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Book Further Results on Pseudo Maximum Likelihood Estimation and Testing in the Constant Elasticity of Variance Continuous Time Model

Download or read book Further Results on Pseudo Maximum Likelihood Estimation and Testing in the Constant Elasticity of Variance Continuous Time Model written by Emma Iglesias and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Handbook Of Applied Investment Research

Download or read book Handbook Of Applied Investment Research written by John B Guerard Jr and published by World Scientific. This book was released on 2020-10-02 with total page 817 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the readers to the rapidly growing literature and latest results on financial, fundamental and seasonal anomalies, stock selection modeling and portfolio management. Fifty years ago, finance professors taught the Efficient Markets Hypothesis which states that the average investor could not outperform the stock market based on technical, seasonal and fundamental data. Many, if not most faculty and investors, no longer share that opinion. In this book, the authors report original empirical evidence that applied investment research can produce statistically significant stock selection and excess portfolio returns in the US, and larger excess returns in international and emerging markets.

Book Pseudo variance Quasi maximum Likelihood Estimation of Semiparametric Time Series Models

Download or read book Pseudo variance Quasi maximum Likelihood Estimation of Semiparametric Time Series Models written by Mirko Armillotta and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a novel estimation approach for a general class of semi-parametric time series models where the conditional expectation is modeled through a parametric function. The proposed class of estimators is based on a Gaussian quasi-likelihood function and it relies on the specification of a parametric pseudo-variance that can contain parametric restrictions with respect to the conditional expectation. The specification of the pseudo-variance and the parametric restrictions follow naturally in observation-driven models with bounds in the support of the observable process, such as count processes and double-bounded time series. We derive the asymptotic properties of the estimators and a validity test for the parameter restrictions. We show that the results remain valid irrespective of the correct specification of the pseudo-variance. The key advantage of the restricted estimators is that they can achieve higher efficiency compared to alternative quasi-likelihood methods that are available in the literature. Furthermore, the testing approach can be used to build specification tests for parametric time series models. We illustrate the practical use of the methodology in a simulation study and two empirical applications featuring integer-valued autoregressive processes, where assumptions on the dispersion of the thinning operator are formally tested, and autoregressions for double-bounded data with application to a realized correlation time series.

Book Targeted Maximum Likelihood Estimation Techniques For Time To Event Data and the Implications Of Coarsening An Explanatory Variable Of Interest Via Dichotomization In The Context Of Causal Inference In Semi parametric Models

Download or read book Targeted Maximum Likelihood Estimation Techniques For Time To Event Data and the Implications Of Coarsening An Explanatory Variable Of Interest Via Dichotomization In The Context Of Causal Inference In Semi parametric Models written by Ori Michael Stitelman and published by . This book was released on 2010 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on three important issues in causal inference. The three chapters focus on the common theme of causal inference in semi-parametric models. The first two chapters focus on further developing targeted maximum likelihood estimation (TMLE) methods for particular situations in survival analysis. Chapter 1 presents the collaborative targeted maximum likelihood estimator (C-TMLE) for the treatment specific survival curve. This estimator improves upon commonly used estimators in survival analysis and is particularly necessary for analyzing observational studies, data that exhibits dependent censoring, or both. Chapter 2 presents two interesting parameters of interest for quantifying effect modification in time to event studies. It then presents the TMLE for estimating these parameters. The third chapter presents the implicit assumptions practitioners make when dichotomizing treatment/exposure variables when trying to asses the causal effect of those variables. Chapter 1 - Current methods used to analyze time to event data either, rely on highly parametric assumptions which result in biased estimates of parameters which are purely chosen out of convenience, or are highly unstable because they ignore the global constraints of the true model. By using Targeted Maximum Likelihood Estimation (TMLE) one may consistently estimate parameters which directly answer the statistical question of interest. Targeted Maximum Likelihood Estimators are substitution estimators, which rely on estimating the underlying distribution. However, unlike other substitution estimators, the underlying distribution is estimated specifically to reduce bias in the estimate of the parameter of interest. We will present here an extension of TMLE for observational time to event data, the Collaborative Targeted Maximum Likelihood Estimator (C-TMLE) for the treatment specific survival curve. Through the use of a simulation study we will show that this method improves on commonly used methods in both robustness and efficiency. In fact, we will show that in certain situations the C-TMLE produces estimates whose mean square error is lower than the semi-parametric efficiency bound. We will also demonstrate that a semi-parametric efficient substituiton estimator (TMLE) outperforms a semi-parametric efficient non-substitution estimator (the Augmented Inverse Probability Weighted estimator) in sparse data situations. Lastly, we will show that the bootstrap is able to produce valid 95 percent confidence intervals in sparse data situations, while influence curve based inference breaks down. Chapter 2 -The Cox proportional hazards model or its discrete time analogue, the logistic failure time model, posit highly restrictive parametric models and attempt to estimate parameters which are specific to the model proposed. These methods are typically implemented when assessing effect modification in survival analyses despite their flaws. The targeted maximum likelihood estimation (TMLE) methodology is more robust than the methods typically implemented and allows practitioners to estimate parameters that directly answer the question of interest. TMLE will be used in this chapter to estimate two newly proposed parameters of interest that quantify effect modification in the time to event setting. These methods are then applied to the emph{Tshepo} study, to assess if either gender or baseline CD4 level modify the effect of two cART therapies of interest, efavirenz (EFV) and nevirapine (NVP), on the progression of HIV. The results show that women tend to have more favorable outcomes using EFV while males tend to have more favorable outcomes with NVP. Furthermore, EFV tends to be favorable compared to NVP for individuals at high CD4 levels. Chapter 3 - It is common in analyses designed to estimate the causal effect of a continuous exposure/treatment to dichotomize the variable of interest. By dichotomizing the variable and assessing the causal effect of the newly fabricated variable practitioners are implicitly making assumptions, though typically these assumptions are ignored in the interpretation of the resulting estimates. In this chapter we formally address what assumptions are made by dichotomizing variables to assess the semi-parametrically adjusted associations of these constructed binary variables and an outcome. Two assumptions are presented, either of which must be met, in order for the estimates of the causal effects to be unbiased estimates of the parameters of interest. Those assumptions are titled the Mechanism Equivalence and Effect Equivalence assumptions. Furthermore, we quantify the bias induced when these assumptions are violated. Lastly, we present an analysis of a Malaria study that exemplifies the danger of naively dichotomizing a continuous variable to assess a causal effect.

Book Maximum Penalized Likelihood Estimation

Download or read book Maximum Penalized Likelihood Estimation written by P.P.B. Eggermont and published by Springer Nature. This book was released on 2020-12-15 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints. The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into technical tools from probability theory and applied mathematics.

Book Quasi Likelihood And Its Application

Download or read book Quasi Likelihood And Its Application written by Christopher C. Heyde and published by Springer Science & Business Media. This book was released on 2008-01-08 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first account in book form of all the essential features of the quasi-likelihood methodology, stressing its value as a general purpose inferential tool. The treatment is rather informal, emphasizing essential principles rather than detailed proofs, and readers are assumed to have a firm grounding in probability and statistics at the graduate level. Many examples of the use of the methods in both classical statistical and stochastic process contexts are provided.

Book Finite Sample Properties of the Maximum Likelihood Estimator in Continuous Time Models

Download or read book Finite Sample Properties of the Maximum Likelihood Estimator in Continuous Time Models written by Nancy Milena Hoyos Gomez and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Piecewise Pseudo maximum Likelihood Estimation in Empirical Models of Auctions

Download or read book Piecewise Pseudo maximum Likelihood Estimation in Empirical Models of Auctions written by Stephen G. Donald and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Exact Maximum Likelihood Estimation of Observation driven Econometric Models

Download or read book Exact Maximum Likelihood Estimation of Observation driven Econometric Models written by Francis X. Diebold and published by . This book was released on 1996 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: The possibility of exact maximum likelihood estimation of many observation-driven models remains an open question. Often only approximate maximum likelihood estimation is attempted, because the unconditional density needed for exact estimation is not known in closed form. Using simulation and nonparametric density estimation techniques that facilitate empirical likelihood evaluation, we develop an exact maximum likelihood procedure. We provide an illustrative application to the estimation of ARCH models, in which we compare the sampling properties of the exact estimator to those of several competitors. We find that, especially in situations of small samples and high persistence, efficiency gains are obtained. We conclude with a discussion of directions for future research, including application of our methods to panel data models.

Book Pseudo Maximum Likelihood Estimation Theory and Applications

Download or read book Pseudo Maximum Likelihood Estimation Theory and Applications written by Gail G. Hannon and published by . This book was released on 1978 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pseudo maximum likelihood estimation easily extends to k parameter models, and is of interest in problems in which the likelihood surface is ill-behaved in higher dimensions but well-behaved in lower dimensions. Several signal plus noise or convolution models are examined which exhibit such behavior and satisfy the regularity conditions of the asymptotic theory. For specific models, a numerical comparison of asymptotic variances suggests that a psuedo maximum likelihood estimate of the signal parameter is uniformly more efficient than estimators that have been advanced by previous authors. A number of other potential applications are noted.

Book Discrete Choice Methods with Simulation

Download or read book Discrete Choice Methods with Simulation written by Kenneth Train and published by Cambridge University Press. This book was released on 2009-07-06 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum stimulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. The second edition adds chapters on endogeneity and expectation-maximization (EM) algorithms. No other book incorporates all these fields, which have arisen in the past 25 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

Book Maximum Likelihood Estimation and Hypothesis Testing in the Bivariate Exponential Model of Marshall and Olkin

Download or read book Maximum Likelihood Estimation and Hypothesis Testing in the Bivariate Exponential Model of Marshall and Olkin written by and published by . This book was released on 1971 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: The present work concerns statistical inference in the bivariate exponential distribution introduced by Marshall and Olkin. Even though the distribution has a singular component, the use of a special dominating measure leads to an explicit form of the likelihood whose properties are investigated. The existence, uniqueness and asymptotic distributional properties of the maximum likelihood estimators are studied. Using the criterion of generalized variance, it is shown that the simple unbiased estimators proposed by Arnold are asymptotically less efficient than the maximum likelihood estimators and the loss in efficiency is particularly serious in the case of independence. Uniformly most powerful test for independence is derived for the special model having identical marginal distributions.