LEADER 04591nam 2200649 450 001 9910826920703321 005 20230125205406.0 010 $a1-60649-975-0 035 $a(CKB)3710000000120246 035 $a(OCoLC)878077804 035 $a(CaPaEBR)ebrary10861550 035 $a(CaBNVSL)swl00403322 035 $a(Au-PeEL)EBL1675705 035 $a(CaPaEBR)ebr10861550 035 $a(CaONFJC)MIL825459 035 $a(OCoLC)878852551 035 $a(CaSebORM)9781606499740 035 $a(MiAaPQ)EBC1675705 035 $a(EXLCZ)993710000000120246 100 $a20140423d2014 fy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aBuilding better econometric models using cross section and panel data /$fJeffrey A. Edwards 205 $aFirst edition. 210 1$aNew York, New York (222 East 46th Street, New York, NY 10017) :$cBusiness Expert Press,$d2014. 215 $a1 online resource (116 p.) 225 1 $aEconomics collection,$x2163-7628 300 $aPart of: 2014 digital library. 311 $a1-60649-974-2 320 $aIncludes bibliographical references (pages 95-96) and index. 327 $a1. What is a statistically adequate model and why is it important? -- 2. Basic misspecifications -- 3. Misspecifications for the more advanced reader -- 4. Original specification and drawing inference from it: two related models -- 5. Basic misspecification testing and respecification: the cross-sectional case -- 6. Variance heterogeneity: the cross-sectional case -- 7. Basic misspecification testing and respecification: the panel data case -- 8. Variance heterogeneity: the panel data case -- 9. Consistent and balanced panels -- 10. Dynamic parametric heterogeneity -- Conclusion -- References -- Index. 330 3 $aMany empirical researchers yearn for an econometric model that better explains their data. Yet these researchers rarely pursue this objective for fear of the statistical complexities involved in specifying that model. This book is intended to alleviate those anxieties by providing a practical methodology that anyone familiar with regression analysis can employ--a methodology that will yield a model that is both more informative and is a better representation of the data. Most empirical researchers have been taught in their undergraduate econometrics courses about statistical misspecification testing and respecification. But the impact these techniques can have on the inference that is drawn from their results is often overlooked. In academia, students are typically expected to explore their research hypotheses within the context of theoretical model specification while ignoring the underlying statistics. Company executives and managers, by contrast, seek results that are immediately comprehensible and applicable, while remaining indifferent to the underlying properties and econometric calculations that lead to these results. This book outlines simple, practical procedures that can be used to specify a better model; that is to say, a model that better explains the data. Such procedures employ the use of purely statistical techniques performed upon a publicly available data set, which allows readers to follow along at every stage of the procedure. Using the econometric software Stata (though most other statistical software packages can be used as well), this book shows how to test for model misspecification, and how to respecify these models in a practical way that not only enhances the inference drawn from the results, but adds a level of robustness that can increase the confidence a researcher has in the output that has been generated. By following this procedure, researchers will be led to a better, more finely tuned empirical model that yields better results. 410 0$a2014 digital library. 410 0$aEconomics collection.$x2163-7628 606 $aEconometric models 610 $across-sectional data 610 $ainference 610 $amisspecification testing 610 $apanel data 610 $aregression 610 $aregression models 610 $arespecification 610 $aStata 610 $astatistical adequacy 615 0$aEconometric models. 676 $a330.015195 700 $aEdwards$b Jeffrey A.$01651573 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910826920703321 996 $aBuilding better econometric models using cross section and panel data$94002045 997 $aUNINA