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Maximum Likelihood Estimation Of Misspecified Models
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Book Synopsis Maximum Likelihood Estimation of Misspecified Models by : T. Fomby
Download or read book Maximum Likelihood Estimation of Misspecified Models written by T. Fomby and published by Elsevier. This book was released on 2003-12-12 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
Book Synopsis Note on Maximum-likelihood Estimation of Misspecified Models by : Gregory C. Chow
Download or read book Note on Maximum-likelihood Estimation of Misspecified Models written by Gregory C. Chow and published by . This book was released on 1982 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation in Possibly Misspecified Dynamic Models with Time Inhomogeneous Markov Regimes by : Demian Pouzo
Download or read book Maximum Likelihood Estimation in Possibly Misspecified Dynamic Models with Time Inhomogeneous Markov Regimes written by Demian Pouzo and published by . This book was released on 2016 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers maximum likelihood (ML) estimation in a large class of models with hidden Markov regimes. We investigate consistency and local asymptotic normality of the ML estimator under general conditions which allow for autoregressive dynamics in the observable process, time-inhomogeneous Markov regime sequences, and possible model misspecification. A Monte Carlo study examines the finite-sample properties of the ML estimator. An empirical application is also discussed.
Book Synopsis Econometric Modelling with Time Series by : Vance Martin
Download or read book Econometric Modelling with Time Series written by Vance Martin and published by Cambridge University Press. This book was released on 2013 with total page 925 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Maximum likelihood estimation is a general method for estimating the parameters of econometric models from observed data. The principle of maximum likelihood plays a central role in the exposition of this book, since a number of estimators used in econometrics can be derived within this framework. Examples include ordinary least squares, generalized least squares and full-information maximum likelihood. In deriving the maximum likelihood estimator, a key concept is the joint probability density function (pdf) of the observed random variables, yt. Maximum likelihood estimation requires that the following conditions are satisfied. (1) The form of the joint pdf of yt is known. (2) The specification of the moments of the joint pdf are known. (3) The joint pdf can be evaluated for all values of the parameters, 9. Parts ONE and TWO of this book deal with models in which all these conditions are satisfied. Part THREE investigates models in which these conditions are not satisfied and considers four important cases. First, if the distribution of yt is misspecified, resulting in both conditions 1 and 2 being violated, estimation is by quasi-maximum likelihood (Chapter 9). Second, if condition 1 is not satisfied, a generalized method of moments estimator (Chapter 10) is required. Third, if condition 2 is not satisfied, estimation relies on nonparametric methods (Chapter 11). Fourth, if condition 3 is violated, simulation-based estimation methods are used (Chapter 12). 1.2 Motivating Examples To highlight the role of probability distributions in maximum likelihood estimation, this section emphasizes the link between observed sample data and 4 The Maximum Likelihood Principle the probability distribution from which they are drawn"-- publisher.
Book Synopsis Misspecification Analysis by : Theo K. Dijkstra
Download or read book Misspecification Analysis written by Theo K. Dijkstra and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Issues of Misspecification in Long Memory Models by : Kanchana Nadarajah
Download or read book Issues of Misspecification in Long Memory Models written by Kanchana Nadarajah and published by . This book was released on 2013 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Misspecification of the short memory dynamics in a long memory model has serious repercussions for the asymptotic properties of any estimator of the long memory parameter. Under misspecification, the estimator converges in probability to a value called the pseudo-true value, which is different from the true value of the parameter. Intuitively, of all the family of spectral densities, the spectral density with the pseudo-true value is the closest spectral density to the true spectral density. Further consequences of misspecification are associated with the rate of convergence and the asymptotic distribution of the estimator of the parameter of the misspecified model. Both the rate of convergence and the asymptotic distribution of the parametric estimator of the misspecified model depends, in turn, on the difference between the true and pseudo-true values. We prove that under misspecification, frequency domain maximum likelihood estimation, Whittle estimation, time domain maximum likelihood estimation and conditional sum of squares estimation are asymptotically equivalent. However, our simulation study demonstrates that in small and medium sized samples, the performance of the parametric estimators of the misspecified model, in terms of bias, mean squared error and the form of the sampling distribution, differs across estimators. Overall, under misspecification, the conditional sum of squares estimator outperforms the other parametric estimators in small and medium sized samples. Further, the approximate frequency domain maximum likelihood estimator is the least efficient of all parametric estimators of the misspecified model, overall. In certain circumstances, where the difference between the true and the pseudo-true value of the long memory parameter is sufficiently large, a clear distinction between the frequency domain and time domain estimators can be observed in small samples. However, as the sample size increases, the behaviour of all of the parametric estimators of the misspecified model is consistent with the theoretical asymptotic results. Whilst misspecified parametric estimators of the long memory parameter are inconsistent for its true value, any semi-parametric estimator is consistent, although very biased in small samples. Thus, we compare the parametric estimators of the long memory parameter in the misspecified model with the semi-parametric Geweke and Porter-Hudak (GPH) estimator, to investigate whether any misspecified parametric estimator is less biased, or more efficient, than this particular semi-parametric estimator to measure the true value of the long memory parameter in finite samples. The CSS estimator under the misspecified model outperforms the GPH estimator in large finite samples in terms of bias and mean squared error, when the misspecified model is close to the true model. If the misspecified model is substantially different from the true model, then the GPH estimator is preferred over the four parametric estimators of the misspecified model in finite samples.
Book Synopsis Maximum Likelihood Estimation by : Scott R. Eliason
Download or read book Maximum Likelihood Estimation written by Scott R. Eliason and published by SAGE. This book was released on 1993 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a short introduction to Maximum Likelihood (ML) Estimation. It provides a general modeling framework that utilizes the tools of ML methods to outline a flexible modeling strategy that accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models linking endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, the author discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.
Book Synopsis Estimation, Inference and Specification Analysis by : Halbert White
Download or read book Estimation, Inference and Specification Analysis written by Halbert White and published by Cambridge University Press. This book was released on 1996-06-28 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the consequences of misspecifications for the interpretation of likelihood-based methods of statistical estimation and interference. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated.
Book Synopsis On Maximum Likelihood Estimation Under Misspecification with Applications to Generalized Linear Models by : Ludwig Fahrmeir
Download or read book On Maximum Likelihood Estimation Under Misspecification with Applications to Generalized Linear Models written by Ludwig Fahrmeir and published by . This book was released on 1987 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation and Inference by : Russell B. Millar
Download or read book Maximum Likelihood Estimation and Inference written by Russell B. Millar and published by John Wiley & Sons. This book was released on 2011-07-26 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
Book Synopsis Estimation in Conditionally Heteroscedastic Time Series Models by : Daniel Straumann
Download or read book Estimation in Conditionally Heteroscedastic Time Series Models written by Daniel Straumann and published by Springer Science & Business Media. This book was released on 2006-01-27 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.
Book Synopsis Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models by : Francisco Blasques
Download or read book Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models written by Francisco Blasques and published by . This book was released on 2016 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Invertibility conditions for observation-driven time series models often fail to be guaranteed in empirical applications. As a result, the asymptotic theory of maximum likelihood and quasi-maximum likelihood estimators may be compromised. We derive considerably weaker conditions that can be used in practice to ensure the consistency of the maximum likelihood estimator for a wide class of observation-driven time series models. Our consistency results hold for both correctly specified and misspecified models. The practical relevance of the theory is highlighted in a set of empirical examples. We further obtain an asymptotic test and confidence bounds for the unfeasible “true” invertibility region of the parameter space.
Book Synopsis Maximum Likelihood Estimation of Functional Relationships by : Nico J.D. Nagelkerke
Download or read book Maximum Likelihood Estimation of Functional Relationships written by Nico J.D. Nagelkerke and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of functional relationships concerns itself with inference from models with a more complex error structure than those existing in regression models. We are familiar with the bivariate linear relationship having measurement errors in both variables and the fact that the standard regression estimator of the slope underestimates the true slope. One complication with inference about parameters in functional relationships, is that many of the standard properties of likelihood theory do not apply, at least not in the form in which they apply to e.g. regression models. This is probably one of the reasons why these models are not adequately discussed in most general books on statistics, despite their wide applicability. In this monograph we will explore the properties of likelihood methods in the context of functional relationship models. Full and conditional likelihood methods are both considered. Possible modifications to these methods are considered when necessary. Apart from exloring the theory itself, emphasis shall be placed upon the derivation of useful estimators and their second moment properties. No attempt is made to be mathematically rigid. Proofs are usually outlined with extensive use of the Landau 0(.) and 0(.) notations. It is hoped that this shall provide more insight than the inevitably lengthy proofs meeting strict standards of mathematical rigour.
Book Synopsis Maximum Likelihood Estimation in Mis-specified Reliability Distributions by : Andrea John
Download or read book Maximum Likelihood Estimation in Mis-specified Reliability Distributions written by Andrea John and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation in Mis-specified Reliability Distributions by : Andrea John
Download or read book Maximum Likelihood Estimation in Mis-specified Reliability Distributions written by Andrea John and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Targeted Learning by : Mark J. van der Laan
Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
Book Synopsis Methods for Estimation and Inference in Modern Econometrics by : Stanislav Anatolyev
Download or read book Methods for Estimation and Inference in Modern Econometrics written by Stanislav Anatolyev and published by CRC Press. This book was released on 2011-06-07 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference.
