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94
Validity of Subsampling and “Plugin Asymptotic” Inference for Parameters Defined by Moment Inequalities
, 2007
"... This paper considers inference for parameters defined by moment inequalities and equalities. The parameters need not be identified. For a specified class of test statistics, this paper establishes the uniform asymptotic validity of subsampling, m out of n bootstrap, and “plugin asymptotic" tes ..."
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Cited by 35 (3 self)
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This paper considers inference for parameters defined by moment inequalities and equalities. The parameters need not be identified. For a specified class of test statistics, this paper establishes the uniform asymptotic validity of subsampling, m out of n bootstrap, and “plugin asymptotic" tests and confidence intervals for such parameters. Establishing uniform asymptotic validity is crucial in moment inequality problems because the test statistics of interest have discontinuities in their pointwise asymptotic distributions. The size results are quite general because they hold without specifying the particular form of the moment conditions–only 2+δ moments finite are required. The results allow for i.i.d. and dependent observations and for preliminary consistent estimation of identified parameters.
Performance Evaluation for Zero NetInvestment Strategies
, 2010
"... This paper introduces new nonparametric statistical methods to evaluate zerocost investment strategies. We focus on directional trading strategies, riskadjusted returns, and the investor’s decisions under uncertainty as the core of our analysis. By relying on classification tools with a long tradi ..."
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Cited by 29 (19 self)
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This paper introduces new nonparametric statistical methods to evaluate zerocost investment strategies. We focus on directional trading strategies, riskadjusted returns, and the investor’s decisions under uncertainty as the core of our analysis. By relying on classification tools with a long tradition in the sciences and biostatistics, we can provide a tighter connection between modelbased risk characteristics and the noarbitrage conditions for market efficiency. Moreover, we extend the methods to multicategorical settings, such as when the investor can sometimes take a neutral position. A variety of inferential procedures are provided, many of which are illustrated with applications to excess equity returns and to currency carry trades.
Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes
, 2005
"... We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for outofsample n ..."
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Cited by 26 (12 self)
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We introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for outofsample nonlinear Granger causality, and in the other we outline a test for selecting amongst multiple alternative forecasting models, all of which are possibly misspecified. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (1999); within the context of encompassing and predictive accuracy tests. In the empirical illustration, it is found that unemployment has nonlinear marginal predictive content for inflation.
A test for comparing multiple misspecified conditional distributions, manuscript
, 2003
"... This paper introduces a test for the comparison of multiple misspecified conditional interval models, for the case of dependent observations+ Model accuracy is measured using a distributional analog of mean square error, in which the approximation error associated with a given model, say, model i, ..."
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Cited by 24 (11 self)
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This paper introduces a test for the comparison of multiple misspecified conditional interval models, for the case of dependent observations+ Model accuracy is measured using a distributional analog of mean square error, in which the approximation error associated with a given model, say, model i, for a given interval, is measured by the expected squared difference between the conditional confidence interval under model i and the “true ” one+ When comparing more than two models, a “benchmark ” model is specified, and the test is constructed along the lines of the “reality check ” of White ~2000, Econometrica 68, 1097–1126!+ Valid asymptotic critical values are obtained via a version of the block bootstrap that properly captures the effect of parameter estimation error+ The results of a small Monte Carlo experiment indicate that the test does not have unreasonable finite sample properties, given small samples of 60 and 120 observations, although the results do suggest that larger samples should likely be used in empirical applications of the test+ 1.
Empirical exchange rate models and currency risk: some evidence from density forecasts
 Journal of International Money and Finance
, 2005
"... A large literature in exchange rate economics has investigated the forecasting performance of empirical exchange rate models using conventional point forecast accuracy criteria. However, in the context of managing exchange rate risk, interest centers on more than just point forecasts. This paper pro ..."
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Cited by 23 (2 self)
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A large literature in exchange rate economics has investigated the forecasting performance of empirical exchange rate models using conventional point forecast accuracy criteria. However, in the context of managing exchange rate risk, interest centers on more than just point forecasts. This paper provides a formal evaluation of recent exchange rate models based on the term structure of forward exchange rates, which previous research has shown to be satisfactory in point forecasting, in terms of density forecasting performance. The economic value of the exchange rate density forecasts is investigated in the context of an application to a simple risk management exercise. JEL classification: F31; F37.
Predictive Density Accuracy Tests
, 2004
"... This paper outlines a testing procedure for assessing the relative outofsample predictive accuracy of multiple conditional distribution models, and surveys existing related methods in the area of predictive density evaluation, including methods based on the probability integral transform and the K ..."
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Cited by 20 (3 self)
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This paper outlines a testing procedure for assessing the relative outofsample predictive accuracy of multiple conditional distribution models, and surveys existing related methods in the area of predictive density evaluation, including methods based on the probability integral transform and the KullbackLeibler Information Criterion. The procedure is closely related to Andrews ’ (1997) conditional Kolmogorov test and to White’s (2000) reality check approach, and involves comparing square ( (approximation) errors associated with models i, i =1,..., n, by constructing weighted averages over U of E Fi(uZ t, θ † i) − F0(uZ t)) 2, θ0) , where F0(··) and Fi(··) are true and approximate distributions, u ∈ U, and U is a possibly unbounded set on the real line. Appropriate bootstrap procedures for obtaining critical values for tests constructed using this measure of loss in conjunction with predictions obtained via rolling and recursive estimation schemes are developed. We then apply these bootstrap procedures to the case of obtaining critical values for our predictive accuracy test. A Monte Carlo experiment comparing our bootstrap methods with methods that do not include location bias adjustment terms is provided, and results indicate coverage improvement when our proposed bootstrap procedures are used. Finally, an empirical example comparing alternative predictive densities for U.S. inflation is given.
Nonparametric Estimation of Distributional Policy Effects
, 2008
"... PRELIMINARY VERSION. COMMENTS ARE WELCOME. This paper proposes a fully nonparametric procedure to evaluate the effect of a counterfactual change in the distribution of some covariates on the unconditional distribution of an outcome variable of interest. In contrast to other methods, we do not restri ..."
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Cited by 15 (1 self)
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PRELIMINARY VERSION. COMMENTS ARE WELCOME. This paper proposes a fully nonparametric procedure to evaluate the effect of a counterfactual change in the distribution of some covariates on the unconditional distribution of an outcome variable of interest. In contrast to other methods, we do not restrict attention to the effect on the mean. In particular, our method can be used to conduct inference on the change of the distribution function as a whole, its moments and quantiles, inequality measures such as the Lorenz curve or Gini coefficient, and to test for stochastic dominance. The practical applicability of our procedure is illustrated via a simulation study and an empirical application.
2004) “Size and Power of Some Stochastic Dominance Tests”, Working Paper
"... The sizes and powers of some stochastic ..."
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Testing for restricted stochastic dominance
 Econometric Reviews
, 2013
"... and Bram Thuysbaert for helpful comments. ..."
The Limit of FiniteSample Size and a Problem with Subsampling
, 2007
"... This paper considers inference based on a test statistic that has a limit distribution that is discontinuous in a nuisance parameter or the parameter of interest. The paper shows that subsample, bn <nbootstrap, and standard fixed critical value tests based on such a test statistic often have asym ..."
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Cited by 12 (3 self)
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This paper considers inference based on a test statistic that has a limit distribution that is discontinuous in a nuisance parameter or the parameter of interest. The paper shows that subsample, bn <nbootstrap, and standard fixed critical value tests based on such a test statistic often have asymptotic size–defined as the limit of the finitesample size–that is greater than the nominal level of the tests. We determine precisely the asymptotic size of such tests under a general set of highlevel conditions that are relatively easy to verify. The highlevel conditions are verified in several examples. Analogous results are established for confidence intervals. The results apply to tests and confidence intervals (i) when a parameter may be near a boundary, (ii) for parameters defined by moment inequalities, (iii) based on superefficient or shrinkage estimators, (iv) based on postmodel selection estimators, (v) in scalar and vector autoregressive models with roots that may be close to unity, (vi) in models with lack of identification at some point(s) in the parameter space, such as models with weak instruments and threshold autoregressive models, (vii) in predictive