Bootstrap Tests For Regression Models

Modern Computer Systems Are Now So Powerful That They Can Be Used To Carry Out Simulation-based Statistical Investigations Without Involving Delays Or The Need To Access High Levels Of Equipment. When Carrying Out Econometric Analyses, The Routine Use Of Computer-based Methods Offers A Valuable Alternative To The Standard Approach In Which Approximations Are Based Upon What Happens As The Sample Size Grows Without Limit. Applied Work Has To Be Based Upon A Finite Number Of Observations. Computationally-intensive Techniques And, In Particular, Bootstrap Methods Provide Ways To Improve The Finite-sample Performance Of Well-known Tests. Bootstrap Tests Can Also Be Employed When Conventional Theory Does Not Lead To A Test Statistic, Which Can Be Compared With Critical Values From Some Standard Distribution. This Book Uses The Familiar Linear Regression Model As A Framework For Introducing Simulation-based Tests To Applied Workers, Students And Others Who Carry Out Empirical Econometric Analyses. Tests For Linear Regression Models -- Simulation-based Tests : Basic Ideas -- Simulation-based Tests For Regression Models With Iid Errors : Some Standard Cases -- Simulation-based Tests For Regression Models With Iid Errors : Some Non-standard Cases -- Bootstrap Methods For Regression Models With Non-iid Errors -- Simulation-based Tests For Regression Models With Non-iid Errors -- Simulation-based Tests For Non-nested Regression Models. Leslie Godfrey. Includes Bibliographical References (pages 305-317) And Indexes. Master And Use Copy. Digital Master Created According To Benchmark For Faithful Digital Reproductions Of Monographs And Serials, Version 1. Digital Library Federation, December 2002. Http://purl.oclc.org/dlf/benchrepro0212 Miaahdl