Improving Robust Model Selection Tests For Dynamic Models
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Improving Robust Model Selection Tests For Dynamic Models
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Author : Hwan-sik Choi
language : en
Publisher:
Release Date : 2010
Improving Robust Model Selection Tests For Dynamic Models written by Hwan-sik Choi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
We propose an improved model selection test for dynamic models based on the method of Rivers and Vuong (2002) using a new asymptotic approximation to the sampling distribution of a new test statistic. The model selection test is applicable to dynamic models with very general selection criteria and estimation methods. Since our test statistic does not assume the exact form of a true model, the test is essentially nonparametric once competing models are estimated. For the unkown serial correlation in data, we use a Heteroskedasticity/Autocorrelation Consistent (HAC) variance estimator, and the sampling distribution of the test statistic is approximated by the fixed-b asymptotic approximation (Kiefer and Vogelsang (2005)). The asymptotic approximation depends on kernel functions and bandwidth parameters used in HAC estimators. We compare the finite sample performance of the new test with the bootstrap methods as well as with the standard normal approximations, and show that the fixed-b asymptotics and the bootstrap methods are markedly superior to the standard normal approximation for a moderate sample size for time series data. An empirical application for foreign exchange rate forecasting models is presented, and the result shows the normal approximation to the distribution of the test statistic considered appears to overstate the data's ability to distinguish between two competing models.
Robust Model Selection In Dynamic Models With An Application To Comparing Predictive Accuracy
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Author : Nicholas M. Kiefer
language : en
Publisher:
Release Date : 2006
Robust Model Selection In Dynamic Models With An Application To Comparing Predictive Accuracy written by Nicholas M. Kiefer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.
A model selection procedure based on a general criterion function, with an example of the Kullback-Leibler Information Criterion (KLIC) using quasi-likelihood functions, is considered for dynamic non-nested models. We propose a robust test which generalizes Lien and Vuong's (1987) test with a Heteroscadasticity/Autocorrelation (HAC) variance estimator. We use the fixed-b asymptotics developed in Kiefer and Vogelsang (2005) to improve the asymptotic approximation to the sampling distribution of the test statistic. The fixed-b approach is compared with a bootstrap method and the standard normal approximation in Monte Carlo simulations. The fixed-b asymptotics and the bootstrap method are found to be markedly superior to the standard normal approximation. An empirical application for foreign exchange rate forecasting models is presented.
The Robustness Of Model Selection Rules
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Author : Jochen A. Jungeilges
language : en
Publisher: LIT Verlag Münster
Release Date : 1992
The Robustness Of Model Selection Rules written by Jochen A. Jungeilges and has been published by LIT Verlag Münster this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Business & Economics categories.
Model Selection In A Multi Hypothesis Test Setting Applications In Financial Econometrics
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Author : Francesco Esposito
language : en
Publisher:
Release Date : 2018
Model Selection In A Multi Hypothesis Test Setting Applications In Financial Econometrics written by Francesco Esposito and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
In this thesis, we investigate model selection in a general setting and perform several exercises in financial econometrics. We present the multi-hypothesis testing (MHT) framework, with which we design different type of model comparisons. We distinguish between test of model performance significance, of relative and absolute model performance and apply our framework to market risk forecasting model, to latent factor jump-diffusion models employed for the estimation of the statistical measure of an equity index, as well as to equity option pricing models. We develop original tests and, with regard to the proper exercise of model selection from an initial battery of models without any reference to a benchmark model, we combine the MHT approach with the model confidence set (MCS) to deliver a novel test of model comparison that is performed along with the established version of the MCS, as well as with an alternative simplified new MCS test that are detailed in the course of this work. We collect empirical evidence concerning model comparison in several subjects. With respect to market risk forecasting models, we have found that models capturing volatility clustering or targeting directly an auto-correlated conditional distribution percentile, perform better than the target model set and in particular, better than the historical simulation, widely employed by practitioners, and better than the so called RiskMetrics model. With respect to the equity index data dynamics, we have found that the popular affine jump-diffusion model requires a CEV augmentation to perform appropriately and that those models are slightly overperformed by an alternative stochastic volatility model, characterised by stochastic hazard with high frequency small jumps. The test performed over a large model set employed in the option pricing exercise points to a wide similarity of the results obtained by the many model specifications of the superior exponential volatility model, therefore suggesting a more careful adjustment of the model complexity. The model selection framework has proven very flexible in dealing with the varied collection of statistical problems. In particular, our main contribution represented by the generalised MHT based MCS test provides a method for model selection that is robust to finite sample distribution and that has the advantage of an adjustable tolerance for false rejections, allowing conservative to aggressive testing profiles.
Essays On Robust Model Selection And Model Averaging For Linear Models
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Author : Le Chang
language : en
Publisher:
Release Date : 2017
Essays On Robust Model Selection And Model Averaging For Linear Models written by Le Chang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Model selection is central to all applied statistical work. Selecting the variables for use in a regression model is one important example of model selection. This thesis is a collection of essays on robust model selection procedures and model averaging for linear regression models. In the first essay, we propose robust Akaike information criteria (AIC) for MM-estimation and an adjusted robust scale based AIC for M and MM-estimation. Our proposed model selection criteria can maintain their robust properties in the presence of a high proportion of outliers and the outliers in the covariates. We compare our proposed criteria with other robust model selection criteria discussed in previous literature. Our simulation studies demonstrate a significant outperformance of robust AIC based on MM-estimation in the presence of outliers in the covariates. The real data example also shows a better performance of robust AIC based on MM-estimation. The second essay focuses on robust versions of the "Least Absolute Shrinkage and Selection Operator" (lasso). The adaptive lasso is a method for performing simultaneous parameter estimation and variable selection. The adaptive weights used in its penalty term mean that the adaptive lasso achieves the oracle property. In this essay, we propose an extension of the adaptive lasso named the Tukey-lasso. By using Tukey's biweight criterion, instead of squared loss, the Tukey-lasso is resistant to outliers in both the response and covariates. Importantly, we demonstrate that the Tukey-lasso also enjoys the oracle property. A fast accelerated proximal gradient (APG) algorithm is proposed and implemented for computing the Tukey-lasso. Our extensive simulations show that the Tukey-lasso, implemented with the APG algorithm, achieves very reliable results, including for high-dimensional data where p>n. In the presence of outliers, the Tukey-lasso is shown to offer substantial improvements in performance compared to the adaptive lasso and other robust implementations of the lasso. Real data examples further demonstrate the utility of the Tukey-lasso. In many statistical analyses, a single model is used for statistical inference, ignoring the process that leads to the model being selected. To account for this model uncertainty, many model averaging procedures have been proposed. In the last essay, we propose an extension of a bootstrap model averaging approach, called bootstrap lasso averaging (BLA). BLA utilizes the lasso for model selection. This is in contrast to other forms of bootstrap model averaging that use AIC or Bayesian information criteria (BIC). The use of the lasso improves the computation speed and allows BLA to be applied even when the number of variables p is larger than the sample size n. Extensive simulations confirm that BLA has outstanding finite sample performance, in terms of both variable and prediction accuracies, compared with traditional model selection and model averaging methods. Several real data examples further demonstrate an improved out-of-sample predictive performance of BLA.
Robustness Tests For Quantitative Research
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Author : Eric Neumayer
language : en
Publisher: Cambridge University Press
Release Date : 2017-08-17
Robustness Tests For Quantitative Research written by Eric Neumayer and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-17 with Business & Economics categories.
This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.
Regression And Time Series Model Selection
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Author : Allan D R Mcquarrie
language : en
Publisher: World Scientific
Release Date : 1998-05-30
Regression And Time Series Model Selection written by Allan D R Mcquarrie and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-05-30 with Mathematics categories.
This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.
Model Selection For Non Linear Dynamic Models
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Author : Massimiliano Giuseppe Marcellino
language : en
Publisher:
Release Date : 2014
Model Selection For Non Linear Dynamic Models written by Massimiliano Giuseppe Marcellino and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
This paper develops tests for selection of competing non-linear dynamic models. The null hypothesis is that the models are equally close the Data Generating Process (DGP), according to a certain measure of closeness. The alternative is that one model is closer to the DGP. The models can be non-nested, overlapping, or nested. They can be correctly specified or not. Their parameters can be estimated by a variety of methods, including Maximum Likelihood, Non-Linear Least Squares, Method of Moments, where the choice depends on the selected measure of closeness to the DGP. The tests are symmetric and directional. Their asymptotic distribution under the null is either normal or a weighted sum of chi-square distributions, depending on the nesting characteristics of the competing models. The comparison of ARMAX and STAR models, and of nested ARMAX-GARCH models are discussed as examples.
Proceedings Of The 2022 3rd International Conference On Big Data And Social Sciences Icbdss 2022
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Author : Guiyun Guan
language : en
Publisher: Springer Nature
Release Date : 2023-02-11
Proceedings Of The 2022 3rd International Conference On Big Data And Social Sciences Icbdss 2022 written by Guiyun Guan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-11 with Computers categories.
This is an open access book. As a leading role in the global megatrend of scientific innovation, China has been creating a more and more open environment for scientific innovation, increasing the depth and breadth of academic cooperation, and building a community of innovation that benefits all. Such endeavors are making new contributions to the globalization and creating a community of shared future. The 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) was held on August 19 – 21, 2022, in Hulunbuir, China. With the support of experts and professors, the ICBDSS 2022 conference successfully held its first conference last year. In order to allow more scholars to have the opportunity to participate in the conference to share and exchange experience. This conference mainly focused on "big data", "social science" and other research fields to discuss. At present, my country has entered the era of "big data cloud migration", that is, the era of big data, the Internet of things, cloud computing and mobile Internet. The market demand for big data talents is also increasing day by day. The purpose of the conference is to provide a way for experts, scholars, engineering technicians, and technical R&D personnel engaged in big data and social science research to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote the academic achievement industry Platform for chemical cooperation. The conference sincerely invites experts, scholars from domestic and foreign universities, scientific research institutions, business people and other relevant personnel to participate in the conference.
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1995
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Aeronautics categories.