LEADER 00889nam0-2200313---450 001 990009208570403321 005 20200424124426.0 010 $a9788813283131 035 $a000920857 035 $aFED01000920857 100 $a20100719d2008----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $ay-------001yy 200 1 $aCorso di diritto internazionale privato$fTito Ballarino, Davide Milan 205 $a3. ed 210 $aPadova$cCEDAM$d2008 215 $aXII, 293 p.$d24 cm 225 1 $aStrumenti per la formazione professionale$iCorsi 676 $a340.9$v11 rid.$zita 700 1$aBallarino,$bTito$0130600 701 1$aMilan,$bDavide$0508415 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990009208570403321 952 $aC 116$b6254$fDSI 959 $aDSI 996 $aCorso di diritto internazionale privato$9777552 997 $aUNINA LEADER 05393nam 2200673 450 001 9910138972803321 005 20200520144314.0 010 $a1-118-71556-X 010 $a1-118-71554-3 010 $a1-118-71557-8 035 $a(CKB)2550000001175424 035 $a(EBL)1583674 035 $a(SSID)ssj0001082088 035 $a(PQKBManifestationID)11587122 035 $a(PQKBTitleCode)TC0001082088 035 $a(PQKBWorkID)11097130 035 $a(PQKB)11315932 035 $a(OCoLC)852488734 035 $a(MiAaPQ)EBC1583674 035 $a(DLC) 2013027674 035 $a(Au-PeEL)EBL1583674 035 $a(CaPaEBR)ebr10822340 035 $a(CaONFJC)MIL556710 035 $a(OCoLC)866839370 035 $a(PPN)19179841X 035 $a(EXLCZ)992550000001175424 100 $a20130708d2014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPanel data analysis using eviews /$fI. Gusti Ngurah Agung 210 1$aHoboken :$cWiley,$d2014. 215 $a1 online resource (541 p.) 300 $aDescription based upon print version of record. 311 $a1-118-71558-6 311 $a1-306-25459-0 320 $aIncludes bibliographical references and index. 327 $aPanel Data Analysis Using EViews; Contents; Preface; About the Author; Part One: Panel Data as a Multivariate Time Series by States; 1 Data Analysis Based on a Single Time Series by States; 1.1 Introduction; 1.2 Multivariate Growth Models; 1.2.1 Continuous Growth Models; 1.2.2 Discontinuous Growth Models; 1.3 Alternative Multivariate Growth Models; 1.3.1 A Generalization of MAR(p)_GM; 1.3.2 Multivariate Lagged Variables Growth Models; 1.3.3 Multivariate Lagged-Variable Autoregressive Growth Models; 1.3.4 Bounded MLVAR(p; q)_GM; 1.3.5 Special Notes 327 $a1.4 Various Models Based on Correlated States1.4.1 Seemingly Causal Models with Trend; 1.4.2 The Application of the Object "VAR"; 1.4.3 The Application of the Instrumental Variables Models; 1.5 Seemingly Causal Models with Time-Related Effects; 1.5.1 SCM Based on the Path Diagram in Figure 1.10(a); 1.5.2 SCM Based on the Path Diagram in Figure 1.10(b); 1.6 The Application of the Object POOL; 1.6.1 What is a Fixed-Effect Model?; 1.6.2 What is a Random Effect Model?; 1.6.3 Special Notes; 1.7 Growth Models of Sample Statistics; 1.8 Special Notes on Time-State Observations 327 $a1.9 Growth Models with an Environmental Variable1.9.1 The Simplest Possible Model; 1.9.2 The Application of VAR and VEC Models; 1.9.3 Application of ARCH Model; 1.9.4 The Application of Instrumental Variables Models; 1.10 Models with an Environmental Multivariate; 1.10.1 Bivariate Correlation and Simple Linear Regressions; 1.10.2 Simple Models with an Environmental Multivariate; 1.10.3 The VAR Models; 1.11 Special Piece-Wise Models; 1.11.1 The Application of Growth Models; 1.11.2 Equality Tests by Classifications; 2 Data Analysis Based on Bivariate Time Series by States; 2.1 Introduction 327 $a2.2 Models Based on Independent States2.2.1 MAR(p) Growth Model with an Exogenous Variable; 2.2.2 A General MAR(p) Model with an Exogenous Variable; 2.3 Time-Series Models Based on Two Correlated States; 2.3.1 Analysis using the Object System; 2.3.2 Two-SLS Instrumental Variables Models; 2.3.3 Three-SLS Instrumental Variables Models; 2.3.4 Analysis using the Object "VAR"; 2.4 Time-Series Models Based on Multiple Correlated States; 2.4.1 Extension of the Path Diagram in Figure 2.6; 2.4.2 SCMs as VAR Models; 2.5 Time-Series Models with an Environmental Variable Zt, Based on Independent States 327 $a2.5.1 The Simplest Possible Model2.5.2 Interaction Models Based on Two Independent States; 2.6 Models Based on Correlated States; 2.6.1 MLV(1) Interaction Model with Trend; 2.6.2 Simultaneous SCMs with Trend; 2.7 Piece-Wise Time-Series Models; 3 Data Analysis Based on Multivariate Time Series by States; 3.1 Introduction; 3.2 Models Based on (X_i,Y_i,Z_i) for Independent States; 3.2.1 MLVAR(p; q) Model with Trend Based on (X_i,Y_i,Z_i); 3.3 Models Based on (X_i, Y_i,Z_i) for Correlated States; 3.3.1 MLV(1) Interaction Model with Trend; 3.3.2 MLV(1) Interaction Model with Time-Related Effects 327 $a3.4 Simultaneous SCMs with Trend 330 $a"Panel Data Analysis using EViews provides graduate students, researchers, and statisticians with step-by-step guidance on how to apply EViews software to panel data analysis using appropriate empirical models and real datasets. The author explores a variety of panel data models, along with the authors own empirical findings, demonstrating the advantages and limitations of each model. The text also examines various alternative models based on panel data, as well as the best and worst ANCOVA models"--$cProvided by publisher. 330 $a"This book explores the use of EViews software in creating panel data analysis using appropriate empirical models and real datasets"--$cProvided by publisher. 606 $aStatistics 615 0$aStatistics. 676 $a005.5/5 686 $aMAT029000$2bisacsh 700 $aAgung$b I Gusti Ngurah$0614603 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910138972803321 996 $aPanel data analysis using eviews$91969134 997 $aUNINA LEADER 00839nam a22002291i 4500 001 991003506329707536 005 20030829105322.0 008 031111s1976 uika||||||||||||||||eng 020 $a0582519934 035 $ab12435934-39ule_inst 035 $aARCHE-046922$9ExL 040 $aDip.to Lingue$bita$cA.t.i. Arché s.c.r.l. 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