Ivana Komunjer

Dynamic Identification of Dynamic Stochastic General Equilibrium Models

This paper studies dynamic identification of parameters of a dynamic stochastic general equilibrium model from the first and second moments of the data. Classical results for dynamic simultaneous equations do not apply because the state space solution of the model does not constitute a standard reduced form. Full rank of the Jacobian matrix of derivatives of the solution parameters with respect to the parameters of interest is necessary but not sufficient for identification. We use restrictions implied by observational equivalence to obtain two sets of rank and order conditions: one for stochastically singular models and another for nonsingular models. Measurement errors, mean, long-run, and a priori restrictions can be accommodated. An example is considered to illustrate the results.

Multivariate Forecast Evaluation and Rationality Testing

In this paper, we propose a new family of multivariate loss functions that can be used to test the rationality of vector forecasts without assuming independence across individual variables. When only one variable is of interest, the loss function reduces to the flexible asymmetric family recently proposed by Elliott, Komunjer, and Timmermann (2005). Following their methodology, we derive a GMM test for multivariate forecast rationality that allows the forecast errors to be dependent, and takes into account forecast estimation uncertainty. We use our test to study the rationality of macroeconomic vector forecasts in the growth rate in nominal output, the CPI inflation rate, and a short-term interest rate.

Testing Models With Multiple Equilibria by Quantile Methods

This paper proposes a method for testing complementarities between explanatory and dependent variables in a large class of economic models. The proposed test is based on the monotone comparative statics (MCS) property of equilibria. Our main result is that MCS produces testable implications on the (small and large) quantiles of the dependent variable, despite the presence of multiple equilibria. The key features of our approach are that (i) we work with a nonparametric structural model of a continuous dependent variable in which the unobservable is allowed to be correlated with the explanatory variable in a reasonably general way; (ii) we do not require the structural function to be known or estimable; (iii) we remain fairly agnostic on how an equilibrium is selected. We illustrate the usefulness of our result for policy evaluation within Berry, Levinsohn, and Pakess (1999) model. Copyright 2009 The Econometric Society.

GLOBAL IDENTIFICATION IN NONLINEAR MODELS WITH MOMENT RESTRICTIONS

This paper derives sufficient conditions for global identification in nonlinear models characterized by a finite number of unconditional moment restrictions. The main contribution of this paper is to provide a set of assumptions that are alternative to those of Gale-Nikaido-Fisher-Rothenberg, and which when satisfied guarantee that the moment conditions globally identify the parameters of interest.

SEMIPARAMETRIC EFFICIENCY BOUND IN TIME-SERIES MODELS FOR CONDITIONAL QUANTILES

We derive the semiparametric efficiency bound in dynamic models of conditional quantiles under a sole strong mixing assumption. We also provide an expression of Stein’s (1956) least favorable parametric submodel. Our approach is as follows: First, we construct a fully parametric submodel of the semiparametric model defined by the conditional quantile restriction that contains the data generating process. We then compare the asymptotic covariance matrix of the MLE obtained in this submodel with those of the M-estimators for the conditional quantile parameter that are consistent and asymptotically normal. Finally, we show that the minimum asymptotic covariance matrix of this class of M-estimators equals the asymptotic covariance matrix of the parametric submodel MLE. Thus, (i) this parametric submodel is a least favorable one, and (ii) the expression of the semiparametric efficiency bound for the conditional quantile parameter follows.

Semi‐Parametric Estimation of Non‐Separable Models: A Minimum Distance from Independence Approach

This paper focuses on nonseparable structural models of the form Y = m(X, U, α0) with U X and in which the structural parameter α0 contains both finite dimensional (θ0) and infinite dimensional (h0) unknown components. Our proposal is to estimate α0 by a minimum distance from independence (MDI) criterion. We show that: (i) our estimator of h0 is consistent and obtain rates of convergence; (ii) the estimator of θ0 is square root n consistent and asymptotically normally distributed.

Correct Specification and Identification of Nonparametric Transformation Models

This paper derives necessary and sufficient conditions for nonparametric transformation models to be (i) correctly specified, and (ii) identified. Our correct specification conditions come in a form of partial differential equations; when satisfied by the true distribution, they ensure that the observables are indeed generated from a nonparametric transformation model. Our nonparametric identification result is global; we derive it under conditions that are substantially weaker than full independence. In particular, we show that a completeness assumption combined with independence with respect to one of the regressors suffices for the model to be identified.

A Test For Monotone Comparative Statics

In this paper we design an econometric test for monotone comparative statics (MCS) often found in models with multiple equilibria. Our test exploits the observable implications of the MCS prediction: that the extreme (high and low) conditional quantiles of the dependent variable increase monotonically with the explanatory variable. The main contribution of the paper is to derive a likelihoodratio test, which to the best of our knowledge, is the first econometric test of MCS proposed in the literature. The test is an asymptotic “chi-bar squared” test for order restrictions on intermediate conditional quantiles. The key features of our approach are: (1) it does not require estimating the underlying nonparametric model relating the dependent and explanatory variables to the latent disturbances; (2) it makes few assumptions on the cardinality, location or probabilities over equilibria. In particular, one can implement our test without assuming an equilibrium selection rule.

Minimum Distance Estimation of Dynamic Models with Errors-In-Variables

Empirical analysis often involves using inexact measures of desired predictors. The bias created by the correlation between the problematic regressors and the error term motivates the need for instrumental variables estimation. This paper considers a class of estimators that can be used when external instruments may not be available or are weak. The idea is to exploit the relation between the parameters of the model and the least squares biases. In cases when this mapping is not analytically tractable, a special algorithm is designed to simulate the latent predictors without completely specifying the processes that induce the biases. The estimators perform well in simulations of the autoregressive distributed lag model and the dynamic panel model. The methodology is used to re-examine the Phillips curve, in which the real activity gap is latent.

Chapter 17 - Quantile Prediction

This chapter is concerned with the problem of quantile prediction (or forecasting). There are numerous applications in economics and finance where quantiles are of interest. We primarily focus on methods that are relevant for dynamic time series data. The chapter is organized around two key questions: first, how to measure and forecast the conditional quantiles of some series of interest given the information currently available and second, how to assess the accuracy of alternative conditional quantile predictors.