Econometrics Journal最新文献

In this paper, we develop a general method of testing for independence when unobservable generalized errors are involved. Our method can be applied to testing for serial independence of generalized errors, and testing for independence between the generalized errors and observable covariates. The former can serve as a unified approach to testing the adequacy of time series models, as model adequacy often implies that the generalized errors obtained after a suitable transformation are independent and identically distributed. The latter is a key identification assumption in many nonlinear economic models. Our tests are based on a classical sample dependence measure, the Hoeffding–Blum–Kiefer–Rosenblatt-type empirical process applied to generalized residuals. We establish a uniform expansion of the process, thereby deriving an explicit expression for the parameter estimation effect, which causes our tests not to be nuisance-parameter-free. To circumvent this problem, we propose a multiplier-type bootstrap to approximate the limit distribution. Our bootstrap procedure is computationally very simple as it does not require a re-estimation of the parameters in each bootstrap replication. Simulations and empirical applications to daily exchange rate data highlight the merits of our approach.

The main contribution of this paper is to establish the formal validity of Edgeworth expansions for realized volatility estimators. First, in the context of no microstructure effects, our results rigorously justify the Edgeworth expansions for realized volatility derived in Gonçalves and Meddahi (2009, Econometrica 77, 283–306). Second, we show that the validity of the Edgeworth expansions for realized volatility might not cover the optimal two-point distribution wild bootstrap proposed by Gonçalves and Meddahi. Then, we propose a new optimal nonlattice distribution, which ensures the second-order correctness of the bootstrap. Third, in the presence of microstructure noise, based on our Edgeworth expansions, we show that the new optimal choice proposed in the absence of noise is still valid in noisy data for the pre-averaged realized volatility estimator proposed by Podolskij and Vetter (2009, Bernoulli 15, 634–658). Finally, we show how confidence intervals for integrated volatility can be constructed using these Edgeworth expansions for noisy data. Our Monte Carlo simulations show that the intervals based on the Edgeworth corrections have improved the finite sample properties relatively to the conventional intervals based on the normal approximation.

We provide generalized least-squares (GLS) detrended versions of single-equation static regression or residuals-based tests for testing whether or not non-stationary time series are cointegrated. Our approach is to consider nearly optimal tests for unit roots and to apply them in the cointegration context. We derive the local asymptotic power functions of all tests considered for a triangular data-generating process, imposing a directional restriction such that the regressors are pure integrated processes. Our GLS versions of the tests do indeed provide substantial power improvements over their ordinary least-squares counterparts. Simulations show that the gains in power are important and stable across various configurations.

In this paper, we propose constructing a confidence set for the date of a one-time structural change using a point optimal test. Following Elliott and Müller (2007, Journal of Econometrics 141, 1196–1218), we first construct a test for the break date that maximizes the weighted average of the power function. The confidence set is then obtained by inverting the test statistic. We carefully choose the weights and show by Monte Carlo simulations that the confidence set based on our method has a relatively accurate coverage rate, while the length of our confidence set is significantly shorter than the lengths proposed in the literature.

In this paper, we propose new cointegration tests for single equations and panels. In both cases, the asymptotic distributions of the tests, which are derived with N fixed and , are shown to be standard normals. The effects of serial correlation and cross-sectional dependence are mopped out via long-run variances. An effective bias correction is derived, which is shown to work well in finite samples, particularly when N is smaller than T. Our panel tests are robust to possible cointegration across units.

Economic variables are often measured with an error and may be endogenous. In this paper, we give new identification results for the ratio of partial effects in linear index models with measurement error and endogeneity. The identification restrictions include independence of covariates and error terms, and the derivative of some conditional mean functions being nonzero. We propose a local polynomial regression estimator to estimate the single-index parameters. We apply these tools to estimate the labour-supply elasticity and find that the labour-supply elasticity for married men is positive, while the coefficients for married women are negative for the full sample and positive for the working sample.