This note derives primitive conditions for global identification in nonlinear simultaneous equations systems. Identification is semiparametric in the sense that the latent structural disturbance is only known to satisfy a number of orthogonality restricitions with respect to observed instruments. Our contribution to the literature on identification in a semiparametric context is twofold. First, we derive a set of unconditional moment restrictions on the observables that are the starting point for identification in nonlinear structural systems. Second, we provide primitive conditions under which a parameter value that solves those restrictions is unique.

This paper establishes the identifiability of the parameters of the Box-Cox model under restrictions that do not require the disturbance in the model to be independent of the explanatory variables. The proposed restrictions are semiparametric in nature: they restrict the support of the conditional distribution of the disturbance but do not require the latter to be known.

We study the properties of a quasi-maximum likelihood (QML) for the parameters of a "weak" GARCH process obtained by contemporaneous aggregation of two independent "strong" GARCH processes. The inconsistency of the Gaussian quasi-likelihood estimator (QMLE) has already been reported by Nijman & Sentana (1996) but has not yet been solved. In this paper we identify the causes of inconsistency of QMLE in the "weak" GARCH case and compare the performance of QMLE when the innovations are assumed to have Gaussian, Laplace (double exponential) or alpha-stable distribution.

This paper derives primitive conditions for global identification in nonlinear simultaneous equations systems. Identification is semiparametric in the sense tht it is based on a set of unconditional moment restrictions. Our contribution to the literature is twofold. First, we derive a set of unconditional moment restrictions on the observables that are the starting point for identification in nonlinear structural systems even in the presence of multiple equilibria. Second, we provide primitive conditions under which a parameter value that solves those restrictions is unique. We apply our results a nonlinear IV model with multiple equilibria and give sufficient conditions for identifiability of its paramters.

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 multivaariate forecast rationality that allows the forecast errors to be dependent, and takes into account forecast estimation ucertainty. 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.

In this paper we consider the problem of efficient estimation in conditional quantile models with time series data. Our first result is to derive the semiparametric efficiency bound in time series models of conditional quantiles; this is a nontrivial extension of a large body of work on efficient estimation, which has traditionally focused on models with independent and identically distributed data. In particular, we generalize the bound derived by New and Powell (1990) to the case where the data is weakly dependent and heterogeneous. We then proceed by constructing an M-estimator which achieves the semiparametric efficiency bound. Our efficient M-estimator is obtained by minimizing an objective function which depends on a nonparametric estimator of the conditional distribution of the variable of interest rather than its density.

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.

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.

Though one of the pillars of the theory of international trade, the extreme predictions of the Ricardian model have made it unsuitable for empirical purposes. A seminal contribution of Eaton and Kortum (2002) is to demonstrate that random productivity shocks are sufficient to make the Ricardian model empirically relevant. While successful at explaining trade volumes, their model remains silent with regards to one important questions: What goods to countries trade? Our main contribution is to generalize their approach and provide and empirically meaningful answer to this question.

This paper proposes a method for comparing and combining conditional quantile forecasts based on the principle of 'encompassing'. Our test for conditional quantile forecast encompassing (CQFE) is a test of superior predictive ability, constructed as a Wald-type test on the coefficients of an optimal combination of alternative forecasts. The CQFE test is a 'model free' test that can be used to compare any given number of alternative forecasts, and is relatively easy to implement by GMM techniques appropriately modified to accommodate non-differentiable criterion functions. Further, our theoretical framework provides a basis for combining quantile forecasts, when neither forecast has superior predictive ability. A central feature of our method is the focus on conditional, rather than unconditional expected loss in the formulation of the encompassing test, which links our approach to Christoffersen's (1998) 'conditional coverage' test for evaluation of quantile forecasts. An empirical application to the problem of Value at Risk evaluation illustrates the usefulness of the proposed techniques.