05841oam 22013214 450 991097028220332120250426110804.09781475575972147557597197814755234921475523491(CKB)2670000000278808(EBL)1606844(SSID)ssj0000939870(PQKBManifestationID)11600600(PQKBTitleCode)TC0000939870(PQKBWorkID)10948415(PQKB)10833007(Au-PeEL)EBL1606844(CaPaEBR)ebr10627029(OCoLC)870245017(IMF)WPIEE2012182(IMF)WPIEA2012182(MiAaPQ)EBC1606844WPIEA2012182(EXLCZ)99267000000027880820020129d2012 uf 0engur|n|---|||||txtccrQuality of Government and Living Standards : Adjusting for the Efficiency of Public Spending /Francesco Grigoli, Eduardo Ley1st ed.Washington, D.C. :International Monetary Fund,2012.1 online resource (22 p.)IMF Working PapersDescription based upon print version of record.9781475514308 1475514301 9781475505306 1475505302 Includes bibliographical references.Cover; Abstract; Contents; I. Introduction; II. Measuring Living Standards; III. Corrected GDP; Tables; 1. GDP Losses Associated with Wasted Public Resources; Figures; 1. GDP Loss Due to Health and Education Waste vs. Per Capita GDP; 2. GDP Loss Due to Health Waste vs. Per Capita GDP; 3. Technical Efficiency Scores, per Capita GDP, and Total Loss; 4. Technical Efficiency Scores, WGI's Government Effectiveness, GDP Loss Due to Health Waste, and Per Capita GDP; IV. Concluding Remarks; ReferencesIt is generally acknowledged that the government’s output is difficult to define and its value is hard to measure. The practical solution, adopted by national accounts systems, is to equate output to input costs. However, several studies estimate significant inefficiencies in government activities (i.e., same output could be achieved with less inputs), implying that inputs are not a good approximation for outputs. If taken seriously, the next logical step is to purge from GDP the fraction of government inputs that is wasted. As differences in the quality of the public sector have a direct impact on citizens’ effective consumption of public and private goods and services, we must take them into account when computing a measure of living standards. We illustrate such a correction computing corrected per capita GDPs on the basis of two studies that estimate efficiency scores for several dimensions of government activities. We show that the correction could be significant, and rankings of living standards could be re-ordered as a result.IMF Working Papers; Working Paper ;No. 2012/182Cost and standard of livingEconomic developmentCivil service & public sectorimfEconomic sectorsimfEducationimfEducation: GeneralimfEnvironmental AccountsimfExpenditureimfExpenditures, PublicimfFinance, PublicimfGeneral Aggregative Models: GeneralimfHealth care spendingimfHealth economicsimfHealthimfHealth: GeneralimfMacroeconomicsimfMeasurement and Data on National Income and Product Accounts and WealthimfNational accountsimfNational Government Expenditures and HealthimfNational incomeimfPublic AdministrationimfPublic EnterprisesimfPublic finance & taxationimfPublic FinanceimfPublic Sector Accounting and AuditsimfPublic sectorimfPublic-Private EnterprisesimfPublicly Provided Goods: GeneralimfSocial Security and Public PensionsimfCyprusimfCost and standard of living.Economic development.Civil service & public sectorEconomic sectorsEducationEducation: GeneralEnvironmental AccountsExpenditureExpenditures, PublicFinance, PublicGeneral Aggregative Models: GeneralHealth care spendingHealth economicsHealthHealth: GeneralMacroeconomicsMeasurement and Data on National Income and Product Accounts and WealthNational accountsNational Government Expenditures and HealthNational incomePublic AdministrationPublic EnterprisesPublic finance & taxationPublic FinancePublic Sector Accounting and AuditsPublic sectorPublic-Private EnterprisesPublicly Provided Goods: GeneralSocial Security and Public Pensions332.1/52Grigoli Francesco1127643Ley Eduardo485587DcWaIMFBOOK9910970282203321Quality of Government and Living Standards4372498UNINA09224oam 22005173 450 991097500030332120251116135230.09781119307914(electronic bk.)9781119307860(MiAaPQ)EBC4722461(Au-PeEL)EBL4722461(CaPaEBR)ebr11286588(CaONFJC)MIL965366(OCoLC)959667473(MiAaPQ)EBC7104511(CKB)17690336100041(BIP)56069974(BIP)55318988(EXLCZ)991769033610004120220831d2016 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCategorical Data Analysis by Example1st ed.Newark :John Wiley & Sons, Incorporated,2016.©2017.1 online resource (215 pages)Print version: Upton, Graham J. G. Categorical Data Analysis by Example Newark : John Wiley & Sons, Incorporated,c2016 9781119307860 Intro -- CATEGORICAL DATA ANALYSIS BY EXAMPLE -- Contents -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 What are Categorical Data? -- 1.2 A Typical Data Set -- 1.3 Visualization and Cross-Tabulation -- 1.4 Samples, Populations, and Random Variation -- 1.5 Proportion, Probability, and Conditional Probability -- 1.6 Probability Distributions -- 1.6.1 The Binomial Distribution -- 1.6.2 The Multinomial Distribution -- 1.6.3 The Poisson Distribution -- 1.6.4 The Normal Distribution -- 1.6.5 The Chi-Squared ( 2) Distribution -- 1.7 *The Likelihood -- 2 Estimation and Inference for Categorical Data -- 2.1 Goodness of Fit -- 2.1.1 Pearson's X2 Goodness-of-Fit Statistic -- 2.1.2 *The Link between X2 and the Poisson and 2-Distributions -- 2.1.3 The Likelihood-Ratio Goodness-of-Fit Statistic, G2 -- 2.1.4 *Why the G2 and X2 Statistics Usually have Similar Values -- 2.2 Hypothesis Tests for a Binomial Proportion (Large Sample) -- 2.2.1 The Normal Score Test -- 2.2.2 *Link to Pearson's X2 Goodness-of-Fit Test -- 2.2.3 G2 for a Binomial Proportion -- 2.3 Hypothesis Tests for a Binomial Proportion (Small Sample) -- 2.3.1 One-Tailed Hypothesis Test -- 2.3.2 Two-Tailed Hypothesis Tests -- 2.4 Interval Estimates for a Binomial Proportion -- 2.4.1 Laplace's Method -- 2.4.2 Wilson's Method -- 2.4.3 The Agresti-Coull Method -- 2.4.4 Small Samples and Exact Calculations -- References -- 3 The 2 × 2 Contingency Table -- 3.1 Introduction -- 3.2 Fisher's Exact Test (for Independence) -- 3.2.1 *Derivation of the Exact Test Formula -- 3.3 Testing Independence with Large Cell Frequencies -- 3.3.1 Using Pearson's Goodness-of-Fit Test -- 3.3.2 The Yates Correction -- 3.4 The 2 × 2 Table in a Medical Context -- 3.5 Measuring Lack of Independence (Comparing Proportions) -- 3.5.1 Difference of Proportions -- 3.5.2 Relative Risk -- 3.5.3 Odds-Ratio -- References.4 The I × J Contingency Table -- 4.1 Notation -- 4.2 Independence in the I × J Contingency Table -- 4.2.1 Estimation and Degrees of Freedom -- 4.2.2 Odds-Ratios and Independence -- 4.2.3 Goodness of Fit and Lack of Fit of the Independence Model -- 4.3 Partitioning -- 4.3.1 *Additivity of G2 -- 4.3.2 Rules for Partitioning -- 4.4 Graphical Displays -- 4.4.1 Mosaic Plots -- 4.4.2 Cobweb Diagrams -- 4.5 Testing Independence with Ordinal Variables -- References -- 5 The Exponential Family -- 5.1 Introduction -- 5.2 The Exponential Family -- 5.2.1 The Exponential Dispersion Family -- 5.3 Components of a General Linear Model -- 5.4 Estimation -- References -- 6 A Model Taxonomy -- 6.1 Underlying Questions -- 6.1.1 Which Variables are of Interest? -- 6.1.2 What Categories should be Used? -- 6.1.3 What is the Type of Each Variable? -- 6.1.4 What is the Nature of Each Variable? -- 6.2 Identifying the Type of Model -- 7 The 2 × J Contingency Table -- 7.1 A Problem with X2 (and G2) -- 7.2 Using the Logit -- 7.2.1 Estimation of the Logit -- 7.2.2 The Null Model -- 7.3 Individual Data and Grouped Data -- 7.4 Precision, Confidence Intervals, and Prediction Intervals -- 7.4.1 Prediction Intervals -- 7.5 Logistic Regression with a Categorical Explanatory Variable -- 7.5.1 Parameter Estimates with Categorical Variables (J &gt -- 2) -- 7.5.2 The Dummy Variable Representation of a Categorical Variable -- References -- 8 Logistic Regression with Several Explanatory Variables -- 8.1 Degrees of Freedom when there are no Interactions -- 8.2 Getting a Feel for the Data -- 8.3 Models with two-Variable Interactions -- 8.3.1 Link to the Testing of Independence between Two Variables -- 9 Model Selection and Diagnostics -- 9.1 Introduction -- 9.1.1 Ockham's Razor -- 9.2 Notation for Interactions and for Models -- 9.3 Stepwise Methods for Model Selection Using G2.9.3.1 Forward Selection -- 9.3.2 Backward Elimination -- 9.3.3 Complete Stepwise -- 9.4 AIC and Related Measures -- 9.5 The Problem Caused by Rare Combinations of Events -- 9.5.1 Tackling the Problem -- 9.6 Simplicity Versus Accuracy -- 9.7 DFBETAS -- References -- 10 Multinomial Logistic Regression -- 10.1 A Single Continuous Explanatory Variable -- 10.2 Nominal Categorical Explanatory Variables -- 10.3 Models for an Ordinal Response Variable -- 10.3.1 Cumulative Logits -- 10.3.2 Proportional Odds Models -- 10.3.3 Adjacent-Category Logit Models -- 10.3.4 Continuation-Ratio Logit Models -- References -- 11 Log-Linear Models for I × J Tables -- 11.1 The Saturated Model -- 11.1.1 Cornered Constraints -- 11.1.2 Centered Constraints -- 11.2 The Independence Model for an I × J Table -- 12 Log-Linear Models for I × J × K Tables -- 12.1 Mutual Independence: A∕B∕C -- 12.2 The Model AB∕C -- 12.3 Conditional Independence and Independence -- 12.4 The Model AB∕AC -- 12.5 The Models AB∕AC∕BC and ABC -- 12.6 Simpson's Paradox -- 12.7 Connection between Log-Linear Models and Logistic Regression -- Reference -- 13 Implications and Uses of Birch's Result -- 13.1 Birch's Result -- 13.2 Iterative Scaling -- 13.3 The Hierarchy Constraint -- 13.4 Inclusion of the All-Factor Interaction -- 13.5 Mostellerizing -- References -- 14 Model Selection for Log-Linear Models -- 14.1 Three Variables -- 14.2 More than Three Variables -- Reference -- 15 Incomplete Tables, Dummy Variables, and Outliers -- 15.1 Incomplete Tables -- 15.1.1 Degrees of Freedom -- 15.2 Quasi-independence -- 15.3 Dummy Variables -- 15.4 Detection of Outliers -- 16 Panel Data and Repeated Measures -- 16.1 The Mover-Stayer Model -- 16.2 The Loyalty Model -- 16.3 Symmetry -- 16.4 Quasi-Symmetry -- 16.5 The Loyalty-Distance Model -- References -- Appendix R Code for Cobweb Function -- Index -- Author Index.Index of Examples -- EULA.Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject. Each new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book: Uses an example to illustrate each new topic in categorical data Provides a clear explanation of an important subject Is understandable to most readers with minimal statistical and mathematical backgrounds Contains examples that are accompanied by R code and resulting output Includes starred sections that provide more background details for interested readers Categorical Data Analysis by Example is a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields. GRAHAM J. G. UPTON is formerly Professor of Applied Statistics, Department of Mathematical Sciences, University of Essex. Dr. Upton is author of The Analysis of Cross-tabulated Data (1978) and joint author of Spatial Data Analysis by Example (2 volumes, 1995), both published by Wiley. He is the lead author of The Oxford Dictionary of Statistics (OUP, 2014). His books have been translated into Japanese, Russian, and Welsh. "Multivariate analysisMultivariate analysis.519.535Upton Graham J. G103103MiAaPQMiAaPQMiAaPQ9910975000303321Categorical Data Analysis by Example4473090UNINA