Download or read book A Test for Comparing Multiple Misspecified Conditional Distributions written by Norman R. Swanson and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper introduces a conditional Kolmogorov test, in the spirit of Andrews (1997), that allows for comparison of multiple misspecifed conditional distribution models, for the case of dependent observations. A conditional confidence interval version of the test is also discussed. Model accuracy is measured using a distributional analog of mean square error, in which the squared (approximation) error associated with a given model, say model i, is measured in terms of the average over U of E((Fi(u|Zt,0iĴ)-F0(u|Zt0o))squared), where U is a possibly unbounded set on the real line, Zt is the conditioning information set, Fi is the distribution function of a particular candidate model, and F0 is the true (unkown) distribution function. When comparing more than two models, a "benchmark" model is specified, and the test is constructed along the lines of the "reality check" of White (2000). Valid asymptotic critical values are obtained via a version of the block bootstrap which properly captures the effect of parameter estimation error. The results of a small Monte Carlo experiment indicate that the conditional confidence interval version of the test has reasonable finite sample properties even for samples with as few as 60 observations.
Download or read book Handbook of Economic Forecasting written by Graham Elliott and published by Elsevier. This book was released on 2013-08-23 with total page 667 pages. Available in PDF, EPUB and Kindle. Book excerpt: The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. - Focuses on innovation in economic forecasting via industry applications - Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications - Makes details about economic forecasting accessible to scholars in fields outside economics
Download or read book Misspecification Testing in a Class of Conditional Distributional Models written by Christoph Rothe and published by . This book was released on 2011 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Comparing Conditional Distributions Under Measurement Errors of Known Variances written by Stanford University. Department of Statistics and published by . This book was released on 1968 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Flexible Imputation of Missing Data Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
Download or read book Omitted Variables and Misspecification Testing Using Auxiliary Regressions written by Aris Spanos and published by . This book was released on 1985 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Density and Conditional Distribution Based Specification Analysis written by Diep Duong and published by . This book was released on 2013 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: The technique of using densities and conditional distributions to carry out consistent specification testing and model selection amongst multiple diffusion processes have received considerable attention from both financial theoreticians and empirical econometricians over the last two decades. In this paper, we discuss advances to this literature introduced by Corradi and Swanson (2005), who compare the cumulative distribution (marginal or joint) implied by a hypothesized null model with corresponding empirical distributions of observed data. We also outline and expand upon further testing results from Bhardwaj, Corradi and Swanson (BCS: 2008) and Corradi and Swanson (2011). In particular, parametric specification tests in the spirit of the conditional Kolmogorov test of Andrews (1997) that rely on block bootstrap re-sampling methods in order to construct test critical values are first discussed. Thereafter, extensions due to BCS (2008) for cases where the functional form of the conditional density is unknown are introduced, and related continuous time simulation methods are introduced. Finally, we broaden our discussion from single process specification testing to multiple process model selection by discussing how to construct predictive densities and how to compare the accuracy of predictive densities derived from alternative (possibly mis-specified) diffusion models. In particular, we generalize simulation Steps outlined in Cai and Swanson (2011) to multi-factor models where the number of latent variables is larger than three. We finish the chapter with an empirical illustration of model selection amongst alternative short term interest rate models.
Download or read book The Gradient Test written by Artur Lemonte and published by Academic Press. This book was released on 2016-02-05 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Gradient Test: Another Likelihood-Based Test presents the latest on the gradient test, a large-sample test that was introduced in statistics literature by George R. Terrell in 2002. The test has been studied by several authors, is simply computed, and can be an interesting alternative to the classical large-sample tests, namely, the likelihood ratio (LR), Wald (W), and Rao score (S) tests. Due to the large literature about the LR, W and S tests, the gradient test is not frequently used to test hypothesis. The book covers topics on the local power of the gradient test, the Bartlett-corrected gradient statistic, the gradient statistic under model misspecification, and the robust gradient-type bounded-influence test. Covers the background of the gradient statistic and the different models Discusses The Bartlett-corrected gradient statistic Explains the algorithm to compute the gradient-type statistic
Download or read book Recent Advances and Future Directions in Causality Prediction and Specification Analysis written by Xiaohong Chen and published by Springer Science & Business Media. This book was released on 2012-08-01 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.
Download or read book On Multiple Test Procedures for Finding Deviating Parameters written by Bo Palaszewski and published by . This book was released on 1993 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book International Journal of forecasting written by and published by . This book was released on 2004 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Testing the Predictive Accuracy of Possibly Misspecified Binary Choice Models written by Chen Wu and published by . This book was released on 2008 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Binary choice models are often used in economics. One general problem of interest is to test and compare the predictive accuracy of competing models. This dissertation proposes a test of predictive accuracy that allows competing models with invalid assumptions about the distribution of the disturbances, exogeneity of the regressors, functional forms of the conditional means and variances, omitted variables, and other misspecifications. This feature is important since in practice one can rarely, if ever, have complete confidence in the underlying statistical assumptions. The test proposed here has several advantages: (1) It captures the effect of estimation uncertainty on the relative forecast performance. (2) The asymptotic distribution of the test statistic does not depend on the asymptotic distribution of the coefficient estimator. Therefore, the test allows the prediction to be produced by the estimator of a misspecified model. (3) The test can be used to evaluate in as well as out-of sample performance. The Monte Carlo experiment results indicate that the test works well and has desirable finite-sample properties. The test is illustrated with applications to two data sets: (1) the retention decisions of U.S. Navy officers, and (2) labor market participation by Swiss married women.
Download or read book The Econometrics of Complex Survey Data written by Kim P. Huynh and published by Emerald Group Publishing. This book was released on 2019-04-10 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume of Advances in Econometrics contains a selection of papers presented at the 'Econometrics of Complex Survey Data: Theory and Applications' conference organized by the Bank of Canada, Ottawa, Canada, from October 19-20, 2017.
Download or read book Conditional Tests for Multivariate One sided Hypotheses with Missing Data written by Madhurima Majumder and published by . This book was released on 2018 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Treatment comparisons in randomized clinical trials usually involve several outcomes. Sometimes it is of interest to determine whether there is a treatment-associated improvement in disease status based on multiple outcomes, particularly if a treatment is expected to have the same directional effect on all of the outcomes. This gives rise to a multivariate onesided hypothesis. Under the multivariate normality assumption, Perlman (1969) derived the likelihood-ratio test in the one-sample case; however, its null distribution depends on the unknown covariance matrix and it is biased. Wang and McDermott (1998) derived a conditional likelihood ratio test (CLRT), conditioning on a sufficient statistic for the covariance matrix, resulting in a uniformly more powerful test. Recently, in an unpublished manuscript, Wang extended the CLRT to the two-sample case. This thesis explores the operating characteristics of the two-sample CLRT. In addition, this thesis develops practical extensions of the test such as covariate adjustment, a two-sided version, and outcome specific inference. Since the problem of missing data is ubiquitous in practical applications involving repeated measurements in multiple outcomes, this thesis proposes an observed likelihood-based approach to incorporate such missing data with a missing at random (MAR) mechanism in the CLRT. This thesis also considers the case of comparison of multiple treatments with respect to multiple outcomes and suggests a conditional test based on a statistic suggested by Sasabuchi (2003) for comparing multiple outcomes in K (> 2) samples. Derivation of the conditional test is provided and a resampling method to calculate the p-value is illustrated based on Markov Chain Monte Carlo sampling.
Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Download or read book Categorical Data Analysis written by Alan Agresti and published by John Wiley & Sons. This book was released on 2013-04-08 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
Download or read book Misspecification Tests in Econometrics written by L. G. Godfrey and published by Cambridge University Press. This book was released on 1988 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Misspecification tests play an important role in detecting unreliable and inadequate economic models. This book brings together many results from the growing literature in econometrics on misspecification testing. It provides theoretical analyses and convenient methods for application. The main emphasis is on the Lagrange multiplier principle, which provides considerable unification, although several other approaches are also considered. The author also examines general checks for model adequacy that do not involve formulation of an alternative hypothesis. General and specific tests are discussed in the context of multiple regression models, systems of simultaneous equations, and models with qualitative or limited dependent variables.
