Time series econometrics: learning through replication
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[2023]
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| Ausgabe: | Second edition |
| Schriftenreihe: | Springer texts in business and economics
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| Beschreibung: | xv, 488 Seiten Diagramme |
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| 245 | 1 | 0 | |a Time series econometrics |b learning through replication |c John D. Levendis |
| 250 | |a Second edition | ||
| 264 | 1 | |a Cham |b Springer |c [2023] | |
| 264 | 4 | |c © 2023 | |
| 300 | |a xv, 488 Seiten |b Diagramme | ||
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| 337 | |b n |2 rdamedia | ||
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| 490 | 0 | |a Springer texts in business and economics | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Statistics for Business, Management, Economics, Finance, Insurance | |
| 650 | 4 | |a Macroeconomics/Monetary Economics//Financial Economics | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Macroeconomics | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913293&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
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Contents 1 Introduction. 1.1 What Makes Time ScriesEconometrics Unique?. 1.2 Notation. 1.3 Statistical Review. 1.4 Specifying Time in Stata. 1.5 Installing New Stata Commands. 1.6 Exercises. 1 1 2 5 7 8 9 2 ARMA(p,q) Processes. 2.1 Introduction. 2.1.1 Stationarity. 2.1.2 A Purely Random Process. 2.2 AR(1) Models. 2.2.1 Estimating an AR(1) Model. 2.2.2 Impulse Responses. 2.2.3 Forecasting. 2.3 AR(p) Models. 2.3.1 Estimating an AR(p)
Model. 2.3.2 Impulse Responses. 2.3.3 Forecasting. 2.4 MA(1) Models. 2.4.1 Estimation. 2.4.2 Impulse Responses. 2.4.3 Forecasting. 2.5 MA(q) Models. 2.5.1 Estimation. 2.5.2 Impulse Responses. 2.6 Nonzero ARMA Processes. 2.6.1 Nonzero AR Processes. 2.6.2 Nonzero MA Processes. 2.6.3 Dealing with Nonzero Means. 2.6.4 Example. 11 11 13 15 15 16 21 24 26 27 29 31 33 34 34 35 38 38 40 40 41 42 43 44 ix
Contents ARMA(p.q) Models. 2.7.1 Estimation. Conclusion. 47 47 47 Model Selection in ARMAtp.q) Processes. 3.1 ACFs and PACFs. 3.1.1 Theoretical ACF of an ARI 1I Process. 3.1.2 Theoretical ACF of an ARip) Process. 3.1.3 Theoretical ACT·' of an MAI 11 Process. 3.1.4 Theoretical ACE of an ΜΑίψ Process. 3.1.5 Theoretical PACFs. 3.1.6 Summary: Theoretical z\CTsand PACFs. 3.2 Empirical ACFs and PACFs. 3.2.1 Calculating Empirical ACFs. 3.2.2 Calculating Empirical PACFs. 49 50 50 54 59 61 65 66 66 70 71 Putting It All Together. 75 3.3.1 Example. 3.3.2 Example. 3.3.3 Exercises.
Information Criteria. 3.4.1 Example. 3.4.2 Exercises. 75 77 79 SO SI 52 2.7 2.8 3 3.3 3.4 4 Stationarity and . . 55 4.1 What Is Stationarity?. 55 4.2 The Importance of Stationarity. 56 4.3 Restrictions on AR Coefficients Which Ensure Slationaril). 57 4.3.1 Restrictions on AR( 1 ) Coefficients. 57 4.3.2 Restrictions on z\R(2) Coefficients. SS 4.3.3 Restrictions on AR(p) Coefficients. 96 4.3.4 Characteristic and Inverse Characteristic Equations. 97 4.3.5 Restrictions on ARIMA(p.q) Coefficients. 9S 4.4 The Connection Between AR and Μ .A Processes. 99 4.4.1 AR(1) to MA(:x). 09 4.4.2 AR(p) to MA(oc). 101 4.4.3 Invertibility: MA(1) to AR(c). 101 4.5 What Arc Unit Roots, and Why Are They Bad?. 103 5 Nonstationarity and ARIMA(p.d.q) Processes. 5.1
Differencing. 5.2 The Random Walk. 5.3 The Random Walk with Drift. 5.4 Deterministic Trend. 5.5 Random Walk with Drift VersusDeterministic Trend. 105 Ю5 10S 110 111 112
xi Contents Differencing and Detrending Appropriately. 5.6.1 Mistakenly Differencing (Over-Differencing). 5.6.2 Mistakenly Detrending. Replicating Granger and Newbold (1974). Conclusion. 113 118 121 122 126 6 Seasonal ARMA(p,q) Processes. 6.1 Two Different Types of Seasonality. 6.1.1 Deterministic Seasonality. 6.1.2 Seasonal Differencing. 6.1.3 Additive Seasonality. 6.1.4 Multiplicative Seasonality. 6.1.5 MA Seasonality. 6.2 Identification. 6.3 Invertibility and Stability. 6.4 How Common Are Seasonal Unit Roots?. 6.5 Using Deseasonalized Data. 6.6 Conclusion. 127 127 128 130 132 132 135 136 138 139 140 141 7 Unit Root Tests.
7.1 Introduction. 7.2 Unit Root Tests. 7.3 Dickey-Fuller Tests. 7.3.1 A Random Walk Versus a Zero-Mean AR( 1 ) Process. 7.3.2 A Random Walk Versus an AR(1) Model with a Constant. 150 7.3.3 A Random Walk with Drift Versus a Deterministic Trend. 152 7.3.4 Augmented Dickey-Fuller Tests. 7.3.5 DF-GLS Tests. 7.3.6 Choosing the Lag Length in DF-Туре Tests. 7.4 Phillips-Perron Tests. 7.5 KPSS Tests. 7.6 Replicating Nelson and Plosser. 7.7 Testing for Seasonal Unit Roots. . 7.8 Conclusion and Further Readings. 143 143 144 145 145 Structural Breaks. 8.1 Structural Breaks and Unit Roots. 8.2 Perron (1989): Tests for a Unit Root with a Known Structural
Break. 177 8.3 Zivot and Andrews' Test of a Break at an Unknown Date. 8.3.1 Replicating Zivot Andrews (1992) in Stata. 8.3.2 The zandrews Command. 8.4 Further Readings. 175 176 5.6 5.7 5.8 8 155 157 157 160 162 164 172 172 188 189 194 197
xii 9 10 Contents ARCH, GARCH, and Time»Varying Variance. 9.1 Introduction. 9.2 Conditional Versus Unconditional Moments. 9.3 ARCH Models. 9.3.1 ARCH(). 9.3.2 AR(1)-ARCH(1). 9.3.3 ARCH(2). 9.3.4 ARCH(q). 9.3.5 Example 1 : Toyota Motor Company. 9.3.6 Example 2: Ford Motor Company. 9.4 GARCH Models. 9.4.1 GARCHO.n. 9.4.2 GARCH(p.q). 9.5 Variations on GARCH. 9.5.1 9.5.2 GARCH-M or . . 9.5.3 Asymmetric Responses in GARCH. 9.5.4 I-GARCH or Integrated GARCH. 9.6 Exercises. 201 201 204 205 205 212 217 221 225 230
233 233 236 242 242 24S 251 25S 260 Vector Autoregressions I: Basics. 10.1 Introduction. 10.1.1 A History Lesson. 10.2 A Simple VAR(l) and How to Estimate It. 10.3 How Many Lags to Include?. 10.4 Expressing VARs in Matrix Form. 10.4.1 Any VAR(p) Can Be Rewritten as a VAR(D). 10.5 Stability. 10.5.1 Method 1. 10.5.2 Method 2. 10,5.3 Stata Command Varstable. 10.6 Long-Run Levels: Including a Constant. 10.7 Expressing a VAR as an VMA Process. 10.8 Impulse Response Functions. 10.8.1 IRFs as the Components of the MA Coefficients. 10.9 Forecasting. 10.10 Granger Causality. 10.10.1 Replicating Sims
(1972). 10.10.2 Indirect Causality. 10.11 VAR Example: GNP and Unemployment. 10.12 Exercises. 263 263 264 266 270 272 274 276 276 279 282 2S2 284 285 286 292 296 298 301 303 308
xüi Contents 11 Vector Autoregressions Π: Extensions. 311 11.1 Orthogonalized . 311 11.1.1 Order Matters in OIRFs. 313 11.1.2 Cholesky Decompositions and OIRFs. 315 11.1.3 Why Order Matters for OIRFs. Forecast Error Variance Decompositions. Structural . . 11.3.1 Reduced Form vs Structural Form. 329 11.3.2 SVARs Are Unidentified. 330 11.3.3 The General Form of SVARs. 332 11.3.4 Cholesky Is an SVAR. 333 11.3.5 Long-Run Restrictions; Blanchard and Quah (1989). VARs with Integrated Variables. Conclusion. 324 326 329 12 Cointegration and VECMs. 12.1 Introduction. 12.2 Cointegration. 12.3 Error Correction Mechanism. 12.3.1 The Effect of the Adjustment
Parameter. 12.4 Deriving the ECM. 12.5 Engle and Granger's Residual-Based Tests of Cointegration. 12.5.1 MacKinnon Critical Values for Engle-Granger Tests. 12.5.2 Engle-Granger Approach. 12.6 Multi-equation Models and VECMs. 12.6.1 Deriving the VECM from a Simple VAR(2). 360 12.6.2 Deriving the VECM(k-l) from a Reduced-Form VAR(k). 362 12.6.3 Π = «ß‘ Is Not Uniquely Identified. 363 12.6.4 Johansen’s Tests and the Rank of Π. 364 12.7 IRFs, OIRFs, and Forecasting from VECMs. 12.8 Lag Length Selection. 12.9 Cointegration Implies Granger Causality. 12.9.1 Testing for Granger Causality. 12.10 Conclusion. 12.11 Exercises. 343 343 343 348 350 350 351 352 354 360 Static Panel Data Models. 13.1 Introduction. 13.2 Formatting the
Data. 13.2.1 Wide and Long. 13.3 The Static Panel Model. 13.3.1 The Error Terms. 13.3.2 Pooled OLS. 13.3.3 Endogeneity. 13.3.4 First Differencing. 13.3.5 Demeaning. 385 385 386 387 388 389 390 390 391 392 11.2 11.3 11.4 11.5 13 336 338 340 375 375 377 377 378 379
xiv Contents 13.4 13.5 Fixed Effects and Random Effects. 13.4.1 Random Effects Models. 13.4.2 Fixed Effects Models. 13.4.3 Estimating FE Models. 13.4.4 Two Equivalent Ways of Estimating FE Models. Choosing Between RE and FE Models. 13.5.1 Hausman Test. 13.5.2 Mundlak Test. 4(X) 13.5.3 You Must Estimate the Correct Model. Time Fixed Effects. 13.6.1 Estimation. 13.6.2 Estimating FEs with Explicit Time Dummies in Stata. 406 Cross-Sectional Dependence. 13.7.1 Driscoll-Kraay Standard Errors. 13.7.2 If CSD Shocks Are Correlated with Regressors. 13.7.3 Testing for CSD in Stata. 13.7.4 Lagrange Multiplier CSD Test. 13.7.5 Estimation When You Have CSD. 401 403 405 Dynamic Panel Data Models. 14.1 Dynamic Panel
Bias. 14.1.1 Demeaning Does Not Fix This Problem. 14.1.2 First Differencing Does Not Fix This Problem. 14.1.3 GMM Estimators Fix This Problem. 14.1.4 Arellano-Bond-Type Estimators. 14.1.5 Forward Orthogonal Deviations. 14.1.6 Arellano-Bond in Stata. 14.1.7 Too Many Instruments. 14.1.8 Sargan's and Hansen's J-Test for Instrument Validity. 415 415 416 417 417 418 419 419 420 13.6 13.7 14 393 394 394 394 395 396 396 14.1.9 14.2 14.3 14.4 14.5 14.6 14,7 408 409 410 411 412 413 423 Difference-in-Hansen/Sargan Tests. 424 14.1.10 Blundell-Bond Test of Autocorrelation. Replicating Arellano and Bond (1991). Replicating Thomson (2017). Stationarity and Panel Unit Root Tests. 14.4.1 First-Generation Tests. 14.4.2 Second-Generation Tests. Structural Breaks. Panel
VARs. 14.6.1 Panel VAR Example. Cointegration Tests. 14.7.1 Cointegration Test Example: The Permanent Income Hypothesis. 424 426 429 431 432 439 441 442 444 44S 451
Contents 14.8 14.9 XV Further Reading. 455 Homework. 455 15 Conclusion. 459 A Tables of Critical Values. 465 References. 471 Index. 483 |
| adam_txt |
Contents 1 Introduction. 1.1 What Makes Time ScriesEconometrics Unique?. 1.2 Notation. 1.3 Statistical Review. 1.4 Specifying Time in Stata. 1.5 Installing New Stata Commands. 1.6 Exercises. 1 1 2 5 7 8 9 2 ARMA(p,q) Processes. 2.1 Introduction. 2.1.1 Stationarity. 2.1.2 A Purely Random Process. 2.2 AR(1) Models. 2.2.1 Estimating an AR(1) Model. 2.2.2 Impulse Responses. 2.2.3 Forecasting. 2.3 AR(p) Models. 2.3.1 Estimating an AR(p)
Model. 2.3.2 Impulse Responses. 2.3.3 Forecasting. 2.4 MA(1) Models. 2.4.1 Estimation. 2.4.2 Impulse Responses. 2.4.3 Forecasting. 2.5 MA(q) Models. 2.5.1 Estimation. 2.5.2 Impulse Responses. 2.6 Nonzero ARMA Processes. 2.6.1 Nonzero AR Processes. 2.6.2 Nonzero MA Processes. 2.6.3 Dealing with Nonzero Means. 2.6.4 Example. 11 11 13 15 15 16 21 24 26 27 29 31 33 34 34 35 38 38 40 40 41 42 43 44 ix
Contents ARMA(p.q) Models. 2.7.1 Estimation. Conclusion. 47 47 47 Model Selection in ARMAtp.q) Processes. 3.1 ACFs and PACFs. 3.1.1 Theoretical ACF of an ARI 1I Process. 3.1.2 Theoretical ACF of an ARip) Process. 3.1.3 Theoretical ACT·' of an MAI 11 Process. 3.1.4 Theoretical ACE of an ΜΑίψ Process. 3.1.5 Theoretical PACFs. 3.1.6 Summary: Theoretical z\CTsand PACFs. 3.2 Empirical ACFs and PACFs. 3.2.1 Calculating Empirical ACFs. 3.2.2 Calculating Empirical PACFs. 49 50 50 54 59 61 65 66 66 70 71 Putting It All Together. 75 3.3.1 Example. 3.3.2 Example. 3.3.3 Exercises.
Information Criteria. 3.4.1 Example. 3.4.2 Exercises. 75 77 79 SO SI 52 2.7 2.8 3 3.3 3.4 4 Stationarity and . . 55 4.1 What Is Stationarity?. 55 4.2 The Importance of Stationarity. 56 4.3 Restrictions on AR Coefficients Which Ensure Slationaril). 57 4.3.1 Restrictions on AR( 1 ) Coefficients. 57 4.3.2 Restrictions on z\R(2) Coefficients. SS 4.3.3 Restrictions on AR(p) Coefficients. 96 4.3.4 Characteristic and Inverse Characteristic Equations. 97 4.3.5 Restrictions on ARIMA(p.q) Coefficients. 9S 4.4 The Connection Between AR and Μ .A Processes. 99 4.4.1 AR(1) to MA(:x). 09 4.4.2 AR(p) to MA(oc). 101 4.4.3 Invertibility: MA(1) to AR(c). 101 4.5 What Arc Unit Roots, and Why Are They Bad?. 103 5 Nonstationarity and ARIMA(p.d.q) Processes. 5.1
Differencing. 5.2 The Random Walk. 5.3 The Random Walk with Drift. 5.4 Deterministic Trend. 5.5 Random Walk with Drift VersusDeterministic Trend. 105 Ю5 10S 110 111 112
xi Contents Differencing and Detrending Appropriately. 5.6.1 Mistakenly Differencing (Over-Differencing). 5.6.2 Mistakenly Detrending. Replicating Granger and Newbold (1974). Conclusion. 113 118 121 122 126 6 Seasonal ARMA(p,q) Processes. 6.1 Two Different Types of Seasonality. 6.1.1 Deterministic Seasonality. 6.1.2 Seasonal Differencing. 6.1.3 Additive Seasonality. 6.1.4 Multiplicative Seasonality. 6.1.5 MA Seasonality. 6.2 Identification. 6.3 Invertibility and Stability. 6.4 How Common Are Seasonal Unit Roots?. 6.5 Using Deseasonalized Data. 6.6 Conclusion. 127 127 128 130 132 132 135 136 138 139 140 141 7 Unit Root Tests.
7.1 Introduction. 7.2 Unit Root Tests. 7.3 Dickey-Fuller Tests. 7.3.1 A Random Walk Versus a Zero-Mean AR( 1 ) Process. 7.3.2 A Random Walk Versus an AR(1) Model with a Constant. 150 7.3.3 A Random Walk with Drift Versus a Deterministic Trend. 152 7.3.4 Augmented Dickey-Fuller Tests. 7.3.5 DF-GLS Tests. 7.3.6 Choosing the Lag Length in DF-Туре Tests. 7.4 Phillips-Perron Tests. 7.5 KPSS Tests. 7.6 Replicating Nelson and Plosser. 7.7 Testing for Seasonal Unit Roots. . 7.8 Conclusion and Further Readings. 143 143 144 145 145 Structural Breaks. 8.1 Structural Breaks and Unit Roots. 8.2 Perron (1989): Tests for a Unit Root with a Known Structural
Break. 177 8.3 Zivot and Andrews' Test of a Break at an Unknown Date. 8.3.1 Replicating Zivot Andrews (1992) in Stata. 8.3.2 The zandrews Command. 8.4 Further Readings. 175 176 5.6 5.7 5.8 8 155 157 157 160 162 164 172 172 188 189 194 197
xii 9 10 Contents ARCH, GARCH, and Time»Varying Variance. 9.1 Introduction. 9.2 Conditional Versus Unconditional Moments. 9.3 ARCH Models. 9.3.1 ARCH(). 9.3.2 AR(1)-ARCH(1). 9.3.3 ARCH(2). 9.3.4 ARCH(q). 9.3.5 Example 1 : Toyota Motor Company. 9.3.6 Example 2: Ford Motor Company. 9.4 GARCH Models. 9.4.1 GARCHO.n. 9.4.2 GARCH(p.q). 9.5 Variations on GARCH. 9.5.1 9.5.2 GARCH-M or . . 9.5.3 Asymmetric Responses in GARCH. 9.5.4 I-GARCH or Integrated GARCH. 9.6 Exercises. 201 201 204 205 205 212 217 221 225 230
233 233 236 242 242 24S 251 25S 260 Vector Autoregressions I: Basics. 10.1 Introduction. 10.1.1 A History Lesson. 10.2 A Simple VAR(l) and How to Estimate It. 10.3 How Many Lags to Include?. 10.4 Expressing VARs in Matrix Form. 10.4.1 Any VAR(p) Can Be Rewritten as a VAR(D). 10.5 Stability. 10.5.1 Method 1. 10.5.2 Method 2. 10,5.3 Stata Command Varstable. 10.6 Long-Run Levels: Including a Constant. 10.7 Expressing a VAR as an VMA Process. 10.8 Impulse Response Functions. 10.8.1 IRFs as the Components of the MA Coefficients. 10.9 Forecasting. 10.10 Granger Causality. 10.10.1 Replicating Sims
(1972). 10.10.2 Indirect Causality. 10.11 VAR Example: GNP and Unemployment. 10.12 Exercises. 263 263 264 266 270 272 274 276 276 279 282 2S2 284 285 286 292 296 298 301 303 308
xüi Contents 11 Vector Autoregressions Π: Extensions. 311 11.1 Orthogonalized . 311 11.1.1 Order Matters in OIRFs. 313 11.1.2 Cholesky Decompositions and OIRFs. 315 11.1.3 Why Order Matters for OIRFs. Forecast Error Variance Decompositions. Structural . . 11.3.1 Reduced Form vs Structural Form. 329 11.3.2 SVARs Are Unidentified. 330 11.3.3 The General Form of SVARs. 332 11.3.4 Cholesky Is an SVAR. 333 11.3.5 Long-Run Restrictions; Blanchard and Quah (1989). VARs with Integrated Variables. Conclusion. 324 326 329 12 Cointegration and VECMs. 12.1 Introduction. 12.2 Cointegration. 12.3 Error Correction Mechanism. 12.3.1 The Effect of the Adjustment
Parameter. 12.4 Deriving the ECM. 12.5 Engle and Granger's Residual-Based Tests of Cointegration. 12.5.1 MacKinnon Critical Values for Engle-Granger Tests. 12.5.2 Engle-Granger Approach. 12.6 Multi-equation Models and VECMs. 12.6.1 Deriving the VECM from a Simple VAR(2). 360 12.6.2 Deriving the VECM(k-l) from a Reduced-Form VAR(k). 362 12.6.3 Π = «ß‘ Is Not Uniquely Identified. 363 12.6.4 Johansen’s Tests and the Rank of Π. 364 12.7 IRFs, OIRFs, and Forecasting from VECMs. 12.8 Lag Length Selection. 12.9 Cointegration Implies Granger Causality. 12.9.1 Testing for Granger Causality. 12.10 Conclusion. 12.11 Exercises. 343 343 343 348 350 350 351 352 354 360 Static Panel Data Models. 13.1 Introduction. 13.2 Formatting the
Data. 13.2.1 Wide and Long. 13.3 The Static Panel Model. 13.3.1 The Error Terms. 13.3.2 Pooled OLS. 13.3.3 Endogeneity. 13.3.4 First Differencing. 13.3.5 Demeaning. 385 385 386 387 388 389 390 390 391 392 11.2 11.3 11.4 11.5 13 336 338 340 375 375 377 377 378 379
xiv Contents 13.4 13.5 Fixed Effects and Random Effects. 13.4.1 Random Effects Models. 13.4.2 Fixed Effects Models. 13.4.3 Estimating FE Models. 13.4.4 Two Equivalent Ways of Estimating FE Models. Choosing Between RE and FE Models. 13.5.1 Hausman Test. 13.5.2 Mundlak Test. 4(X) 13.5.3 You Must Estimate the Correct Model. Time Fixed Effects. 13.6.1 Estimation. 13.6.2 Estimating FEs with Explicit Time Dummies in Stata. 406 Cross-Sectional Dependence. 13.7.1 Driscoll-Kraay Standard Errors. 13.7.2 If CSD Shocks Are Correlated with Regressors. 13.7.3 Testing for CSD in Stata. 13.7.4 Lagrange Multiplier CSD Test. 13.7.5 Estimation When You Have CSD. 401 403 405 Dynamic Panel Data Models. 14.1 Dynamic Panel
Bias. 14.1.1 Demeaning Does Not Fix This Problem. 14.1.2 First Differencing Does Not Fix This Problem. 14.1.3 GMM Estimators Fix This Problem. 14.1.4 Arellano-Bond-Type Estimators. 14.1.5 Forward Orthogonal Deviations. 14.1.6 Arellano-Bond in Stata. 14.1.7 Too Many Instruments. 14.1.8 Sargan's and Hansen's J-Test for Instrument Validity. 415 415 416 417 417 418 419 419 420 13.6 13.7 14 393 394 394 394 395 396 396 14.1.9 14.2 14.3 14.4 14.5 14.6 14,7 408 409 410 411 412 413 423 Difference-in-Hansen/Sargan Tests. 424 14.1.10 Blundell-Bond Test of Autocorrelation. Replicating Arellano and Bond (1991). Replicating Thomson (2017). Stationarity and Panel Unit Root Tests. 14.4.1 First-Generation Tests. 14.4.2 Second-Generation Tests. Structural Breaks. Panel
VARs. 14.6.1 Panel VAR Example. Cointegration Tests. 14.7.1 Cointegration Test Example: The Permanent Income Hypothesis. 424 426 429 431 432 439 441 442 444 44S 451
Contents 14.8 14.9 XV Further Reading. 455 Homework. 455 15 Conclusion. 459 A Tables of Critical Values. 465 References. 471 Index. 483 |
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| author | Levendis, John D. |
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| dewey-full | 330.015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.015195 |
| dewey-search | 330.015195 |
| dewey-sort | 3330.015195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| id | DE-604.BV049568038 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:29:51Z |
| indexdate | 2024-09-12T10:00:42Z |
| institution | BVB |
| isbn | 9783031373091 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034913293 |
| oclc_num | 1429568542 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-20 DE-N2 |
| owner_facet | DE-355 DE-BY-UBR DE-20 DE-N2 |
| physical | xv, 488 Seiten Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in business and economics |
| spelling | Levendis, John D. Verfasser (DE-588)1181253942 aut Time series econometrics learning through replication John D. Levendis Second edition Cham Springer [2023] © 2023 xv, 488 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Springer texts in business and economics Econometrics Statistics for Business, Management, Economics, Finance, Insurance Macroeconomics/Monetary Economics//Financial Economics Statistics Macroeconomics 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=034913293&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Levendis, John D. Time series econometrics learning through replication Econometrics Statistics for Business, Management, Economics, Finance, Insurance Macroeconomics/Monetary Economics//Financial Economics Statistics Macroeconomics |
| title | Time series econometrics learning through replication |
| title_auth | Time series econometrics learning through replication |
| title_exact_search | Time series econometrics learning through replication |
| title_exact_search_txtP | Time series econometrics learning through replication |
| title_full | Time series econometrics learning through replication John D. Levendis |
| title_fullStr | Time series econometrics learning through replication John D. Levendis |
| title_full_unstemmed | Time series econometrics learning through replication John D. Levendis |
| title_short | Time series econometrics |
| title_sort | time series econometrics learning through replication |
| title_sub | learning through replication |
| topic | Econometrics Statistics for Business, Management, Economics, Finance, Insurance Macroeconomics/Monetary Economics//Financial Economics Statistics Macroeconomics |
| topic_facet | Econometrics Statistics for Business, Management, Economics, Finance, Insurance Macroeconomics/Monetary Economics//Financial Economics Statistics Macroeconomics |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034913293&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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