A Test for Comparing Multiple Misspecified Conditional Distributions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Test for Comparing Multiple Misspecified Conditional Distributions write by Norman R. Swanson. This book was released on 2003. A Test for Comparing Multiple Misspecified Conditional Distributions available in PDF, EPUB and Kindle. 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.
Handbook of Economic Forecasting
Handbook of Economic Forecasting - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Handbook of Economic Forecasting write by Graham Elliott. This book was released on 2013-08-23. Handbook of Economic Forecasting available in PDF, EPUB and Kindle. 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
Misspecification Testing in a Class of Conditional Distributional Models
Misspecification Testing in a Class of Conditional Distributional Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Misspecification Testing in a Class of Conditional Distributional Models write by Christoph Rothe. This book was released on 2011. Misspecification Testing in a Class of Conditional Distributional Models available in PDF, EPUB and Kindle.
Comparing Conditional Distributions Under Measurement Errors of Known Variances
Comparing Conditional Distributions Under Measurement Errors of Known Variances - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Comparing Conditional Distributions Under Measurement Errors of Known Variances write by Stanford University. Department of Statistics. This book was released on 1968. Comparing Conditional Distributions Under Measurement Errors of Known Variances available in PDF, EPUB and Kindle.
Flexible Imputation of Missing Data, Second Edition
Flexible Imputation of Missing Data, Second Edition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Flexible Imputation of Missing Data, Second Edition write by Stef van Buuren. This book was released on 2018-07-17. Flexible Imputation of Missing Data, Second Edition available in PDF, EPUB and Kindle. 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.
