Using GARCH models for density prediction of stock index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between qualities of whole ...density forecasts, whereas the Bayesian approach exhibits significantly better left-tail forecast accuracy.
► We compare frequentist and Bayesian density predictions of GARCH models. ► We test the overall density and the left tail using KLIC and censored likelihood. ► Bayesian estimation outperforms its frequentist counterpart.
We propose a nonparametric likelihood ratio testing procedure for choosing between a parametric (likelihood) model and a moment condition model when both models could be misspecified. Our procedure ...is based on comparing the Kullback–Leibler Information Criterion (KLIC) between the parametric model and moment condition model. We construct the KLIC for the parametric model using the difference between the parametric log likelihood and a sieve nonparametric estimate of population entropy, and obtain the KLIC for the moment model using the empirical likelihood statistic. We also consider multiple
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model comparison tests, when all the competing models could be misspecified, and some models are parametric while others are moment-based. We evaluate the performance of our tests in a Monte Carlo study, and apply the tests to an example from industrial organization.
Bioequivalence testing has been traditionally centered in summary variables such as AUC, C (max) and t (max) which filter out the intrinsic information conveyed by discrete sequential ...concentration-time observations. Comparing entire concentration-time profiles between test and reference formulations for bioequivalence purposes provides stronger evidence about either their similarity or their discrepancy. The Kullback-Leibler information criterion (KLIC) may be computed for each concentration-time across all subjects between formulations of the same drug, with a standard crossover study design. It has been shown that if properly scaled it follow a chi-squared distribution and dependent p-values may be computed in order to construct a bioequivalence criterion. Extensive simulations and real data were used to compare it with the current standard procedures. This statistical shape analysis method may provide important clinical and regulatory advantages.
Uma vez que as amostras, para consumo e retornos de ativos, usando dados brasileiros, possuem tamanho pequeno, o que faz com que o poder do teste de restrições de sobre-identificação baseado no GMM ...seja baixo, uma abordagem GMM impõe dificuldades à avaliação de núcleos de formação de preço de ativos mais adequados para descrever fenômenos associados ao mercado de ativos no Brasil. Este ensaio trata da questão de avaliar e testar dois modelos de formação de preço de ativos utilizando um estimador de método de momentos baseado em teoria da informação, que minimiza o Critério de Informações de Kullback-Leibler (KLIC).O objetivo é comparar o método GMM tradicional com a abordagem alternativa, baseada em teoria da informação, que tem propriedades em amostras finitas promissoras. O ensaio se concentra em comparar resultados relativos a testes de restrições de sobre-identificação e as estimativas de parâmetros.
This paper investigates the behaviour of estimators based on the Kullback–Leibler information criterion (KLIC), as an alternative to the generalized method of moments (GMM). We first study the ...estimators in a Monte Carlo simulation model of consumption growth with power utility. Then we compare KLIC and GMM estimators in macroeconomic applications, in which preference parameters are estimated with aggregate data. KLIC probability measures serve as useful diagnostics. In dependent data, tests of overidentifying restrictions in the KLIC framework have size properties comparable to those of the J-test in iterated GMM, but superior size-adjusted power.
Using well-known GARCH models for density prediction of daily S&P 500 and Nikkei 225 index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is ...found between the qualities of the forecasts of the whole density, whereas the Bayesian approach exhibits significantly better left-tail forecast accuracy.
We apply a suite of models to produce quasi-real-time density forecasts of Norwegian GDP and in ation, and evaluate dfferent combination and selection methods using the Kullback-Leibler information ...criterion (KLIC). We use linear and logarithmic opinion pools in conjunction with various weighting schemes, and we compare these combinations to two different selection methods. In our application, logarithmic opinion pools were better than linear opinion pools, and score-based weights were generally superior to other weighting schemes. Model selection generally yielded poor density forecasts, as evaluated by KLIC.
In this paper we introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly ...misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. Use of KLIC is practically attractive as well as convenient, given its equivalent of the widely used LR test. We include an illustrative simulation to compare a set of distributions, including symmetric and asymmetric distribution, and a family of GARCH volatility models. Our results on simulated data show that the choice of the conditional distribution appears to be a more dominant factor in determining the adequacy and accuracy (quality) of density forecasts than the choice of volatility model.
Being able to choose most suitable volatility model and distribution specification is a more demanding task. This paper introduce an analyzing procedure using the Kullback-Leibler information ...criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. We include an illustrative simulation and an empirical application to compare a set of distributions, including symmetric/asymmetric distribution, and a family of GARCH volatility models. We highlight the use of our approach to a daily index, the Kuala Lumpur Composite index (KLCI). Our results shows that the choice of the conditional distribution appear to be a more dominant factor in determining the adequacy of density forecasts than the choice of volatility model. Furthermore, the results support the Skewed for KLCI return distribution.
In this paper we introduce the Extended Method of Moments (XMM) estimator. This estimator accommodates a more general set of moment restrictions than the standard Generalized Method of Moments (GMM) ...estimator. More specifically, the XMM differs from the GMM in that it can handle not only uniform conditional moment restrictions (i.e. valid for any value of the conditioning variable), but also local conditional moment restrictions valid for a given fixed value of the conditioning variable. The local conditional moment restrictions are of special relevance in derivative pricing for reconstructing the pricing operator at a given day, by using the information in a few cross-sections of observed traded derivative prices and a time series of underlying asset returns. The estimated derivative prices are consistent for large time series dimension, but fixed number of cross-sectionally observed derivative prices. The asymptotic properties of the XMM estimator are nonstandard, since the combination of uniform and local conditional moment restrictions induces different rates of convergence (parametric and nonparametric) for the parameters.
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