This paper develops bootstrap methods for testing whether, in a finite sample, competing out-of-sample forecasts from nested models are equally accurate. Most prior work on forecast tests for nested models has focused on a null hypothesis of equal accuracy in population — basically, whether coefficients on the extra variables in the larger, nesting model are zero. We instead use an asymptotic a...

Our results complement the recent ̄ndings of real exchange rates as stationary processes. The standard procedure of applying a battery of unit root tests can be problematic since the tests are sensitive to the speci ̄cs of the time series process. The novelty of the approach we apply is in emphasizing the information content of the data in distinguishing between the competing processes. Stationa...

We explore the finite sample properties of several semiparametric estimators of average treatment effects, including propensity score reweighting, matching, double robust, and control function estimators. When there is good overlap in the distribution of propensity scores for treatment and control units, reweighting estimators are preferred on bias grounds and attain the semiparametric efficien...

In this paper, we propose a Vuong (1989)-type model selection test for conditional moment inequality models. The test uses a new average generalized empirical likelihood (AGEL) criterion function designed to incorporate full restriction of the conditional model. We also introduce a new adjustment to the test statistic making it asymptotically pivotal whether the candidate models are nested or n...

We find that covariance matrix forecasts for an international interest rate portfolio generated by a model that incorporates interest-rate level volatility effects perform best with respect to statistical loss functions. However, within a value-at-risk (VaR) framework, the relative performance of the covariance matrix forecasts depends greatly on the VaR distributional assumption. Simple foreca...

This paper analyzes whether standard covariance matrix tests work when dimensionality is large, and in particular larger than sample size. In the latter case, the singularity of the sample covariance matrix makes likelihood ratio tests degenerate, but other tests based on quadratic forms of sample covariance matrix eigenvalues remain well-defined. We study the consistency property and limiting ...

This paper employs non-parametric specification tests developed in Hong and Li (2005) to evaluate several one-factor reduced-form credit risk models for actual default intensities. Using estimates for actual default probabilities provided by Moody’s KMV from 1994 to 2005 for 106 U.S. firms in seven industry groups, we strongly reject popular univariate affine model specifications. As a good com...

This paper studies the asymptotic relationship between Bayesian model averaging and postselection frequentist predictors in both nested and nonnested models. We derive conditions under which their difference is of a smaller order of magnitude than the inverse of the square root of the sample size in large samples. This result depends crucially on the relation between posterior odds and frequent...

We propose a new three-step model-selection framework for size distributions in empirical data. It generalizes a recent frequentist plausibility-of-fit analysis (Step 1) and combines it with a relative ranking based on the Bayesian Akaike Information Criterion (Step 2). We enhance these statistical criteria with the additional criterion of microfoundation (Step 3) which is to select the size di...

This paper applies the Model Confidence Set (MCS) procedure of Hansen, Lunde, and Nason (2003) to a set of volatility models. A MCS is analogous to confidence interval of a parameter in the sense that the former contains the best forecasting model with a certain probability. The key to the MCS is that it acknowledges the limitations of the information in the data. The empirical exercise is base...