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Moment Restrictions (moment + restriction)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	100%

Selected Abstracts

Empirical Likelihood-Based Inference in Conditional Moment Restriction Models

ECONOMETRICA, Issue 6 2004
Yuichi Kitamura
This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples. [source]

Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models

THE ECONOMETRICS JOURNAL, Issue 2 2009
Francesco Bravo
Summary, This paper shows how the blockwise generalized empirical likelihood method can be used to obtain valid asymptotic inference in non-linear dynamic moment conditions models for possibly non-stationary weakly dependent stochastic processes. The results of this paper can be used to construct test statistics for overidentifying moment restrictions, for additional moments, and for parametric restrictions expressed in mixed implicit and constraint form. Monte Carlo simulations seem to suggest that some of the proposed test statistics have competitive finite sample properties. [source]

Alternative tilts for nonparametric option pricing

THE JOURNAL OF FUTURES MARKETS, Issue 10 2010
M. Ryan Haley
This study generalizes the nonparametric approach to option pricing of Stutzer, M. (1996) by demonstrating that the canonical valuation methodology introduced therein is one member of the Cressie,Read family of divergence measures. Alhough the limiting distribution of the alternative measures is identical to the canonical measure, the finite sample properties are quite different. We assess the ability of the alternative divergence measures to price European call options by approximating the risk-neutral, equivalent martingale measure from an empirical distribution of the underlying asset. A simulation study of the finite sample properties of the alternative measure changes reveals that the optimal divergence measure depends upon how accurately the empirical distribution of the underlying asset is estimated. In a simple Black,Scholes model, the optimal measure change is contingent upon the number of outliers observed, whereas the optimal measure change is a function of time to expiration in the stochastic volatility model of Heston, S. L. (1993). Our extension of Stutzer's technique preserves the clean analytic structure of imposing moment restrictions to price options, yet demonstrates that the nonparametric approach is even more general in pricing options than originally believed. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:983,1006, 2010 [source]

TWO-STEP EMPIRICAL LIKELIHOOD ESTIMATION UNDER STRATIFIED SAMPLING WHEN AGGREGATE INFORMATION IS AVAILABLE,

THE MANCHESTER SCHOOL, Issue 5 2006
ESMERALDA A. RAMALHO
Empirical likelihood is appropriate to estimate moment condition models when a random sample from the target population is available. However, many economic surveys are subject to some form of stratification, in which case direct application of empirical likelihood will produce inconsistent estimators. In this paper we propose a two-step empirical likelihood estimator to deal with stratified samples in models defined by unconditional moment restrictions in the presence of some aggregate information such as the mean and the variance of the variable of interest. A Monte Carlo simulation study reveals promising results for many versions of the two-step empirical likelihood estimator. [source]