Econometric analysis of panel data:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
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Cham, Switzerland
Springer
[2021]
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| Ausgabe: | Sixth edition |
| Schriftenreihe: | Springer texts in business and economics
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | xx, 424 Seiten 25 cm |
| ISBN: | 9783030539528 |
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Datensatz im Suchindex
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| adam_text | Contents 1 Introduction................................................................................................ 1.1 Panel Data: Some Examples........................................................ 1.1.1 Examples of Micro-panels............................................... 1.1.2 Examples of Macro-panels............................................. 1.1.3 Some Basic References.................................................... 1.2 Why Should We Use Panel Data? Their Benefits and Limitations................................................................................ 1.3 Note.................................................................................................. References..................................................................................................... 1 1 2 4 5 6 12 12 2 The One-Way Error Component Regression Model.......................... 2.1 Introduction.................................................................................... 2.2 The One-Way Fixed Effects Model............................................. 2.3 The One-Way Random Effects Modef........................................ 2.4 Maximum Likelihood Estimation................................................. 2.5 Prediction......................................................................................... 2.6 Examples......................................................................................... 2.6.1 Example 1 : Investment Equation................................... 2.6.2 Example 2: Gasoline Demand Equation....................... 2.6.3 Example 3:
Public Capital Productivity........................ 2.7 Selected Applications.................................................................... 2.8 Computational Note...................................................................... 2.9 Notes................................................................................................ 2.10 Problems......................................................................................... References..................................................................................................... 15 15 16 24 28 29 31 31 33 35 38 39 39 40 43 3 The Two-Way Error Component Regression Model........................... 3.1 Introduction.................................................................................... 3.2 The Two-Way Fixed Effects Model............................................. 3.2.1 Testing for Fixed Effects................................................. 3.3 The Two-Way Random Effects Model........................................ 3.3.1 Monte Carlo Results........................................................ 3.4 Maximum Likelihood Estimation................................................. 47 47 48 49 50 55 56 XV
xvi 4 5 Contents 3.5 3.6 Prediction.......................................................................................... Examples.......................................................................................... 3.6.1 Example 1:Investment Equation.................................... 3.6.2 Example 2: Gasoline Demand Equation........................ 3.6.3 Example 3: Public Capital Productivity........................ 3.7 Computational Note....................................................................... 3.8 Notes................................................................................................. 3.9 Problems.......................................................................................... References...................................................................................................... 59 60 60 62 64 65 66 66 73 Test of Hypotheses with Panel Data...................................................... 4.1 Tests for Poolability....................................................................... 4.1.1 Test for Poolability Under u~V(0,u2Int) ................. 4.1.2 Test for Poolability Under the General Assumption u~N{ 0, Ω)....................................................................... 4.1.3 Examples............................................................................ 4.2 Tests for Individual and Time Elfects........................................... 4.2.1 The Breusch-Pagan Test.................................................. 4.2.2 Honda, King and Wu, and the Standardized Lagrange Multiplier
Tests............................................... 4.2.3 Gourieroux, Holly and Monfort Test............................ 4.2.4 Conditional LM Tests...................................................... 4.2.5 ANOVA F and theLikelihood Ratio Tests.................. 4.2.6 Monte Carlo Results........................................................ 4.2.7 An Illustrative Example................................................. 4.3 Hausman’s Specification Test...................................................... 4.3.1 Example 1: Investment Equation................................... 4.3.2 Example 2: Gasoline Demand Equation........................ 4.3.3 Example 3: Canadian ManufacturingIndustries........... 4.3.4 Example 4: Sawmills in WashingtonState..................... 4.3.5 Example 5: Mariage Premium........................................ 4.3.6 Example 6: Currency Union.......................................... 4.3.7 Hausman’s Test for the Two-Way Model..................... 4.4 Further Reading.............................................................................. 4.5 Notes................................................................................................ 4.6 Problems......................................................................................... References..................................................................................................... 75 75 76 Heteroskedasticity and Serial Correlation in the Error Component Model........................................................................... 5.1
Heteroskedasticity........................................................................... 5.1.1 Testing for Homoskedasticity in an Error Component Model.......................................................... 77 80 81 81 83 84 85 85 86 87 89 94 96 97 98 98 98 99 101 103 103 106 109 109 113
Contents xvii 5.2 Serial Correlation........................................................................... 5.2.1 The AR(1) Process.......................................................... 5.2.2 The AR(2) Process.......................................................... 5.2.3 The AR(4) Process for Quarterly Data.......................... 5.2.4 The MA(1) Process ........................................................ 5.2.5 Unequally Spaced Panels with AR(1) Disturbances...................................................................... 5.2.6 Prediction........................................................................... 5.2.7 Testing for Serial Correlation and Individual Effects............................................................................... 5.3 Time-Wise Autocorrelated and Cross-Sectionally Heteroskedastic Panel Regression................................................. 5.4 Further Reading............................................................................. 5.5 Notes................................................................................................ 5.6 Problems......................................................................................... References.................................................................................................... 115 115 117 118 119 6 Seemingly Unrelated Regressions withError Components................ 6.1 The One-Way Model...................................................................... 6.2 The Two-Way
Model.................................................................... 6.3 Applications and Extensions........................................................ 6.4 Problems......................................................................................... References.................................................................................................... 149 149 150 152 153 154 7 Simultaneous Equations withError Components................................ 7.1 Single Equation Estimation........................................................... 7.2 Empirical Example: Crime in North Carolina............................ 7.3 System Estimation........................................................................... 7.4 The Hausman and Taylor Estimator............................................ 7.5 Empirical Example: Earnings Equation Using PSID Data .... 7.6 Further Reading............................................................................. 7.7 Notes................................................................................................ 7.8 Problems......................................................................................... References..................................................................................................... 157 157 161 166 170 173 177 179 180 185 8 Dynamic Panel Data Models.................................................................... 8.1 Introduction.................................................................................... 8.2 The Arellano and Bond
Estimator............................................... 8.2.1 Testing for Over-Identification Restrictions and Serial Correlation in Dynamic Panel Models .... 8.2.2 Downward Bias of the Estimated Asymptotic Standard Errors............................................................... 8.2.3 Too Many Moment Conditions and theBias Efficiency Trade-Off........................................................ 187 187 189 120 121 124 136 139 142 142 145 191 193 194
xviii 9 10 Contents 8.3 8.4 8.5 8.6 8.7 8.8 The Arellano and Bover Estimator............................................... The Ahn and Schmidt Moment Conditions................................. The BlundeU and Bond System GMM Estimator..................... The Keane and Runkle Estimator.................................................. Limited Information Maximum Likelihood................................. Empirical Examples ....................................................................... 8.8.1 Example 1: Dynamic Demand forCigarettes................ 8.8.2 Example 2: Democracy and Education.......................... 8.9 Selected Applications..................................................................... 8.10 Further Reading.............................................................................. 8.11 Notes................................................................................................. 8.12 Problems.......................................................................................... References...................................................................................................... 194 198 201 203 205 207 207 210 213 216 220 221 225 Unbalanced Panel DataModels............................................................... 9.1 Introduction..................................................................................... 9.2 The Unbalanced One-Way Error Component Model................. 9.2.1 ANOVA Methods.............................................................. 9.3 Maximum Likelihood
Estimators.................................................. 9.3.1 Minimum Norm and Minimum Variance Quadratic UnbiasedEstimators (MINQUE and MIVQUE)........... 9.3.2 Monte Carlo Results......................................................... 9.4 Empirical Example: Hedonic Housing........................................ 9.5 The Unbalanced Two-Way Error ComponentModel.................. 9.5.1 The Fixed Effects Model.................................................. 9.5.2 The Random Elfects Model............................................. 9.6 Testing for Individual and Time Effects Using Unbalanced Panel Data....................................................................................... 9.7 The Unbalanced Nested Error ComponentModel...................... 9.7.1 Empirical Example: Nested States Public Capital Productivity....................................................................... 9.8 Notes................................................................................................. 9.9 Problems......................................................................................... References..................................................................................................... 229 229 229 232 234 235 235 236 240 240 241 243 246 247 251 252 255 Special Topics.............................................................................................. 259 10.1 Measurement Error and Panel Data............................................. 259 10.2 Rotating Panels................................................................................
263 10.3 Pseudo-Panels.................................................................................. 265 10.4 Short-Run Versus Long-Run Estimates inPooled Models .... 268 10.5 Heterogeneous Panels.................................................................... 269 10.6 Count Panel Data........................................................................... 276
Contents xix 10.7 Notes................................................................................................ 282 10.8 Problems......................................................................................... 283 References.................................................................................................... 287 11 12 Limited Dependent Variables and Panel Data................................... 11.1 Fixed and Random Logit and Probit Models.............................. 11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data............................................................... 11.3 Dynamic Panel Data Limited Dependent Variable Models.... 11.4 Selection Bias in Panel Data........................................................ 11.5 Censored and Truncated Panel Data Models.............................. 11.6 Empirical Applications................................................................. 11.7 Empirical Example: Nurses Labor Supply................................... 11.8 Further Reading............................................................................. 11.9 Notes................................................................................................ 11.10 Problems ......................................................................................... References..................................................................................................... 291 292 Nonstationary Panels............................................................................... 12.1
Introduction.................................................................................... 12.2 Panel Unit Roots Tests Assuming Cross-Sectional Independence .................................................................................. 12.2.1 Levin, Lin and Chu Test................................................. 12.2.2 Im, Pesaran and Shin Test............................................... 12.2.3 Breitung’s Test................................................................. 12.2.4 Combining p-Value Tests............................................... 12.2.5 Residual-Based LM Test................................................. 12.3 Panel Unit Roots Tests Allowing for Cross-Sectional Dependence.................................................................................... 12.4 Spurious Regression in Panel Data............................................... 12.5 Panel Cointegration Tests............................................................. 12.5.1 Residual-Based DF and ADF Tests (Kao Tests).......... 12.5.2 Residual-Based LM Test................................................. 12.5.3 Pedroni Tests.................................................................... 12.5.4 Likelihood-Based Cointegration Test............................ 12.5.5 Finite Sample Properties................................................. 12.6 Estimation and Inference in Panel Cointegration Models......... 12.7 Empirical Examples ...................................................................... 12.7.1 Example 1: Purchasing Power Parity............................
12.7.2 Example 2: International R D Spillover..................... 12.7.3 Example 3: OECD Health Care Expenditures.............. 12.8 Further Reading............................................................................. 337 337 301 303 309 314 319 321 324 326 327 332 339 340 344 345 346 348 350 354 357 357 358 360 361 362 364 368 368 371 372 374
xx 13 Contents 12.9 Notes................................................................................................. 12.10 Problems.......................................................................................... References...................................................................................................... 381 382 385 Spatial Panel Data Models....................................................................... 13.1 Introduction..................................................................................... 13.2 Spatial Error Component Regression Model............................... 13.3 Spatial Lag Panel Data Regression Model ................................. 13.4 Forecasts Using Panel Data with Spatial Error Correlation. . . . 13.5 Panel Unit Root Tests and Spatial Dependence.......................... 13.6 Panel Data Tests for Cross-Sectional Dependence..................... 13.7 Computational Note....................................................................... 13.8 Problems.......................................................................................... References..................................................................................................... 391 391 392 401 408 410 411 418 418 421
Springer Texts in Business and Economics Badi H. Baltagi Econometric Analysis of Panel Data Sixth Edition This textbook offers a comprehensive introduction to panel data econometrics, an area that has enjoyed considerable growth over the last two decades. Micro and Macro panels are becoming increasingly available, and methods for dealing with these types of data are in high demand among practitioners. Software programs have fostered this growth, including freely available programs in R and numerous user-written programs in both Stata and EViews. Written by one of the world’s leading researchers and authors in the field, Econometric Analysis of Panel Data has established itself as the leading textbook for graduate and postgraduate courses on panel data. It provides up-to-date coverage of basic panel data techniques, illustrated with real economic applications and datasets, which are available at the books website on springer.com. This new sixth edition has been fully revised and updated, and includes new material on dynamic panels, limited dependent variables and nonstationary panels, as well as spatial panel data. The author also provides empirical illustrations and examples using Stata and EViews.
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| adam_txt |
Contents 1 Introduction. 1.1 Panel Data: Some Examples. 1.1.1 Examples of Micro-panels. 1.1.2 Examples of Macro-panels. 1.1.3 Some Basic References. 1.2 Why Should We Use Panel Data? Their Benefits and Limitations. 1.3 Note. References. 1 1 2 4 5 6 12 12 2 The One-Way Error Component Regression Model. 2.1 Introduction. 2.2 The One-Way Fixed Effects Model. 2.3 The One-Way Random Effects Modef. 2.4 Maximum Likelihood Estimation. 2.5 Prediction. 2.6 Examples. 2.6.1 Example 1 : Investment Equation. 2.6.2 Example 2: Gasoline Demand Equation. 2.6.3 Example 3:
Public Capital Productivity. 2.7 Selected Applications. 2.8 Computational Note. 2.9 Notes. 2.10 Problems. References. 15 15 16 24 28 29 31 31 33 35 38 39 39 40 43 3 The Two-Way Error Component Regression Model. 3.1 Introduction. 3.2 The Two-Way Fixed Effects Model. 3.2.1 Testing for Fixed Effects. 3.3 The Two-Way Random Effects Model. 3.3.1 Monte Carlo Results. 3.4 Maximum Likelihood Estimation. 47 47 48 49 50 55 56 XV
xvi 4 5 Contents 3.5 3.6 Prediction. Examples. 3.6.1 Example 1:Investment Equation. 3.6.2 Example 2: Gasoline Demand Equation. 3.6.3 Example 3: Public Capital Productivity. 3.7 Computational Note. 3.8 Notes. 3.9 Problems. References. 59 60 60 62 64 65 66 66 73 Test of Hypotheses with Panel Data. 4.1 Tests for Poolability. 4.1.1 Test for Poolability Under u~V(0,u2Int) . 4.1.2 Test for Poolability Under the General Assumption u~N{ 0, Ω). 4.1.3 Examples. 4.2 Tests for Individual and Time Elfects. 4.2.1 The Breusch-Pagan Test. 4.2.2 Honda, King and Wu, and the Standardized Lagrange Multiplier
Tests. 4.2.3 Gourieroux, Holly and Monfort Test. 4.2.4 Conditional LM Tests. 4.2.5 ANOVA F and theLikelihood Ratio Tests. 4.2.6 Monte Carlo Results. 4.2.7 An Illustrative Example. 4.3 Hausman’s Specification Test. 4.3.1 Example 1: Investment Equation. 4.3.2 Example 2: Gasoline Demand Equation. 4.3.3 Example 3: Canadian ManufacturingIndustries. 4.3.4 Example 4: Sawmills in WashingtonState. 4.3.5 Example 5: Mariage Premium. 4.3.6 Example 6: Currency Union. 4.3.7 Hausman’s Test for the Two-Way Model. 4.4 Further Reading. 4.5 Notes. 4.6 Problems. References. 75 75 76 Heteroskedasticity and Serial Correlation in the Error Component Model. 5.1
Heteroskedasticity. 5.1.1 Testing for Homoskedasticity in an Error Component Model. 77 80 81 81 83 84 85 85 86 87 89 94 96 97 98 98 98 99 101 103 103 106 109 109 113
Contents xvii 5.2 Serial Correlation. 5.2.1 The AR(1) Process. 5.2.2 The AR(2) Process. 5.2.3 The AR(4) Process for Quarterly Data. 5.2.4 The MA(1) Process . 5.2.5 Unequally Spaced Panels with AR(1) Disturbances. 5.2.6 Prediction. 5.2.7 Testing for Serial Correlation and Individual Effects. 5.3 Time-Wise Autocorrelated and Cross-Sectionally Heteroskedastic Panel Regression. 5.4 Further Reading. 5.5 Notes. 5.6 Problems. References. 115 115 117 118 119 6 Seemingly Unrelated Regressions withError Components. 6.1 The One-Way Model. 6.2 The Two-Way
Model. 6.3 Applications and Extensions. 6.4 Problems. References. 149 149 150 152 153 154 7 Simultaneous Equations withError Components. 7.1 Single Equation Estimation. 7.2 Empirical Example: Crime in North Carolina. 7.3 System Estimation. 7.4 The Hausman and Taylor Estimator. 7.5 Empirical Example: Earnings Equation Using PSID Data . 7.6 Further Reading. 7.7 Notes. 7.8 Problems. References. 157 157 161 166 170 173 177 179 180 185 8 Dynamic Panel Data Models. 8.1 Introduction. 8.2 The Arellano and Bond
Estimator. 8.2.1 Testing for Over-Identification Restrictions and Serial Correlation in Dynamic Panel Models . 8.2.2 Downward Bias of the Estimated Asymptotic Standard Errors. 8.2.3 Too Many Moment Conditions and theBias Efficiency Trade-Off. 187 187 189 120 121 124 136 139 142 142 145 191 193 194
xviii 9 10 Contents 8.3 8.4 8.5 8.6 8.7 8.8 The Arellano and Bover Estimator. The Ahn and Schmidt Moment Conditions. The BlundeU and Bond System GMM Estimator. The Keane and Runkle Estimator. Limited Information Maximum Likelihood. Empirical Examples . 8.8.1 Example 1: Dynamic Demand forCigarettes. 8.8.2 Example 2: Democracy and Education. 8.9 Selected Applications. 8.10 Further Reading. 8.11 Notes. 8.12 Problems. References. 194 198 201 203 205 207 207 210 213 216 220 221 225 Unbalanced Panel DataModels. 9.1 Introduction. 9.2 The Unbalanced One-Way Error Component Model. 9.2.1 ANOVA Methods. 9.3 Maximum Likelihood
Estimators. 9.3.1 Minimum Norm and Minimum Variance Quadratic UnbiasedEstimators (MINQUE and MIVQUE). 9.3.2 Monte Carlo Results. 9.4 Empirical Example: Hedonic Housing. 9.5 The Unbalanced Two-Way Error ComponentModel. 9.5.1 The Fixed Effects Model. 9.5.2 The Random Elfects Model. 9.6 Testing for Individual and Time Effects Using Unbalanced Panel Data. 9.7 The Unbalanced Nested Error ComponentModel. 9.7.1 Empirical Example: Nested States Public Capital Productivity. 9.8 Notes. 9.9 Problems. References. 229 229 229 232 234 235 235 236 240 240 241 243 246 247 251 252 255 Special Topics. 259 10.1 Measurement Error and Panel Data. 259 10.2 Rotating Panels.
263 10.3 Pseudo-Panels. 265 10.4 Short-Run Versus Long-Run Estimates inPooled Models . 268 10.5 Heterogeneous Panels. 269 10.6 Count Panel Data. 276
Contents xix 10.7 Notes. 282 10.8 Problems. 283 References. 287 11 12 Limited Dependent Variables and Panel Data. 11.1 Fixed and Random Logit and Probit Models. 11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data. 11.3 Dynamic Panel Data Limited Dependent Variable Models. 11.4 Selection Bias in Panel Data. 11.5 Censored and Truncated Panel Data Models. 11.6 Empirical Applications. 11.7 Empirical Example: Nurses Labor Supply. 11.8 Further Reading. 11.9 Notes. 11.10 Problems . References. 291 292 Nonstationary Panels. 12.1
Introduction. 12.2 Panel Unit Roots Tests Assuming Cross-Sectional Independence . 12.2.1 Levin, Lin and Chu Test. 12.2.2 Im, Pesaran and Shin Test. 12.2.3 Breitung’s Test. 12.2.4 Combining p-Value Tests. 12.2.5 Residual-Based LM Test. 12.3 Panel Unit Roots Tests Allowing for Cross-Sectional Dependence. 12.4 Spurious Regression in Panel Data. 12.5 Panel Cointegration Tests. 12.5.1 Residual-Based DF and ADF Tests (Kao Tests). 12.5.2 Residual-Based LM Test. 12.5.3 Pedroni Tests. 12.5.4 Likelihood-Based Cointegration Test. 12.5.5 Finite Sample Properties. 12.6 Estimation and Inference in Panel Cointegration Models. 12.7 Empirical Examples . 12.7.1 Example 1: Purchasing Power Parity.
12.7.2 Example 2: International R D Spillover. 12.7.3 Example 3: OECD Health Care Expenditures. 12.8 Further Reading. 337 337 301 303 309 314 319 321 324 326 327 332 339 340 344 345 346 348 350 354 357 357 358 360 361 362 364 368 368 371 372 374
xx 13 Contents 12.9 Notes. 12.10 Problems. References. 381 382 385 Spatial Panel Data Models. 13.1 Introduction. 13.2 Spatial Error Component Regression Model. 13.3 Spatial Lag Panel Data Regression Model . 13.4 Forecasts Using Panel Data with Spatial Error Correlation. . . . 13.5 Panel Unit Root Tests and Spatial Dependence. 13.6 Panel Data Tests for Cross-Sectional Dependence. 13.7 Computational Note. 13.8 Problems. References. 391 391 392 401 408 410 411 418 418 421
Springer Texts in Business and Economics Badi H. Baltagi Econometric Analysis of Panel Data Sixth Edition This textbook offers a comprehensive introduction to panel data econometrics, an area that has enjoyed considerable growth over the last two decades. Micro and Macro panels are becoming increasingly available, and methods for dealing with these types of data are in high demand among practitioners. Software programs have fostered this growth, including freely available programs in R and numerous user-written programs in both Stata and EViews. Written by one of the world’s leading researchers and authors in the field, Econometric Analysis of Panel Data has established itself as the leading textbook for graduate and postgraduate courses on panel data. It provides up-to-date coverage of basic panel data techniques, illustrated with real economic applications and datasets, which are available at the books website on springer.com. This new sixth edition has been fully revised and updated, and includes new material on dynamic panels, limited dependent variables and nonstationary panels, as well as spatial panel data. The author also provides empirical illustrations and examples using Stata and EViews. |
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| id | DE-604.BV047265274 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T17:12:26Z |
| indexdate | 2024-07-10T09:07:13Z |
| institution | BVB |
| isbn | 9783030539528 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032669057 |
| oclc_num | 1245582707 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-N2 DE-20 DE-11 DE-384 |
| owner_facet | DE-355 DE-BY-UBR DE-N2 DE-20 DE-11 DE-384 |
| physical | xx, 424 Seiten 25 cm |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in business and economics |
| spelling | Baltagi, Badi H. 1954- Verfasser (DE-588)128356812 aut Econometric analysis of panel data Badi H. Baltagi Sixth edition Cham, Switzerland Springer [2021] © 2021 xx, 424 Seiten 25 cm txt rdacontent n rdamedia nc rdacarrier Springer texts in business and economics Ökonometrie (DE-588)4132280-0 gnd rswk-swf Panelanalyse (DE-588)4173172-4 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s Panelanalyse (DE-588)4173172-4 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-53953-5 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032669057&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032669057&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Baltagi, Badi H. 1954- Econometric analysis of panel data Ökonometrie (DE-588)4132280-0 gnd Panelanalyse (DE-588)4173172-4 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4173172-4 |
| title | Econometric analysis of panel data |
| title_auth | Econometric analysis of panel data |
| title_exact_search | Econometric analysis of panel data |
| title_exact_search_txtP | Econometric analysis of panel data |
| title_full | Econometric analysis of panel data Badi H. Baltagi |
| title_fullStr | Econometric analysis of panel data Badi H. Baltagi |
| title_full_unstemmed | Econometric analysis of panel data Badi H. Baltagi |
| title_short | Econometric analysis of panel data |
| title_sort | econometric analysis of panel data |
| topic | Ökonometrie (DE-588)4132280-0 gnd Panelanalyse (DE-588)4173172-4 gnd |
| topic_facet | Ökonometrie Panelanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032669057&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032669057&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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