of Wald (1950) to study design of trials under ambiguity. The authors show that ε-optimal rules exist when trials have large enough sample size.

Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with no large parameters and very many small non-zero parameters. The authors consider here a generalisation of these two basic models, termed here a “sparse + dense” model, in which the signal is given by the sum of a sparse signal and a dense signal.

The authors propose new concepts of statistical depth, multivariate quantiles, vector quantiles and ranks, ranks, and signs, based on canonical transportation maps between a distribution of interest on Rd and a reference distribution on the d-dimensional unit ball.

The authors propose a notion of conditional vector quantile function and a vector quantile regression.

The authors investigate a model in which they connect slowly time varying unconditional long-run volatility with short-run conditional volatility whose representation is given as a semi-strong GARCH(1,1) process with heavy tailed errors.

An incomplete model of English auctions with symmetric independent private values, similar to the one studied in Haile and Tamer (2003), is shown to fall in the class of Generalized Instrumental Variable Models introduced in Chesher and Rosen (2014). A characterization of the sharp identified set for the distribution of valuations is thereby obtained and shown to refine the bounds available until now.

Does economic theory help in forecasting key macroeconomic variables? This article aims to provide some insight into the question by drawing lessons from the literature.

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities.

This paper considers a classical linear simultaneous equations model with random coefficients on the endogenous variables.

This paper introduces econometric methods to perform estimation and inference on the permanent and transitory components of the stochastic discount factor (SDF) in dynamic Markov environments.

The authors study a generalization of the treatment effect model in which an observed discrete classifier indicates in which one of a set of counterfactual processes a decision maker is observed. The other observed outcomes are delivered by the particular counterfactual process in which the decision maker is found.

Random utility models are widely used to study consumer choice. The vast majority of applications make strong assumptions about the marginal utility of income, which restricts income effects, demand curvature and pass-through. The authors show that flexibly modeling income effects can be important, particularly if one is interested in the distributional effects of a policy change, even in a market in which, a priori, the expectation is that income effects will play a limited role. The authors allow for much more flexible forms of income effects than is common and illustrate the implications by simulating the introduction of an excise tax.

The authors derive the expected value and distribution of taxable income conditional on a nonlinear budget set, allowing general heterogeneity and optimization error in taxable income.

The authors propose a method of inference about unknown parameters for incomplete economic models that is robust to heterogeneity and dependence of unknown form.