Download or read book Autoregressive Model Inference in Finite Samples written by Hans Einar Wensink and published by . This book was released on 1996 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Inference in Possibly Integrated Vector Autoregressive Models written by Hiroshi Yamada and published by . This book was released on 1996 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Studies in Asymptotic and Finite Sample Inference of Nonstationary and Nearly Nonstationary Autoregressive Models written by Juha Antti Ahtola and published by . This book was released on 1983 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Stability of First order Autoregressive Models Case of Small Samples written by Hocine Fellag and published by LAP Lambert Academic Publishing. This book was released on 2012 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book studies the stability of estimators of autoregressive models in the case of finite samples (nonasymptotic setup). The stability is one of aspects of approaches of robustness proposed by Zielinki (institute of mathematics, academy of sciences, Warsaw, Poland). When an autoregressive model is violated, the well known least square estimator presents a high variability which makes this estimator rather useless. An unexpected fact we discovered is the lack of monotonicity of the bias when the amount of contamination is growing. Similar effects for the Student and ANOVA tests are studied in this document. Also, a review on various approaches of robustness and some results on estimation of a gaussian autoregressive model are presented. This document is very useful for beginners in research works connected with robust inference in time series."
Download or read book Essays on Finite Sample Inference and Financial Econometrics written by Yong Bao and published by . This book was released on 2004 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Markovian Processes Two sided Autoregressions and Finite sample Inference for Stationary and Nonstationary Autoregressive Processes written by Jean-Marie Dufour and published by . This book was released on 1999 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Seemingly Unrelated Regression Equations Models written by Virendera K. Srivastava and published by CRC Press. This book was released on 2020-08-14 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the scattered literature associated with the seemingly unrelated regression equations (SURE) model used by econometricians and others. It focuses on the theoretical statistical results associated with the SURE model.
Download or read book Finite Sample Econometrics written by Aman Ullah and published by OUP Oxford. This book was released on 2004-05-20 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and unified treatment of finite sample statistics and econometrics, a field that has evolved in the last five decades. Within this framework, this is the first book which discusses the basic analytical tools of finite sample econometrics, and explores their applications to models covered in a first year graduate course in econometrics, including repression functions, dynamic models, forecasting, simultaneous equations models, panel data models, and censored models. Both linear and nonlinear models, as well as models with normal and non-normal errors, are studied. Finite sample results are extremely useful for applied researchers doing proper econometric analysis with small or moderately large sample data. Finite sample econometrics also provides the results for very large (asymptotic) samples. This book provides simple and intuitive presentations of difficult concepts, unified and heuristic developments of methods, and applications to various econometric models. It provides a new perspective on teaching and research in econometrics, statistics, and other applied subjects.
Download or read book Finite sample Simulation based Inference in VAR Models with Applications to Order Selection and Causality Testing written by Dufour, Jean-Marie and published by Montréal : CIRANO. This book was released on 2005 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Finite sample Simulation based Inference in VAR Models with Applications to Order Selection and Causality Testing written by Jean-Marie Dufour and published by Centre interuniversitaire de recherche en économie quantitative. This book was released on 2005* with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Likelihood based Inference in Cointegrated Vector Autoregressive Models written by Søren Johansen and published by Oxford University Press, USA. This book was released on 1995 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is concerned with the statistical analysis of multivariate systems of non-stationary time series of type I. It applies the concepts of cointegration and common trends in the framework of the Gaussian vector autoregressive model.
Download or read book Finite Sample Bias of the Least Squares Estimator in an AR p Model written by Kerry David Patterson and published by . This book was released on 1999 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Autocorrelation and Spectral Analysis written by Piet M. T. Broersen and published by Springer Science & Business Media. This book was released on 2006-04-20 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spectral analysis requires subjective decisions which influence the final estimate and mean that different analysts can obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that is only acceptable if it is close to the best attainable accuracy for most types of stationary data. This book describes a method which fulfils the above near-optimal-solution criterion, taking advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data.
Download or read book Statistical Inference for Some Econometric Time Series Models written by Yang Li and published by . This book was released on 2017-01-26 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation, "Statistical Inference for Some Econometric Time Series Models" by Yang, Li, 李杨, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: With the increasingly economic activities, people have more and more interest in econometric models. There are two mainstream econometric models which are very popular in recent decades. One is quantile autoregressive (QAR) model which allows varying-coefficients in linear time series and greatly promotes the ranges of regression research. The first topic of this thesis is to focus on the modeling of QAR model. We propose two important measures, quantile correlation (QCOR) and quantile partial correlation (QPCOR). We then apply them to QAR models, and introduce two valuable quantities, the quantile autocorrelation function (QACF) and the quantile partial autocorrelation function (QPACF). This allows us to extend the Box-Jenkins three-stage procedure (model identification, model parameter estimation, and model diagnostic checking) from classical autoregressive models to quantile autoregressive models. Specifically, the QPACF of an observed time series can be employed to identify the autoregressive order, while the QACF of residuals obtained from the model can be used to assess the model adequacy. We not only demonstrate the asymptotic properties of QCOR, QPCOR, QACF and PQACF, but also show the large sample results of the QAR estimates and the quantile version of the Ljung- Box test. Moreover, we obtain the bootstrap approximations to the distributions of parameter estimators and proposed measures. Simulation studies indicate that the proposed methods perform well in finite samples, and an empirical example is presented to illustrate the usefulness of QAR model. The other important econometric model is autoregressive conditional duration (ACD) model which is developed with the purpose of depicting ultra high frequency (UHF) financial time series data. The second topic of this thesis is designed to incorporate ACD model with one of the extreme value distributions, i.e. Frechet distribution. We apply the maximum likelihood estimation (MLE) to Frechet ACD models and derive its generalized residuals for model adequacy checking. It is noteworthy that simulations show a relative greater sensitiveness in the linear parameters to sampling errors. This phenomenon successfully reflects the skewness of the Frechet distribution and suggests a method to practitioners in proceeding model accuracy. Furthermore, we present the empirical sizes and powers for Box-Pierce, Ljung-Box and modified Box-Pierce statistics as comparisons of the proposed portmanteau statistic. In addition to the Frechet ACD, we also systematically analyze theWeibull ACD, where the Weibull distribution is the other nonnegative extreme value distribution. The last topic of the thesis explains the estimation and diagnostic checking the Weibull ACD model. By investigating the MLE in this model, there exhibits a slight sensitiveness in linear parameters. However, there is an obvious phenomenon on the trade-off between the skewness of Weibull distribution and the sampling error when the simulations are conducted. Moreover, the asymptotic properties are also studied for the generalized residuals and a goodness-of-fit test is employed to obtain a portmanteau statistic. Through the simulation results in size and power, it shows that Weibull ACD is superior to Frechet ACD in specifying the wrong model. This is meaningful in practice. DOI: 10.5353/th_b5153693 Subjects: Econometrics Time-series analysis
Download or read book Inference in Cointegrated Var Models written by Alessandra Canepa and published by LAP Lambert Academic Publishing. This book was released on 2009-10 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obtaining reliable inference procedures is one of the main challenges of econometric research. Test statistics are usually based on applications of the central limit theorem. However, in order to work well the first order asymptotic approximation requires that the asymptotic distribution is an accurate approximation to the finite sample distribution. When dealing with time series models, this is not generally the case. In this book we investigate the small sample performance of various bootstrap based inference procedures when applied to vector autoregressive models. Special attention is given to Johansen s maximum likelihood method for conducting inference on cointegrated VAR models. Throughout the book, empirical applications are provided to illustrate the bootstrap method and its applications. The analysis should provide some guidance to practitioners in doubt about which inference procedure to use when dealing with cointegrated VAR models.
Download or read book Accurate Finite Sample Inference for Generalized Linear Models and Models on Overidentifying Moment Conditions written by Ndame Lô and published by . This book was released on 2006 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classical inference in statistic and econometric models is typically carried out by means of asymptotic approximations to the sampling distribution of estimators and test statistics. These approximations often do not provide accurate p-values and confidences intervals, especially when the sample size is small. Moreover, even if the sample size is large, the accuracy can be poor due to model misspecification (nonrobustness). Several alternative techniques have been proposed in the statistic and econometric literature to improve the accuracy of clasical inference. In general, these alternatives address either the accuracy of the first-order approximations or the nonrobustness issue. However, the development of general procedures which are both robust and second order accurate is still an open question. In this thesis, we propose an alternative statistical test wich has both robustness and small sample properties for two large and important classes of models: Generalized Linear Models (GLM) and models on overidentifying moments conditions.
Download or read book Simulation based Inference in Econometrics written by Roberto Mariano and published by Cambridge University Press. This book was released on 2000-07-20 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: This substantial volume has two principal objectives. First it provides an overview of the statistical foundations of Simulation-based inference. This includes the summary and synthesis of the many concepts and results extant in the theoretical literature, the different classes of problems and estimators, the asymptotic properties of these estimators, as well as descriptions of the different simulators in use. Second, the volume provides empirical and operational examples of SBI methods. Often what is missing, even in existing applied papers, are operational issues. Which simulator works best for which problem and why? This volume will explicitly address the important numerical and computational issues in SBI which are not covered comprehensively in the existing literature. Examples of such issues are: comparisons with existing tractable methods, number of replications needed for robust results, choice of instruments, simulation noise and bias as well as efficiency loss in practice.
