Introduction to time series using Stata:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Tex.
Stata Press
2013
|
| Schriftenreihe: | A Stata Press publication
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references |
| Beschreibung: | xxv, 443 Seiten Diagramme 24 cm |
| ISBN: | 9781597181327 1597181323 |
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| 100 | 1 | |a Becketti, Sean |e Verfasser |0 (DE-588)170149366 |4 aut | |
| 245 | 1 | 0 | |a Introduction to time series using Stata |c Sean Becketti |
| 264 | 1 | |a College Station, Tex. |b Stata Press |c 2013 | |
| 300 | |a xxv, 443 Seiten |b Diagramme |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a A Stata Press publication | |
| 500 | |a Includes bibliographical references | ||
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Datensatz im Suchindex
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IMAGE 1
CONTENTS
LIST OF TABLES XIII
LIST OF FIGURES XV
PREFACE XXI
ACKNOWLEDGMENTS XXVII
JUST ENOUGH STATA 1
1.1 GETTING STARTED 2
1.1.1 ACTION FIRST, EXPLANATION LATER 2
1.1.2 NOW SOME EXPLANATION G
1.1.3 NAVIGATING THE INTERFACE 7
1.1.4 THE GESTALT OF STATA 13
1.1.5 THE PARTS OF STATA SPEECH 15
1.2 ALL ABOUT DATA 19
1.3 LOOKING AT DATA 29
1.4 STATISTICS 49
1.4.1 BASICS 49
1.4.2 ESTIMATION 53
1.5 ODDS AND ENDS 60
1.6 MAKING A DATE 62
L.G.L HOW TO LOOK GOOD 63
1.6.2 TRANSFORMERS 65
1.7 TYPING DATES AND DATE VARIABLES 68
1.8 LOOKING AHEAD 69
JUST ENOUGH STATISTICS 71
2.1 RANDOM VARIABLES AND THEIR MOMENTS 72
IMAGE 2
VI CONTENTS
2.2 HYPOTHESIS TESTS 73
2.3 LINEAR REGRESSION 74
2.3.1 ORDINARY LEAST SQUARES 74
2.3.2 INSTRUMENTAL VARIABLES 77
2.3.3 FGLS 77
2.4 MULTIPLE-EQUATION MODELS 78
2.5 TIME SERIES 79
2.5.1 WHITE NOISE, AUTOCORRELATION, AND STATIONARITY 80
2.5.2 ARMA MODELS 82
3 FILTERING TIME-SERIES DATA 85
3.1 PREPARING TO ANALYZE A TIME SERIES 87
3.1.1 QUESTIONS FOR ALL TYPES OF DATA 87
HOW ARE THE VARIABLES DEFINED? 87
WHAT IS THE RELATIONSHIP BETWEEN THE DATA AND THE PHE- NOMENON OF
INTEREST? 88
WHO COMPILED THE DATA? 90
WHAT PROCESSES GENERATED THE DATA? 90
3.1.2 QUESTIONS SPECIFICALLY FOR TIME-SERIES DATA 91
WHAT IS THE FREQUENCY OF MEASUREMENT? 91
ARE THE DATA SEASONALLY ADJUSTED? 91
ARE THE DATA REVISED? 91
3.2 THE FOUR COMPONENTS OF A TIME SERIES 92
TREND 93
CYCLE 95
SEASONAL 97
3.3 SOME SIMPLE FILTERS 100
3.3.1 SMOOTHING A TREND 103
3.3.2 SMOOTHING A CYCLE 109
3.3.3 SMOOTHING A SEASONAL PATTERN 114
3.3.4 SMOOTHING REAL DATA 115
IMAGE 3
CONTENTS VII
3.4 ADDITIONAL FILTERS 121
3.4.1 MA: WEIGHTED MOVING AVERAGES 123
3.4.2 EWMAS 125
EXPONENTIAL: EWMAS 126
DEXPONENTIAL: DOUBLE-EXPONENTIAL MOVING AVERAGES 130
3.4.3 HOLT-WINTERS SMOOTHERS 131
HWINTERS: HOLT-WINTERS SMOOTHERS WITHOUT A SEASONAL COMPONENT 131
SHWINTERS: HOLT-WINTERS SMOOTHERS INCLUDING A SEASONAL COMPONENT 137
3.5 POINTS TO REMEMBER 138
4 A FIRST PASS AT FORECASTING 141
4.1 FORECAST FUNDAMENTALS 141
4.1.1 TYPES OF FORECASTS 142
4.1.2 MEASURING THE QUALITY OF A FORECAST 144
4.1.3 ELEMENTS OF A FORECAST 144
4.2 FILTERS THAT FORECAST 146
4.2.1 FORECASTS BASED ON EWMAS 148
4.2.2 FORECASTING A TRENDING SERIES WITH A SEASONAL COMPONENT . . 159
4.3 POINTS TO REMEMBER 165
4.4 LOOKING AHEAD 166
5 AUTOCORRELATED DISTURBANCES 167
5.1 AUTOCORRELATION 168
5.1.1 EXAMPLE: MORTGAGE RATES 169
5.2 REGRESSION MODELS WITH AUTOCORRELATED DISTURBANCES 172
5.2.1 FIRST-ORDER AUTOCORRELATION 173
5.2.2 EXAMPLE: MORTGAGE RATES (CONT.) 175
5.3 TESTING FOR AUTOCORRELATION 176
5.3.1 OTHER TESTS 177
5.4 ESTIMATION WITH FIRST-ORDER AUTOCORRELATED DATA 178
IMAGE 4
VIII CONTENTS
5.4.1 MODEL 1: STRICTLY EXOGENOUS REGRESSORS AND AUTOCORRE- LATED
DISTURBANCES 179
THE OLS STRATEGY 181
THE TRANSFORMATION STRATEGY 183
THE FGLS STRATEGY 185
COMPARISON OF ESTIMATES OF MODEL 1 188
5.4.2 MODEL 2: A LAGGED DEPENDENT VARIABLE AND I.I.D. ERRORS . . . 18!)
5.4.3 MODEL 3: A LAGGED DEPENDENT VARIABLE WITH AR(1) ERRORS . . 102
THE TRANSFORMATION STRATEGY 193
THE IV STRATEGY 195
5.5 ESTIMATING THE MORTGAGE RATE EQUATION . 19(I
5.6 POINTS TO REMEMBER L98
6 UNIVARIATE TIME-SERIES MODELS 201
6.1 THE GENERAL LINEAR PROCESS 202
6.2 LAG POLYNOMIALS: NOTATION OR PRESTIDIGITATION? 203
6.3 THE ARMA MODEL 205
6.4 STATIONARITY AND INVERTIBILITY . 208
6.5 WHAT CAN ARMA MODELS DO? 210
6.6 POINTS TO REMEMBER 214
6.7 LOOKING AHEAD 215
7 MODELING A REAL-WORLD TIME SERIES 217
7.1 GETTING READY TO MODEL A TIME SERIES 218
7.2 THE BOX-JENKINS APPROACH 226
7.3 SPECIFYING AN ARMA MODEL 228
7.3.1 STEP 1: INDUCE STATIONARITY (ARMA BECOMES AR1MA) . . . 228
7.3.2 STEP 2: MIND YOUR P'S AND Q'S 233
7.4 ESTIMATION 243
7.5 LOOKING FOR TROUBLE: MODEL DIAGNOSTIC CHECKING 253
7.5.1 OVERFITTING 253
7.5.2 TESTS OF THE RESIDUALS 254
IMAGE 5
CONTENTS IX
7.6 FORECASTING WITH ARIMA MODELS 257
7.7 COMPARING FORECASTS 262
7.8 POINTS TO REMEMBER 266
7.9 WHAT HAVE WE LEARNED SO FAR? 267
7.10 LOOKING AHEAD 269
8 TIME-VARYING VOLATILITY 271
8.1 EXAMPLES OF TIME-VARYING VOLATILITY 272
8.2 ARCH: A MODEL OF TIME-VARYING VOLATILITY 277
8.3 EXTENSIONS TO THE ARCH MODEL 285
8.3.1 GARCH: LIMITING THE ORDER OF THE MODEL 286
8.3.2 OTHER EXTENSIONS 292
ASYMMETRIC RESPONSES TO "NEWS" 293
VARIATIONS IN VOLATILITY AFFECT THE MEAN OF THE OBSERVABLE
SERIES 295
XONNONNAL ERRORS . 296
ODDS AND ENDS 296
8.4 POINTS TO REMEMBER 298
9 MODELS OF MULTIPLE TIME SERIES 299
9.1 VECTOR AUTOREGRESSIONS 300
9.1.1 THREE TYPES OF VARS 302
9.2 A YAR OF THE U.S. MACROECOUOMY 303
9.2.1 USING STATA TO ESTIMATE A REDUCED-FORM VAR 305
9.2.2 TESTING A VAR FOR STATIONARITY 309
OTHER TESTS 312
9.2.3 FORECASTING 316
EVALUATING A VAR FORECAST 325
9.3 WHO'S ON FIRST? 329
9.3.1 CROSS CORRELATIONS 33(1
9.3.2 SUMMARIZING TEMPORAL RELATIONSHIPS IN A VAR 335
GRANGER CAUSALITY 336
IMAGE 6
X CONTENTS
HOW TO IMPOSE ORDER 339
FEVDS 343
USING STATA TO CALCULATE IRFS AND FEVDS 343
9.4 SVARS 356
9.4.1 EXAMPLES OF A SHORT-RUN SVAR 359
9.4.2 EXAMPLES OF A LONG-RUN SVAR . 368
9.5 POINTS TO REMEMBER 371
9.6 LOOKING AHEAD 372
10 MODELS OF NONSTATIONARY T I ME SERIES 375
10.1 TRENDS AND UNIT ROOTS 376
10.2 TESTING FOR UNIT ROOTS 380
10.3 COINTEGRATION: LOOKING FOR A LONG-TERM RELATIONSHIP . 385
10.4 COINTEGRATING RELATIONSHIPS AND VECMS 387
10.4.1 DETERMINISTIC COMPONENTS IN THE VECM 391
10.5 FROM INTUITION TO VECM: AN EXAMPLE 392
STEP 1: CONFIRM THE UNIT ROOT 397
STEP 2: IDENTIFY THE NUMBER OF LAGS 399
STEP 3: IDENTIFY THE NUMBER OF COINTEGRATING RELATIONSHIPS . 400
STEP 4: FIT A VECM 404
STEP 5: TEST FOR STABILITY AND WHITE-NOISE RESIDUALS 414
STEP 6: REVIEW THE MODEL IMPLICATIONS FOR REASONABLENESS . 415
10.6 POINTS TO REMEMBER 422
10.7 LOOKING AHEAD 422
11 CLOSING OBSERVATIONS 425
11.1 MAKING SENSE OF IT ALL 425
11.2 WHAT DID WE MISS? 426
11.2.1 ADVANCED TIME-SERIES TOPICS 427
11.2.2 ADDITIONAL STATA TIME-SERIES FEATURES . . . . . . . . . . . .
429
DATA MANAGEMENT TOOLS AND UTILITIES 429
UNIVARIATE MODELS 430
IMAGE 7
CONTENTS XI
MULTIVARIATE MODELS 430
11.3 FAREWELL 431
REFERENCES 433
AUTHOR INDEX 437
SUBJECT INDEX 439 |
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| language | English |
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| spelling | Becketti, Sean Verfasser (DE-588)170149366 aut Introduction to time series using Stata Sean Becketti College Station, Tex. Stata Press 2013 xxv, 443 Seiten Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier A Stata Press publication Includes bibliographical references Mathematical statistics / Data processing Datenverarbeitung Stata (DE-588)4617285-3 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Stata (DE-588)4617285-3 s DE-604 SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025423718&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Becketti, Sean Introduction to time series using Stata Mathematical statistics / Data processing Datenverarbeitung Stata (DE-588)4617285-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4617285-3 (DE-588)4067486-1 |
| title | Introduction to time series using Stata |
| title_auth | Introduction to time series using Stata |
| title_exact_search | Introduction to time series using Stata |
| title_full | Introduction to time series using Stata Sean Becketti |
| title_fullStr | Introduction to time series using Stata Sean Becketti |
| title_full_unstemmed | Introduction to time series using Stata Sean Becketti |
| title_short | Introduction to time series using Stata |
| title_sort | introduction to time series using stata |
| topic | Mathematical statistics / Data processing Datenverarbeitung Stata (DE-588)4617285-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Mathematical statistics / Data processing Datenverarbeitung Stata Zeitreihenanalyse |
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