Econometrics Journal最新文献

We propose a Vuong-type model-selection test for models defined by conditional moment restrictions. The moment restrictions that define the models can be standard equality restrictions that point-identify the model parameters, or moment equality or inequality restrictions that partially identify the model parameters. The test uses a new average generalized empirical likelihood criterion function designed to incorporate full restriction of the conditional model. We also introduce a new adjustment to the test statistic that makes it asymptotically pivotal whether the candidate models are nested or non-nested. The test uses simple standard normal critical values and is shown to be asymptotically similar, to be consistent against all fixed alternatives, and to have non-trivial power against -local alternatives. Monte Carlo simulations demonstrate that the finite sample performance of the test is in accordance with the theoretical prediction.

In this paper, we develop tests for a change in an unconditional small quantile (Value-at-Risk, VaR, in financial time series analysis) based on an estimator motivated by extreme value theory. This so-called Weissman estimator allows tests to be applied for extreme VaR, where extant tests mostly fail. In view of applications, we allow for weakly dependent observations. Our test statistics rely on self-normalization, which obviates the need to estimate the complicated asymptotic variance. Consistency is shown under local alternatives, where multiple breaks can occur. A simulation study shows that in finite samples our tests compare favourably in the tail region with extant tests based on order statistic estimators and also with tail index break tests. Two empirical examples serve to illustrate the practical use of our tests.

In this paper, we investigate the second-order properties of empirical likelihood ratio (ELR) tests of general nonlinear parametric restrictions for over-identified moment restriction models. We derive the stochastic expansion of the ELR statistic for this very large class of testing problems and its formal distributional expansion. We show that we can improve the size properties of the ELR tests via either Bartlett correction or pseudo observation adjustment. Monte Carlo experiments show that tests based on these modified ELR statistics exhibit good finite-sample properties.

We construct tests for the null hypothesis that the conditional average treatment effect is non-negative, conditional on every possible value of a subset of covariates. Testing such a null hypothesis can provide more information than the sign of the average treatment effects parameter. The null hypothesis can be characterized as infinitely many of unconditional moment inequalities. A Kolmogorov–Smirnov test is constructed based on these unconditional moment inequalities, and a simulated critical value is proposed. It is shown that our test can control the size uniformly over a broad set of data-generating processes asymptotically, that it is consistent against fixed alternatives and that it is unbiased against some local alternatives. Several extensions of our test are also considered and we apply our tests to examine the effect of a job-training programme on real earnings.

Motivated by the problem of the detection of a change point in the mean structure of yield curves, we introduce several methods to test the null hypothesis that the mean structure of a time series of curves does not change. The mean structure does not refer merely to the level of the curves, but also to their range and other aspects of their shape, most prominently concavity. The performance of the tests depends on whether possible break points in the error structure, which refers to the random variability in the aspects of the curves listed above, are taken into account or not. If they are not taken into account, then an existing change point in the mean structure may fail to be detected with a large probability. The paper contains a complete asymptotic theory, a simulation study and illustrative data examples, as well as details of the numerical implementation of the testing procedures.

Using unique information on a representative sample of US teenagers, we investigate peer effects in adolescent bedtime decisions. We extend the nonlinear least-squares estimator for spatial autoregressive models to estimate network models with network fixed effects and sampled observations on the dependent variable. We show the extent to which neglecting the sampling issue yields misleading inferential results. When accounting for sampling, we find that, besides the individual, family and peer characteristics, the bedtime decisions of peers help to shape one's own bedtime decision.