LEADER 04632oam 22012254 450 001 9910788222603321 005 20230721045725.0 010 $a1-4623-6282-6 010 $a1-4518-7416-2 010 $a9786612844584 010 $a1-282-84458-X 010 $a1-4527-8537-6 035 $a(CKB)3170000000055392 035 $a(SSID)ssj0001476961 035 $a(PQKBManifestationID)11818865 035 $a(PQKBTitleCode)TC0001476961 035 $a(PQKBWorkID)11449474 035 $a(PQKB)10325648 035 $a(OCoLC)680613480 035 $a(MiAaPQ)EBC1605983 035 $a(IMF)WPIEE2009271 035 $a(EXLCZ)993170000000055392 100 $a20020129d2009 uf 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe Hedonic Country Product Dummy Method and Quality Adjustments for Purchasing Power Parity Calculations /$fMick Silver 210 1$aWashington, D.C. :$cInternational Monetary Fund,$d2009. 215 $a28 p 225 1 $aIMF Working Papers 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4519-1832-1 320 $aIncludes bibliographical references. 330 3 $aThe 2005 International Comparison Program's (ICP) estimates of economy-wide purchasing power parity (PPP) are based on parity estimates for 155 basic expenditure headings, mainly estimated using country product dummy (CPD) regressions. The estimates are potentially inefficient and open to omitted variable bias for two reasons. First, they use average prices across outlets as the left-hand-side variable. Second, quality-adjusted prices of non-comparable replacements, required when products in outlets do not match the required specifications, cannot be effectively included. This paper provides an analytical framework based on panel data and hedonic CPD regressions for ameliorating these sources of bias and inefficiency. 410 0$aIMF Working Papers; Working Paper ;$vNo. 2009/271 606 $aPurchasing power parity 606 $aPurchasing power 606 $aInvestments: Metals$2imf 606 $aForeign Exchange$2imf 606 $aMacroeconomics$2imf 606 $aPublic Finance$2imf 606 $aNational Government Expenditures and Related Policies: Infrastructures$2imf 606 $aOther Public Investment and Capital Stock$2imf 606 $aLabor Economics: General$2imf 606 $aMetals and Metal Products$2imf 606 $aCement$2imf 606 $aGlass$2imf 606 $aCeramics$2imf 606 $aPrice Level$2imf 606 $aInflation$2imf 606 $aDeflation$2imf 606 $aCurrency$2imf 606 $aForeign exchange$2imf 606 $aPublic finance & taxation$2imf 606 $aLabour$2imf 606 $aincome economics$2imf 606 $aInvestment & securities$2imf 606 $aPurchasing power parity$2imf 606 $aPublic investment and public-private partnerships (PPP)$2imf 606 $aLabor$2imf 606 $aSilver$2imf 606 $aPrice adjustments$2imf 606 $aPublic-private sector cooperation$2imf 606 $aLabor economics$2imf 606 $aPrices$2imf 607 $aUnited States$2imf 615 0$aPurchasing power parity. 615 0$aPurchasing power. 615 7$aInvestments: Metals 615 7$aForeign Exchange 615 7$aMacroeconomics 615 7$aPublic Finance 615 7$aNational Government Expenditures and Related Policies: Infrastructures 615 7$aOther Public Investment and Capital Stock 615 7$aLabor Economics: General 615 7$aMetals and Metal Products 615 7$aCement 615 7$aGlass 615 7$aCeramics 615 7$aPrice Level 615 7$aInflation 615 7$aDeflation 615 7$aCurrency 615 7$aForeign exchange 615 7$aPublic finance & taxation 615 7$aLabour 615 7$aincome economics 615 7$aInvestment & securities 615 7$aPurchasing power parity 615 7$aPublic investment and public-private partnerships (PPP) 615 7$aLabor 615 7$aSilver 615 7$aPrice adjustments 615 7$aPublic-private sector cooperation 615 7$aLabor economics 615 7$aPrices 700 $aSilver$b Mick$01449453 712 02$aInternational Monetary Fund. 801 0$bDcWaIMF 906 $aBOOK 912 $a9910788222603321 996 $aThe Hedonic Country Product Dummy Method and Quality Adjustments for Purchasing Power Parity Calculations$93741530 997 $aUNINA LEADER 06855nam 22006735 450 001 9910298168603321 005 20251113183830.0 010 $a1-4899-8068-7 024 7 $a10.1007/978-1-4899-8068-7 035 $a(CKB)3710000000089073 035 $a(EBL)1782004 035 $a(SSID)ssj0001186018 035 $a(PQKBManifestationID)11645067 035 $a(PQKBTitleCode)TC0001186018 035 $a(PQKBWorkID)11217673 035 $a(PQKB)10404790 035 $a(MiAaPQ)EBC1782004 035 $a(DE-He213)978-1-4899-8068-7 035 $a(PPN)176749853 035 $a(EXLCZ)993710000000089073 100 $a20140225d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData Envelopment Analysis $eA Handbook of Modeling Internal Structure and Network /$fedited by Wade D. Cook, Joe Zhu 205 $a1st ed. 2014. 210 1$aNew York, NY :$cSpringer US :$cImprint: Springer,$d2014. 215 $a1 online resource (601 p.) 225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v208 300 $aDescription based upon print version of record. 311 08$a1-322-13266-6 311 08$a1-4899-8067-9 320 $aIncludes bibliographical references and index. 327 $aDEA for Two-State Networks: Efficiency Decompositions and Modeling Techniques -- Network DEA Pitfalls: Divisional Efficiency and Frontier Projection -- Efficiency Decomposition in Network Data Envelopment Analysis -- Two-Stage Network Processes: DEA Frontier Identification -- Additive Efficiency Decomposition in Network DEA -- Scale Efficiency Measurement in Two-Stage Production Systems -- Decomposing Efficiency and Returns to Scale in Two-Stage Network Systems -- Evaluating Two-Stage Network Structures: Bargaining Game Approach -- Shared Resources and Efficiency Decomposition in Two-Stage Networks -- A Network DEA Model with Internal Dynamic Effect -- Slacks-Based Network DEA -- DEA Models for Extended Two-Stage Network Structures -- An Efficiency Measurement Framework for Multi-Stage Production Systems -- Network DEA II -- Network, Shared Flow and Multi-Level DEA Models: A Critical Review -- Multicomponent Efficiency Measurement in Banking -- Evaluating Power Plant Efficiency: Hierarchical Models -- Multicomponent Efficiency: Measurement and Core Business Identification in Multiplant Firms -- Two-Stage Network DEA with Bad Outputs -- Performance Measurement of Major League Baseball Teams Using Network DEA -- Production and Marketing Efficiencies of the U.S. Airline Industry: A Two-Stage Network DEA Approach -- Network Representations of Efficiency Analysis for Engineering Systems: Examples, Issues and Research Opportunities.    . 330 $aThis handbook serves as a complement to the Handbook on Data Envelopment Analysis (eds, W.W. Cooper, L.M. Seiford, and J, Zhu, 2011, Springer) in an effort to extend the frontier of DEA research. It provides a comprehensive source for the state-of-the art DEA modeling on internal structures and network DEA.   Chapter 1 provides a survey on two-stage network performance decomposition and modeling techniques. Chapter 2 discusses the pitfalls in network DEA modeling. Chapter 3 discusses efficiency decompositions in network DEA under three types of structures, namely series, parallel, and dynamic.  Chapter 4 studies the determination of the network DEA frontier. In chapter 5 additive efficiency decomposition in network DEA is discussed.  An approach in scale efficiency measurement in two-stage networks is presented in chapter 6. Chapter 7 further discusses the scale efficiency decomposition in two stage networks. Chapter 8 offers a bargaining game approach to modeling two-stage networks. Chapter 9 studies shared resources and efficiency decomposition in two-stage networks. Chapter 10 introduces an approach to computing the technical efficiency scores for a dynamic production network and its sub-processes.  Chapter 11 presents a slacks-based network DEA. Chapter 12 discusses a DEA modeling technique for a two-stage network process where the inputs of the second stage include both the outputs from the first stage and additional inputs to the second stage.  Chapter 13 presents an efficiency measurement methodology  for multi-stage production systems. Chapter 14 discusses network DEA models, both static and dynamic. The discussion also explores various useful objective functions that can be applied to the models to find the optimal allocation of resources for processes within the black box, that are normally invisible to DEA. Chapter 15 provides a comprehensive review of various type network DEA modeling techniques. Chapter 16presents shared resources models for deriving aggregate measures of bank-branch performance, with accompanying component measures that make up that aggregate value. Chapter 17 examines a set of manufacturing plants operating under a single umbrella, with the objective being to use the component or function measures to decide what might be considered as each plant?s core business.  Chapter 18 considers problem settings where there may be clusters or groups of DMUs that form a hierarchy. The specific case of a set off electric power plants is examined in this context.  Chapter 19 models bad outputs in two-stage network DEA. Chapter 20 presents an application of network DEA to performance measurement of Major League Baseball (MLB) teams. Chapter 21 presents an application of a two-stage network DEA model for examining the performance of 30 U.S. airline companies. Chapter 22 then presents two distinct network efficiency models that are applied to engineering systems.                                           . 410 0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v208 606 $aOperations research 606 $aManagement science 606 $aIndustrial engineering 606 $aProduction engineering 606 $aOperations Research and Decision Theory 606 $aOperations Research, Management Science 606 $aIndustrial and Production Engineering 615 0$aOperations research. 615 0$aManagement science. 615 0$aIndustrial engineering. 615 0$aProduction engineering. 615 14$aOperations Research and Decision Theory. 615 24$aOperations Research, Management Science. 615 24$aIndustrial and Production Engineering. 676 $a658.5036 702 $aCook$b Wade D$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhu$b Joe$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910298168603321 996 $aData envelopment analysis$9394439 997 $aUNINA