Time series econometrics: learning through replication
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer
[2018]
|
| Schriftenreihe: | Springer texts in business and economics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xiii, 409 Seiten Diagramme |
| ISBN: | 9783319982816 |
Internformat
MARC
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| 100 | 1 | |a Levendis, John D. |e Verfasser |0 (DE-588)1181253942 |4 aut | |
| 245 | 1 | 0 | |a Time series econometrics |b learning through replication |c John D. Levendis |
| 264 | 1 | |a Cham |b Springer |c [2018] | |
| 264 | 4 | |c © 2018 | |
| 300 | |a xiii, 409 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Springer texts in business and economics | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 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 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-319-98282-3 |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-030944738 | ||
Datensatz im Suchindex
| _version_ | 1804179543680876544 |
|---|---|
| adam_text | Contents
1 Introduction...................................................... 1
1.1 What Makes Time-Series Econometrics Unique?.............. 1
1.2 Notation..................................................... 3
1.3 Statistical Review........................................ 5
1.4 Specifying Time in Stata..................................... 7
1.5 Installing New Stata Commands................................ 8
1.6 Exercises.................................................. 9
2 ARMA(p,q) Processes............................................... 11
2.1 Introduction.............................................. 11
2.1.1 Stationarity....................................... 12
2.1.2 A Purely Random Process............................ 14
2.2 AR(1) Models................................................ 15
2.2.1 Estimating an AR(1) Model.......................... 15
2.2.2 Impulse Responses.................................. 20
2.2.3 Forecasting........................................ 24
2.3 AR(p) Models................................................ 25
2.3.1 Estimating an AR(p) Model.......................... 26
2.3.2 Impulse Responses.................................. 28
2.3.3 Forecasting........................................ 30
2.4 MA(1) Models................................................ 32
2.4.1 Estimation......................................... 33
2.4.2 Impulse Responses.................................. 33
2.4.3 Forecasting........................................ 34
2.5 MA(q) Models................................................ 37
2.5.1 Estimation......................................... 37
2.5.2 Impulse Responses.................................. 38
2.6 Non-zero ARMA Processes..................................... 39
2.6.1 Non-zero AR Processes ......................-.... 40
2.6.2 Non-zero MA Processes.............................. 41
2.6.3 Dealing with Non-zero Means........................ 42
2.7 ARMA(p,q) Models............................................ 45
2.7.1 Estimation........................................ 46
2.8 Conclusion.................................................. 46
IX
X
Contents
3 Model Selection in ARMA(p,q) Processes .............................. 47
3.1 ACFs and PACFs............................................... 48
3.1.1 Theoretical ACF of an AR(1) Process.................. 48
3.1.2 Theoretical ACF of an AR(p) Process.................. 52
3.1.3 Theoretical ACF of an MA(1) Process .................. 57
3.1.4 Theoretical ACF of an MA(q) Process ................... 59
3.1.5 Theoretical PACFs.................................. 63
3.1.6 Summary: Theoretical ACFs and PACFs.................... 64
3.2 Empirical ACFs and PACFs......................................... 64
3.2.1 Calculating Empirical ACFs .......................... 68
3.2.2 Calculating Empirical PACFs............................ 69
3.3 Putting It All Together.......................................... 72
3.4 Information Criteria............................................ 77
4 Stationarity and Invertibility....................................... 81
4.1 What Is Stationarity? ........................................... 81
4.2 The Importance of Stationarity................................... 82
4.3 Restrictions on AR coefficients Which Ensure Stationarity.... 83
4.3.1 Restrictions on AR(1) Coefficients .................... 83
4.3.2 Restrictions on AR(2) Coefficients .................... 84
4.3.3 Restrictions on AR(p) Coefficients .................... 92
4.3.4 Characteristic and Inverse Characteristic Equations ... 93
4.3.5 Restrictions on ARIMA(p,q) Coefficients................ 94
4.4 The Connection Between AR and MA Processes....................... 95
4.4.1 AR(1) to MA(oo)........................................ 95
4.4.2 AR(p) to MA(co)........................................ 97
4.4.3 Invertibility: MA(1) to AR(oo)........................ 97
4.5 What Are Unit Roots, and Why Are They Bad?....................... 99
5 Non-stationarity and ARIMA(p,d,q) Processes............................. 101
5.1 Differencing.................................................. 101
5.1.1 Example of Differencing............................... 102
5.2 The Random Walk................................................. 104
5.2.1 The Mean and Variance of the Random Walk.............. 104
5.2.2 Taking the First Difference Makes it Stationary.... 105
5.3 The Random Walk with Drift.................................... 106
5.3.1 The Mean and Variance of the Random Walk
with Drift............................................ 106
5.3.2 Taking the First Difference Makes it Stationary.... 107
5 A Deterministic Trend........................................... 107
5.4.1 Mean and Variance..................................... 107
5.4.2 First Differencing Introduces an MA Unit Root...... 107
5.5 Random Walk with Drift vs Deterministic Trend................... 108
5.6 Differencing and Detrending Appropriately....................... 109
5.6.1 Mistakenly Differencing (Overdifferencing)............ 113
5.6.2 Mistakenly Detrending............................... 116
Contents xi
5.7 Replicating Granger and Newbold (1974)....................... 117
5.8 Conclusion .................................................. 121
6 Seasonal ARMA(p,q) Processes...................................... 123
6.1 Different Types of Seasonality............................ 123
6.1.1 Deterministic Seasonality ......................... 125
6.1.2 Seasonal Differencing ........................... 126
6.1.3 Additive Seasonality............................... 127
6.1.4 Multiplicative Seasonality......................... 128
6.1.5 MA Seasonality..................................... 131
6.2 Identification............................................... 133
6.3 Invertibility and Stability.................................. 135
6.4 How Common are Seasonal Unit Roots?.......................... 135
6.5 Using De-seasonalized Data................................. 136
6.6 Conclusion .................................................. 137
7 Unit Root Tests................................................... 139
7.1 Introduction................................................. 139
7.2 Unit Root Tests............................................ 140
7.3 Dickey-Fuller Tests.......................................... 141
7.3.1 A Random Walk vs a Zero-Mean AR( 1) Process...... 142
7.3.2 A Random Walk vs an AR(1) Model with a Constant. 146
7.3.3 A Random Walk with Drift vs a Deterministic Trend.. 148
7.3.4 Augmented Dickey-Fuller Tests ..................... 150
7.3.5 DF-GLS Tests....................................... 152
7.3.6 Choosing the Lag Length in DF-Type Tests........... 153
7.4 Phillips-Perron Tests........................................ 156
7.5 KPSS Tests................................................... 158
7.6 Nelson and Plosser........................................... 160
7.7 Testing for Seasonal Unit Roots.............................. 168
7.8 Conclusion and Further Readings.............................. 169
8 Structural B reaks................................................ 171
8.1 Structural Breaks and Unit Roots............................. 171
8.2 Perron (1989): Tests for a Unit Root with a Known
Structural Break............................................. 173
8.3 Zivot and Andrews’ Test of a Break at an Unknown Date...... 184
8.3.1 Replicating Zivot and Andrews (1992) in Stata.... 185
8.3.2 The zandrews Command .............................. 191
8.4 Further Readings............................................. 194
9 ARCH, GARCH and Time-Varying Variance............................... 197
9.1 Introduction................................................. 197
9.2 Conditional vs Unconditional Moments....................... 200
9.3 ARCH Models................................................ 201
9.3.1 ARCH(l)............................................ 201
9.3.2 AR(1)-ARCH(1)...................................... 208
221
226
229
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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 GARCH(1,1)...........................
9.4.2 GARCH(p,q)...........................
9.5 Variations on GARCH.................................
9.5.1 GARCH-t..............................
9.5.2 GARCH-M or GARCH-IN-MEAN...................
9.5.3 Asymmetric Responses in GARCH..............
9.5.4 I-GARCH or Integrated GARCH................
9.6 Exercises ..........................................
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(l)__
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 a 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 ..........................................
Vector Autoregressions II: Extensions .....................
11.1 Orthogonalized IRFs.................................
11.1.1 Order Matters in OIRFs ....................
11.1.2 Cholesky Decompositions and OIRFs..........
11.1.3 Why Order Matters for OIRFs................
11.2 Forecast Error Variance Decompositions..............
11.3 Structural VARs.....................................
11.3.1 Reduced Form vs Structural Form............
11.3.2 SVARs are Unidentified.....................
11.3.3 The General Form of SVARs..................
Contents xiii
11.3.4 Cholesky is an SVAR............................... 333
11.3.5 Long-Run Restrictions: Blanchard and Quah (1989) .. 336
11.4 VARs with Integrated Variables.............................. 339
11.5 Conclusion ................................................. 340
12 Cointegration and VECMs................................. 343
12.1 Introduction................................................ 343
12.2 Cointegration............................................... 343
12.3 Error Correction Mechanism.................................. 348
12.3.1 The Effect of the Adjustment Parameter........... 350
12.4 Deriving the ECM ........................................... 350
12.5 Engle and Granger’s Residual-Based Tests of Cointegration.. 351
12.5.1 MacKinnon Critical Values for Engle-Granger Tests .. 352
12.5.2 Engle-Granger Approach........................... 354
12.6 Multi-Equation Models and VECMs .............. 360
12.6.1 Deriving the VECM from a Simple VAR(2) .......... 361
12.6.2 Deriving the VECM(k-l) from a Reduced-form
VAR(k)............................................ 362
12.6.3 II = afif is Not Uniquely Identified............. 363
12.6.4 Johansen’s Tests and the Rank of II.............. 364
12.7 IRFs, OIRFs and Forecasting from VECMs...................... 376
12.8 Lag-Length Selection........................................ 376
12.9 Cointegration Implies Granger Causality..................... 377
12.9.1 Testing for Granger Causality....................... 378
12.10 Conclusion ................................................. 379
12.11 Exercises .................................................. 379
13 Conclusion.......................................................... 383
A Tables of Critical Values........................................... 389
Bibliography........................................................... 395
Index
405
|
| any_adam_object | 1 |
| author | Levendis, John D. |
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| indexdate | 2024-07-10T08:21:32Z |
| institution | BVB |
| isbn | 9783319982816 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030944738 |
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| spelling | Levendis, John D. Verfasser (DE-588)1181253942 aut Time series econometrics learning through replication John D. Levendis Cham Springer [2018] © 2018 xiii, 409 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Springer texts in business and economics Hier auch später erschienene, unveränderte Nachdrucke Econometrics Statistics for Business, Management, Economics, Finance, Insurance Macroeconomics/Monetary Economics//Financial Economics Statistics Macroeconomics Erscheint auch als Online-Ausgabe 978-3-319-98282-3 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=030944738&sequence=000002&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_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=030944738&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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