Time series econometrics: using Microfit 5.0
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2009
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| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXII, 563 S. Ill., graph. Darst. 25 cm |
| ISBN: | 9780199563531 0199563535 |
Internformat
MARC
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| 100 | 1 | |a Pesaran, Bahram |e Verfasser |0 (DE-588)170392341 |4 aut | |
| 245 | 1 | 0 | |a Time series econometrics |b using Microfit 5.0 |c Bahram Pesaran and M. Hashem Pesaran |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2009 | |
| 300 | |a XXII, 563 S. |b Ill., graph. Darst. |c 25 cm | ||
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| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Microfit (Computer program) | |
| 650 | 4 | |a Econometrics / Computer programs | |
| 650 | 4 | |a Time-series analysis / Computer programs | |
| 650 | 7 | |a Microfit (computerprogramma) |2 gtt | |
| 650 | 7 | |a Tijdreeksen |2 gtt | |
| 650 | 7 | |a Econometrie |2 gtt | |
| 650 | 7 | |a Zeitreihenanalyse |2 stw | |
| 650 | 7 | |a Ökonometrie |2 stw | |
| 650 | 7 | |a Software |2 stw | |
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| 700 | 1 | |a Pesaran, M. Hashem |d 1946- |e Verfasser |0 (DE-588)122674146 |4 aut | |
| 856 | 4 | 2 | |m HBZ Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022562830&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1804145707326636032 |
|---|---|
| adam_text | Titel: Time series econometrics
Autor: Pesaran, Bahram
Jahr: 2009
Contents
I Introduction to Microfit 1
1 Introduction 3
1.1 What is Microfit?................................. 3
1.2 New features of Microfit 5.0 ........................... 3
1.2.1 New functions and commands...................... 5
1.2.2 Single equation estimation techniques.................. 5
1.2.3 System equation estimation techniques................. 6
1.3 Tutorial lessons................................... 7
1.4 Other features of Microfit 5.0........................... 8
1.4.1 Data management............................. 8
1.4.2 Data transformations........................... 8
1.4.3 High-resolution graphics......................... 8
1.4.4 Batch Operations............................. 8
1.4.5 General statistics............................. 9
1.4.6 Dynamic Simulation............................ 9
1.4.7 Other Single equation estimation techniques.............. 9
1.4.8 Model respecification........................... 10
1.4.9 Diagnostic tests and model selection criteria.............. 10
1.4.10 Variable addition and variable deletion tests.............. 11
1.4.11 Cointegration tests............................ 11
1.4.12 Testing for unit roots........................... 11
1.4.13 Tests of linear and non-linear restrictions................ 11
1.4.14 Non-nested tests.............................. 11
1.4.15 Static and dynamic univariate forecasts................. 11
1.5 Installation and System configuration ...................... 12
1.6 System requirements for Microfit 5-0 ...................... 12
2 Installation and Getting Started 13
2.1 Installation..................................... 13
2.1.1 Single user Installation.......................... 13
2.1.2 Network installation ........................... 13
2.2 Starting and ending a Session........................... 14
2.2.1 Running Microfit............................. 14
CONTENTS
2.2.2 Quitting Microfit............................. 14
2.3 Using Windows, menus and buttons ....................... 14
2.3.1 The main window............................. 14
2.3.2 Main Menü bar.............................. 14
2.3.3 Buttons .................................. 16
2.4 The Variables window............................... 17
2.5 The Data window................................. 18
2.5.1 Program options ............................. 18
2.5.2 Help.................................... 19
II Processing and Data Management 21
3 Inputting and Saving Data Files 23
3.1 Change data dimension.............................. 23
3.2 Inputting data................................... 23
3.2.1 Inputting data from the keyboard.................... 24
3.2.2 Loading an existing data set....................... 26
3.2.3 Inputting data from a raw data (ASCII) file.............. 26
3.2.4 Inputting data from a special Microfit file saved previously ..... 27
3.2.5 Inputting data from an Excel file.................... 27
3.2.6 Inputting data from CSV files...................... 28
3.2.7 Inputting data from AREMOS (TSD) files............... 28
3.2.8 Input new data from the clipboard into Microfit ........... 28
3.2.9 Adding data from the clipboard into Microfit workspace....... 29
3.2.10 Inputting daily data ........................... 30
3.3 Adding two data files............................... 30
3.3.1 Adding two special Microfit files containing the same variables ... 31
3.3.2 Adding two special Microfit files containing different variables . ? ? 33
3.4 Using the Commands and Data Transformations box ............. 34
3.5 Saving data..................................... 34
3.5.1 Save as a special Microfit file ...................... 34
3.5.2 Save as an Excel sheet.......................... 35
3.5.3 Save as a comma separated values (CSV) file.............. 35
3.5.4 Save as an AREMOS (TSD) file..................... 36
3.5.5 Save as a raw data (numbers only) file................. 36
3.6 Starting with a new data set........................... 36
4 Data Processing and Preliminary Data Analysis 37
4.1 Creating constant terms, time trends and seasonal dummies.......... ^
4.1.1 Creating a constant (intercept) term.................. 39
4.1.2 Creating a time trend........................... 39
4.1.3 Creating (0,1) seasonal dummies .................... 39
4.1.4 Creating centred seasonal dvimmies................... *ß
CONTENTS
xi
4.2
4.3
4.1.5 Creating seasonal dummies relative to the last season......... 40
Typing formulae in Microfit............................ 41
4.2.1 Printing, saving, viewing, and copying files............... 43
Using built-in functions in Microfit........................ 43
4.3.1
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
4.3.7
4.3.8
4.3.9
4.3.10
4.3.11
4.3.12
4.3.13
4.3.14
4.3.15
4.3.16
4.3.17
4.3.18
4.3.19
4.3.20
4.3.21
4.3.22
4.3.23
4.3.24
4.3.25
4.3.26
4.3.27
4.3.28
4.3.29
4.3.30
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on ABS .............................. 43
on COS .............................. 43
on CPHI.............................. 44
on CSUM............................. 44
on EXP .............................. 44
on GDL.............................. 44
onHPF .............................. 45
onlNVNORM.......................... 45
on LOG .............................. 46
on MAX.............................. 46
on MAV.............................. 46
onMEAN............................. 46
on MIN .............................. 47
on NORMAL........................... 47
on ORDER............................ 47
on PHI............................... 47
on PTTEST............................ 48
onRANK............................. 48
on RATE ............................. 48
onREC-MAX.......................... 49
on REC-MIN........................... 49
on ROLL_MAX ......................... 49
onROLLJVHN.......................... 49
on SIGN.............................. 50
onSIN............................... 50
on SORT.............................. 50
on SQRT.............................. 51
onSTD .............................. 51
on SUM.............................. 51
on UNIFORM.......................... 51
4.4 Using commands in Microfit ........................... 52
4.4.1 Command ADD............................. 52
4.4.2 Command ADF ............................. 52
4.4.3 Command ADF_GLS.......................... 54
4.4.4 Command ADFJMAX......................... 55
4.4.5 Command ADF-WS........................... 55
4.4.6 Command BATCH........................... 56
4.4.7 Command CCA ............................. 57
4.4.8 Command COR............................. 58
4.4.9 Command DELETE........................... 58
xii CONTENTS
4.4.10 Command DF_PP............................ 58
4.4.11 Command ENTITLE.......................... 59
4.4.12 Command FILL _FORWARD..................... 59
4.4.13 Command FILLJVIISSING ...................... 60
4.4.14 Command HIST............................. 60
4.4.15 Command KEEP............................. 60
4.4.16 Command KPSS............................. 61
4.4.17 Command LIST ............................. 61
4.4.18 Command NONPARM......................... 61
4.4.19 Command PCA ............................. 63
4.4.20 Command PLOT............................. 63
4.4.21 Command REORDER......................... 64
4.4.22 Command RESTORE.......................... 64
4.4.23 Command SAMPLE .......................... 64
4.4.24 Command SCATTER.......................... 65
4.4.25 Command SIM.............................. 65
4.4.26 Command SIMB............................. 66
4.4.27 Command SPECTRUM........................ 66
4.4.28 Command TITLE............................ 67
4.4.29 Command XPLOT ........................... 67
5 Printing/Saving Results and Graphs 68
5.1 Result screens ................................... 68
5.1.1 On-line printing of results........................ 68
5.1.2 Saving results............................... 68
5.2 Print/save/retrieve graphs ............................ 69
5.2.1 Altering the display of graphs...................... 70
5.2.2 Printing graphs.............................. 71
5.2.3 Saving graphs............................... 71
5.2.4 Retrieval of graphic files......................... 72
5.2.5 Capturing graphs onto the clipboard.................. 72
5.3 Exercises using graphs............................... 72
5.3.1 Exercise 5.1................................ 72
5.3.2 Exercise 5.2................................ 72
III Estimation Menüs 73
6 Single-Equation Options 75
6.1 The classical normal linear regression model .................. 75
6.1.1 Testing the assumptions of the classical model............. 76
6.1.2 Estimation of the classical linear regression model........... 77
6.1.3 Testing zero restrictions and reporting probability values....... 79
6.2 The maximum likelihood approach........................ 79
CONTENTS xiii
6.2.1 Newton-Raphson algorithm ....................... 80
6.2.2 Properties of maximum likelihood estimators.............. 80
6.2.3 Likelihood-based tests .......................... 81
6.3 Estimation menus in Microfit........................... 83
6.4 Single Equation Estimation Menü........................ 84
6.5 The Linear Regression Menü........................... 85
6.5.1 Specification of a linear regression equation .............. 85
6.5.2 Specification of the estimation period.................. 87
6.6 Ordinary Least Squares Option.......................... 88
6.6.1 Tests of residual serial correlation.................... 88
6.6.2 Ramsey s RESET test for functional form misspecification...... 89
6.6.3 The normality test............................ 89
6.6.4 Heteroscedasticity test.......................... 89
6.6.5 Predictive failure test........................... 89
6.6.6 Chow s test of the stability of regression coefficients.......... 90
6.6.7 Measures of leverage........................... 90
6.7 Generalized instrumental variable method Option................ 90
6.8 AR errors (exact ML) Option........................... 92
6.9 AR errors (Cochrane-Orcutt) Option....................... 93
6.10 AR errors (Gauss-Newton) Option........................ 94
6.11 IV with AR errors (Gauss-Newton) option ................... 94
6.12 MA errors (exact ML) option........................... 96
6.13 IV with MA errors option............................. 96
6.13.1 Specification of initial estimates for the parameters of the AR/MA
error process................................ 97
6.14 Recursive regression options............................ 97
6.14.1 Recursive OLS Regression Results Menü................ 98
6.15 Rolling Linear Regression Menü......................... 98
6.15.1 Rolling Regression Results Menü.................... 99
6.16 Non-Linear Regression Menü........................... 99
6.16.1 Specification of a non-linear regression equation............ 101
6.16.2 Specification of initial parameter estimates............... 102
6.16.3 Estimation results for the non-linear regression equation....... 102
6.17 Phillips-Hansen Estimation Menü ........................ 102
6.18 ARDL approach to cointegration......................... 104
6.18.1 Specification of an ARDL regression equation............. 104
6.18.2 ARDL Order Selection Menü ...................... 105
6.18.3 Post ARDL Model Selection Menü................... 105
6.18.4 ARDL Forecast Menü .......................... 106
6.19 Logit and Probit modeis.............................. 106
6.19.1 Specification of the Logit/Probit model................. 107
6.19.2 Logit/Probit Estimation Menü...................... 107
6.19.3 Estimation results for Logit and Probit options............ 108
6.19.4 Logit/Probit Post Estimation Menü................... 108
xiv CONTENTS
6.20 Post Regression Menü............................... 109
6.21 Display/Save Residuais and Fitted Values Menü................ 110
6.22 Standard, White and Newey-West Adjusted Variance Menü.......... 111
6.23 Hypothesis Testing Menü............................. 112
7 Multiple Equation Options 117
7.1 The canonical multivariate model ........................ 117
7.1.1 The log-likelihood function of the multivariate model......... 119
7.2 General guidelines................................. 119
7.3 System Estimation Menü............................. 121
7.4 Unrestricted VAR option............................. 122
7.4.1 Unrestricted VAR Post Estimation Menü ............... 123
7.4.2 Unrestricted VAR Dynamic Response Analysis Menü......... 124
7.4.3 VAR Hypothesis Testing Menü ..................... 125
7.4.4 Multivariate Forecast Menü....................... 127
7.5 Cointegrating VAR options............................ 128
7.5.1 Specification of the cointegrating VAR model............. 130
7.5.2 Cointegrating VAR Post Estimation Menü............... 131
7.5.3 Long-Run Structural Modelling Menü.................. 132
7.5.4 Impulse Response Analysis and Forecasting Menü........... 133
7.5.5 Beveridge-Nelson Trend/Cycle Decomposition............. 136
7.5.6 Trend/Cycle Decomposition Results Menü............... 136
7.6 Cointegrating VARX option............................ 136
7.7 SURE options................................... 138
7.7.1 Unrestricted SURE options ....................... 139
7.7.2 Restricted SURE options......................... I40
7.7.3 SURE Post Estimation Menü...................... 141
8 Volatility Modelling Options 143
8.1 Introduction.................................... 143
8.2 Historical approaches to volatility measurement................. 144
8.2.1 RiskMetrics? (JP Morgan) method.................. 144
8.2.2 Econometric approaches......................... 14°
8.3 Univariate GARCH modeis............................ 145
8.4 Multivariate GARCH modeis........................... u6
8.4.1 DCC and t-DCC Multivariate Volatility Models............ 147
8.5 Volatility Modelling Menü............................. 148
8.6 Univariate GARCH Estimation Menü...................... 149
8.6.1 Specification of the GARCH, AGARCH and EGARCH modeis ... 149
8.6.2 Specification of the initial parameter values for GARCH, AGARCH
and EGARCH modeis.......................... 15°
8.6.3 Estimation results for the GARCH-M options............. 150
8.7 Multivariate GARCH Menü............................ 151
8.7.1 Estimation results for the MGARCH.................. 152
CONTENTS xv
8.8 Multivariate GARCH Post Estimation Menü.................. 153
8.8.1 Testing the Validity of Multivariate GARCH Menü.......... 154
8.8.2 Compute the VaR of a portfolio..................... 154
IV Tutorial Lessons 157
9 Lessons in Data Management 159
9.1 Lesson 9.1: Reading in the raw data file UKSTOCK.DAT........... 159
9.2 Lesson 9.2: Saving your current data set as a special Microfit file....... 160
9.3 Lesson 9.3: Reading in the special Microfit file UKSTOCK.FIT........ 160
9.4 Lesson 9.4: Combining two special Microfit files containing different variables 161
9.5 Lesson 9.5: Combining two special Microfit files containing the same variables 161
9.6 Lesson 9.6: Extending the sample period of a special Microfit file....... 162
9.7 Lesson 9.7: Reading the CSV file UKCON.CSV into Microfit ........ 162
9.8 Lesson 9.8: Reading the Excel file DAILYFUTURES.XLS into Microfit ... 163
9.9 Lesson 9.9: Saving the DAILYFUTURES.XLS file excluding missing values . 163
9.10 Exercises in data management........................... 164
9.10.1 Exercise 9.1................................ 164
9.10.2 Exercise 9.2................................ 164
9.10.3 Exercise 9.3................................ 164
9.10.4 Exercise 9.4................................ 165
10 Lessons in Data Processing 166
10.1 Lesson 10.1: Interactive data transformations.................. 166
10.2 Lesson 10.2: Doing data transformations using the BATCH command . . . 167
10.3 Lesson 10.3: Adding titles (descriptions) to variables.............. 168
10.4 Lesson 10.4: Creating dummy variables..................... 169
10.5 Lesson 10.5: Plotting variables against time and/or against each other .... 171
10.6 Lesson 10.6: The use of command XPLOT in generating probability density
function....................................... 173
10.7 Lesson 10.7: Histogram of US stock market returns .............. 174
10.8 Lesson 10.8: Hodrick-Prescott filter applied to UK GDP............ 175
10.9 Lesson 10.9: Summary statistics and correlation coefficients of US and UK
Output growths................................... 177
10.10 Lesson 10.10: Autocorrelation coefficients of US Output growth........ 178
10.11 Lesson 10.11: Spectral density function of the US Output growth ...... 179
10.12 Lesson 10.12: Constructing a geometrically declining distributed lag variable:
using the SIM command............................. 181
10.13 Lesson 10.13: Computation of OLS estimators using formulae and commands 182
10.14 Lesson 10.14: Construction of indices of effective exchange rates and foreign
prices........................................ 185
10.15 Lesson 10.15: Non-parametric density estimation of futures returns ..... 188
xvi CONTENTS
10.16 Lesson 10.16: Principal components analysis of US macro-economic time
series ........................................ 191
10.17 Lesson 10.17: Canonical correlation analysis of bond and equity futures . . . 194
10.18 Exercises in data processing............................ 196
10.18.1 Exercise 10.1................................ 196
10.18.2 Exercise 10.2................................ 196
10.18.3 Exercise 10.3................................ 196
10.18.4 Exercise 10.4................................ 196
11 Lessons in Linear Regression Analysis 197
11.1 Lesson 11.1: OLS estimation of simple regression modeis ........... 197
11.2 Lesson 11.2: Two alternative methods of testing linear restrictions...... 202
11.3 Lesson 11.3: Estimation of long-run effects and mean lags........... 206
11.4 Lesson 11.4: The multicollinearity problem................... 208
11.5 Lesson 11.5: Testing common factor restrictions ................ 212
11.6 Lesson 11.6: Estimation of regression modeis with serially correlated errors . 213
11.7 Lesson 11.7: Estimation of a surprise consumption function: an example of
two-step estimation................................ 217
11.8 Lesson 11.8: An example of non-nested hypothesis testing........... 219
11.9 Lesson 11.9: Testing linear versus log-linear modeis .............. 220
11.10 Lesson 11.10: Testing for exogeneity: computation of the Wu-Hausman
statistic....................................... 222
ll.llLessonll.il: Recursive prediction of US monthly excess returns....... 224
11.12 Lesson 11.12: Rolling regressions and the Lucas critique............ 228
11.13 Exercises in linear regression analysis ...................... 230
11.13.1 Exercise 11.1................................ 230
11.13.2 Exercise 11.2................................ 231
11.13.3 Exercise 11.3................................ 231
11.13.4 Exercise 11.4................................ 231
11.13.5 Exercise 11.5................................ 231
11.13.6 Exercise 11.6................................ 231
12 Lessons in Univariate Time-Series Analysis 232
12.1 Lesson 12.1: Using the ADF command to test for unit roots......... 233
12.2 Lesson 12.2: Spectral analysis of US Output growth .............. 237
12.3 Lesson 12.3: Using an ARMA model for forecasting US Output growth . . ¦ 243
12.4 Lesson 12.4: Alternative measures of persistence of shocks to US real GNP . 246
12.5 Lesson 12.5: Non-stationarity and structuraJ. breaks in real GDP ...... 249
12.6 Lesson 12.6: Unit roots in US nominal wages and the stock market crash . . 251
12.7 Exercises in univariate time-series analysis ................... 254
12.7.1 Exercise 12.1 ............................... 254
12.7.2 Exercise 12.2................................ 254
12.7.3 Exercise 12.3................................ 254
CONTENTS xvii
13 Lessons in Non-Linear Estimation 255
13.1 Lesson 13.1: Non-linear estimation of Cobb-Douglas production function . . 255
13.2 Lesson 13.2: Estimation of Euler equations by the NLS-IV method ..... 257
13.3 Lesson 13.3: Estimation of Almon distributed lag modeis........... 261
13.4 Lesson 13.4: Estimation of a non-linear Phillips curve............. 263
13.5 Lesson 13.5: Estimating a non-linear Phillips curve with serially correlated
errors........................................ 267
13.6 Exercises in non-linear estimation ........................ 269
13.6.1 Exercise 13.1................................ 269
13.6.2 Exercise 13.2................................ 269
13.6.3 Exercise 13.3................................ 269
13.6.4 Exercise 13.4................................ 269
14 Lessons in Probit and Logit Estimation 270
14.1 Lesson 14.1: Modelling the choice of fertilizer use by Philippine farmers . . . 270
14.1.1 Forecasting with Probit/Logit modeis.................. 273
14.2 Lesson 14.2: Fertilizer use model estimated over a sub-sample of farmers . . 274
14.3 Exercises in Logit/Probit estimation....................... 276
14.3.1 Exercise 14.1................................ 276
14.3.2 Exercise 14.2................................ 276
15 Lessons in VAR Modelling 277
15.1 Lesson 15.1: Selecting the order of the VAR................... 277
15.2 Lesson 15.2: Testing for the presence of oil shock dummies in Output
equations...................................... 281
15.3 Lesson 15.3: International transmission of Output shocks ........... 282
15.4 Lesson 15.4: Contemporaneous correlation of Output shocks.......... 283
15.5 Lesson 15.5: Forecasting Output growths using the VAR............ 285
15.6 Lesson 15.6: Impulse responses of the effects of Output growth shocks .... 286
15.7 Exercises in VAR modelling............................ 288
15.7.1 Exercise 15.1................................ 288
15.7.2 Exercise 15.2................................ 288
15.7.3 Exercise 15.3................................ 289
16 Lessons in Cointegration Analysis 290
16.1 Lesson 16.1: Testing for cointegration when the cointegrating coefficients are
known........................................ 291
16.2 Lesson 16.2: A residual-based approach to testing for cointegration ..... 294
16.3 Lesson 16.3: Testing for cointegration: Johansen ML approach........ 296
16.4 Lesson 16.4: Testing for cointegration in modeis with 1(1) exogenous
variables ...................................... 302
16.5 Lesson 16.5: Long-run analysis of consumption, income and inflation: the
ARDL approach.................................. 308
16.6 Lesson 16.6: Great ratios and long-run money demand in the US....... 312
xvüi CONTENTS
16.7 Lesson 16.7: Application of the cointegrating VAR analysis to the UK term
structure of interest rates............................. 326
16.8 Lesson 16.8: Canonical correlations and cointegration analysis ........ 334
16.9 Exercises in cointegration analysis........................ 337
16.9.1 Exercise 16.1................................ 337
16.9.2 Exercise 16.2................................ 337
16.9.3 Exercise 16.3................................ 337
16.9.4 Exercise 16.4................................ 337
16.9.5 Exercise 16.5................................ 337
17 Lessons in VARX Modelling and Trend/Cycle Dec. 339
17.1 Lesson 17.1: Testing the long-run validity of PPP and IRP hypotheses using
UKdata ......................................339
17.2 Lesson 17.2: A macroeconomic model for Indonesia ..............347
17.3 Lesson 17.3: Testing for over-identifying restrictions in the Indonesian model 351
17.4 Lesson 17.4: Forecasting UK inflation......................356
17.5 Lesson 17.5: Permanent and transitory components of Output and
consumption in a small model of the US economy ...............360
17.6 Lesson 17.6: The trend-cycle decomposition of interest rates .........366
17.7 Lesson 17.7: The US equity market and the UK economy...........370
17.8 Exercises in VARX modelling...........................373
17.8.1 Exercise 17.1................................373
17.8.2 Exercise 17.2................................373
17.8.3 Exercise 17.3................................373
17.8.4 Exercise 17.4................................373
18 Lessons in SURE Estimation 374
18.1 Lesson 18.1: A restricted bivariate VAR model of patents and Output growth
in the US...................................... 374
18.2 Lesson 18.2: Estimation of Grunfeld-Griliches Investment equations..... 376
18.3 Lesson 18.3: Testing cross-equation restrictions after SURE estimation .... 378
18.4 Lesson 18.4: Estimation of a static almost ideal demand System....... 379
18.5 Lesson 18.5: Estimation of a New Keynesian three equation model ..... 382
18.6 Lesson 18.6: 2SLS and 3SLS estimation of an exactly identified system ... 384
18.7 Exercises in SURE Estimation.......................... 386
18.7.1 Exercise 18.1................................ 386
18.7.2 Exercise 18.2 ............................... 386
18.7.3 Exercise 18.3 ............................... 386
18.7.4 Exercise 18.4................................ 386
18.7.5 Exercise 18.5................................ 386
18.7.6 Exercise 18.6................................ 386
19.1 Lesson 19.1
19.2 Lesson 19.2
19.3 Lesson 19.3
19.4 Lesson 19.4
19.5 Lesson 19.5
19.6 Lesson 19.6
CONTENTS xix
19 Lessons in Univariate GARCH Modelling 387
Testing for ARCH effects in monthly $/£ exchange rates .... 387
Estimating GARCH modeis for monthly $/£ exchange rate . . 389
Estimating EGARCH modeis for monthly $/£ exchange rate . . 393
Forecasting volatility........................ 395
Modelling volatility in daily exchange rates............ 396
Estimation of GARCH-in-mean modeis of US excess returns . . 398
19.7 Exercises in GARCH modelling.......................... 401
19.7.1 Exercise 19.1................................ 401
19.7.2 Exercise 19.2................................ 402
20 Lessons in Multivariate GARCH Modelling 403
20.1 Lesson 20.1: Estimating DCC modeis for a portfolio of currency futures . . . 403
20.2 Lesson 20.2: Plotting the estimated conditional volatilities and correlations . 407
20.3 Lesson 20.3: Testing for linear restrictions.................... 409
20.4 Lesson 20.4: Testing the validity of the t-DCC model ............. 410
20.5 Lesson 20.5: Forecasting conditional correlations................ 413
20.6 Lesson 20.6: MGARCH applied to a set of OLS residuals........... 414
20.7 Exercises in Multivariate GARCH Estimation.................. 416
20.7.1 Exercise 20.1................................ 416
20.7.2 Exercise 20.2................................ 416
20.7.3 Exercise 20.3................................ 416
V Econometric Methods 417
21 Econometrics of Single Equation Models 419
21.1 Summary statistics and autocorrelation coefficients............... 419
21.1.1 Box-Pierce and Ljung-Box tests..................... 420
21.2 Non-parametric estimation of the density function............... 421
21.3 Estimation of spectral density .......................... 422
21.4 Hodrick-Prescott (HP) filter ........................... 423
21.5 Pesaran-Timmermann non-parametric test of predictive Performance .... 424
21.6 Ordinary least Squares estimates......................... 424
21.6.1 Regression results............................. 425
21.6.2 Diagnostic test statistics (the OLS case)................ 427
21.7 Statistical model selection criteria........................ 430
21.7.1 Akaike Information criterion (AIC)................... 430
21.7.2 Schwarz Bayesian criterion (SBC).................... 431
21.7.3 Hannan and Quinn criterion (HQC)................... 431
21.7.4 Consistency properties of the different model-selection criteria . . . 431
21.8 Non-nested tests for linear regression modeis.................. 432
21.9 Non-nested tests for modeis with different transformations of the dependent
variable....................................... 435
xx CONTENTS
21.9.1 The PE test statistic........................... 435
21.9.2 The Bera-McAleer test statistic..................... 436
21.9.3 The double-length regression test statistic............... 436
21.9.4 The Cox non-nested statistics computed by Simulation........ 437
21.9.5 Sargan and Vuong s likelihood criteria................. 439
21.10 The generalized instrumental variable method ................. 439
21.10.1 Two-stage least Squares.......................... 440
21.10.2 Generalized R2 for IV regressions.................... 441
21.10.3 Sargan s general mis-specification test ................. 441
21.10.4 Sargan s test of residual serial correlation for IV regressions..... 442
21.11 Exact ML/AR estimators............................. 442
21.11.1 The AR(1) case.............................. 444
21.11.2 The AR(2) case.............................. 444
21.11.3 Covariance matrix of the exact ML estimators for the AR(1) and
AR(2) options............................... 445
21.11.4 Adjusted residuals, R2, R2, and other statistics............ 445
21.11.5 Log-likelihood ratio statistics for tests of residual serial correlation . 447
21.12 The Cochrane-Orcutt iterative method ..................... 447
21.12.1 Covariance matrix of the CO estimators................ 449
21.13 ML/AR estimators by the Gauss-Newton method............... 449
21.13.1 AR(m) error process with zero restrictions............... 450
21.14 The IV/AR estimation method.......................... 450
21.14.1 Sargan s general mis-specification test in the case of the IV/AR
option................................... 451
21.14.2 R2,R2,GR2,GR2, and other statistics: AR options........... 452
21.15 Exact ML/MA estimators............................. 453
21.15.1 Covariance matrix ofthe unknown parameters in the MA option . . 455
21.16 The IV/MA estimators.............................. 456
21.16.1 R2,R2,GR2,GR2, and other statistics: MA options.......... 456
21.17 Recursive regressions................................ 457
21.17.1 The CUSUM test............................. 458
21.17.2 The CUSUM of Squares test....................... 458
21.17.3 Recursive coefficients: the OLS option................. 458
21.17.4 Standardized recursive residuals: the OLS option........... 459
21.17.5 Recursive Standard errors: the OLS option............... 459
21.17.6 Recursive estimation: the IV option................... 459
21.17.7 Adaptive coefficients in expectations formation modeis under
incomplete learning............................ 460
21.17.8 Recursive predictions........................... 460
21.18Phi_lips-Hansenfully modified OLS estimators................. 461
21.18.1 Choice of lag Windows _?(s,m)...................... 462
21.18.2 Estimation ofthe variance matrix ofthe FM-OLS estimator..... 462
21.19 Autoregressive distributed lag modeis...................... 463
21.20 Probit and Logit modeis.............................. 465
CONTENTS xxi
21.20.1 Estimating and testing vector functions of ß.............. 467
21.20.2 Fitted probability and fitted discrete values.............. 467
21.20.3 Measures of goodness of fit and related test statistics......... 468
21.20.4 Forecasting with Probit/Logit modeis.................. 468
21.21 Non-linear estimation............................... 469
21.21.1 The non-linear least Squares (NLS) method .............. 470
21.21.2 The non-linear instrumental variables (NL/IV) method........ 470
21.22 Heteroscedasticity-consistent variance estimators................ 471
21.23 Newey-West variance estimators......................... 472
21.24 Variance of vector function of estimators .................... 473
21.25 Wald statistic for testing linear and non-linear restrictions........... 474
21.26 Univariate forecasts in regression modeis .................... 474
21.26.1 Univariate static forecasts........................ 475
21.26.2 Univariate dynamic forecasts....................... 476
21.26.3 Standard errors of univariate forecast errors: the OLS and IV options 476
21.26.4 Forecasts based on non-linear modeis.................. 477
21.26.5 Measures of forecast accuracy...................... 477
22 Econometrics of Multiple Equation Models 479
22.1 Seemingly unrelated regression equations (SURE)............... 480
22.1.1 Maximum likelihood estimation..................... 480
22.2 Three-stage least Squares............................. 482
22.2.1 Testing linear/non-linear restrictions.................. 484
22.2.2 LR statistic for testing whether £ is diagonal............. 484
22.3 System estimation subjeet to linear restrictions................. 485
22.4 Augmented vector autoregressive modeis .................... 487
22.4.1 VAR order selection............................ 488
22.4.2 Testing the deletion of deterministic/exogenous variables....... 489
22.4.3 Testing for block Granger non-causality................. 489
22.5 Impulse response analysis............................. 490
22.5.1 Orthogonalized impulse responses.................... 491
22.5.2 Generalized impulse responses...................... 492
22.6 Forecast error variance decompositions...................... 494
22.6.1 Orthogonalized forecast error variance decomposition......... 494
22.6.2 Generalized forecast error variance decomposition........... 494
22.7 Cointegrating VAR................................. 496
22.7.1 Cointegrating relations.......................... 497
22.8 ML estimation and tests of cointegration.................... 499
22.8.1 Maximum eigenvalue statistic...................... 502
22.8.2 Trace statistic............................... 502
22.8.3 Model selection criteria for choosing the number of cointegrating
relations.................................. 503
22.9 Long-run structural modelling.......................... 504
22.9.1 Identification of the cointegrating relations............... 504
xxü CONTENTS
22.9.2 Estimation of the cointegrating relations under general linear
restrictions.................................505
22.9.3 Log-likelihood ratio statistics for tests of over-identifying restrictions
on the cointegrating relations...................... 508
22.9.4 Impulse response analysis in cointegrating VAR modeis........ 509
22.9.5 Impulse response functions of cointegrating relations......... 510
22.9.6 Persistence profiles for cointegrating relations and speed of
convergence to equilibrium........................ 511
22.10 VARX Models...................................511
22.10.1 The structural VARX model....................... 512
22.10.2 The reduced form VARX model..................... 513
22.10.3 The cointegrated VAR model with 1(1) exogenous variables..... 513
22.10.4 Forecasting and impulse response analysis in VARX modeis..... 517
22.11 Trend/cycle decomposition in VARs....................... 518
22.12 Principal components............................... 521
22.12.1 Selecting the number of PCs or factors................. 522
22.13 Canonical correlations............................... 523
23 Econometrics of Volatility Models 525
23.1 Univariate conditionally heteroscedastic modeis................. 525
23.1.1 GARCH-in-mean modeis......................... 525
23.1.2 ML estimation with Gaussian errors .................. 527
23.1.3 ML estimation with Student s t-distributed errors........... 527
23.1.4 Exponential GARCH-in-Mean modeis.................. 528
23.1.5 Absolute GARCH-in-Mean modeis................... 528
23.1.6 Computational considerations...................... 529
23.1.7 Testing for ARCH (or GARCH) effects................. 529
23.1.8 Residuals, DW, R2 and other statistics................. 529
23.1.9 Forecasting with conditionally heteroscedastic modeis......... 530
23.2 Multivariate conditionally heteroscedastic modeis................ 532
23.2.1 Initialization, estimation and evaluation samples............ 534
23.2.2 Maximum likelihood estimation..................... 534
23.2.3 Simple diagnostic tests of the DCC model............... 537
23.2.4 Forecasting volatilities and conditional correlations.......... 539
Appendix A Size Limitations 541
Appendix B Statistical Tables 543
B.l Upper and lower bound F-test and W-test critical values of Pesaran, Shin
and Smith single-equation cointegration test ..................543
|
| any_adam_object | 1 |
| author | Pesaran, Bahram Pesaran, M. Hashem 1946- |
| author_GND | (DE-588)170392341 (DE-588)122674146 |
| author_facet | Pesaran, Bahram Pesaran, M. Hashem 1946- |
| author_role | aut aut |
| author_sort | Pesaran, Bahram |
| author_variant | b p bp m h p mh mhp |
| building | Verbundindex |
| bvnumber | BV037410353 |
| classification_rvk | QH 237 QH 330 SK 845 |
| ctrlnum | (OCoLC)440158598 (DE-599)BVBBV037410353 |
| dewey-full | 330.15 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.15 |
| dewey-search | 330.15 |
| dewey-sort | 3330.15 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| id | DE-604.BV037410353 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T23:23:44Z |
| institution | BVB |
| isbn | 9780199563531 0199563535 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-022562830 |
| oclc_num | 440158598 |
| open_access_boolean | |
| owner | DE-945 DE-521 |
| owner_facet | DE-945 DE-521 |
| physical | XXII, 563 S. Ill., graph. Darst. 25 cm |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Oxford Univ. Press |
| record_format | marc |
| spelling | Pesaran, Bahram Verfasser (DE-588)170392341 aut Time series econometrics using Microfit 5.0 Bahram Pesaran and M. Hashem Pesaran 1. publ. Oxford [u.a.] Oxford Univ. Press 2009 XXII, 563 S. Ill., graph. Darst. 25 cm txt rdacontent n rdamedia nc rdacarrier Microfit (Computer program) Econometrics / Computer programs Time-series analysis / Computer programs Microfit (computerprogramma) gtt Tijdreeksen gtt Econometrie gtt Zeitreihenanalyse stw Ökonometrie stw Software stw Computergestütztes Verfahren stw Anwendungssoftware (DE-588)4120906-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s Zeitreihenanalyse (DE-588)4067486-1 s Anwendungssoftware (DE-588)4120906-0 s b DE-604 Pesaran, M. Hashem 1946- Verfasser (DE-588)122674146 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022562830&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Pesaran, Bahram Pesaran, M. Hashem 1946- Time series econometrics using Microfit 5.0 Microfit (Computer program) Econometrics / Computer programs Time-series analysis / Computer programs Microfit (computerprogramma) gtt Tijdreeksen gtt Econometrie gtt Zeitreihenanalyse stw Ökonometrie stw Software stw Computergestütztes Verfahren stw Anwendungssoftware (DE-588)4120906-0 gnd Ökonometrie (DE-588)4132280-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4120906-0 (DE-588)4132280-0 (DE-588)4067486-1 |
| title | Time series econometrics using Microfit 5.0 |
| title_auth | Time series econometrics using Microfit 5.0 |
| title_exact_search | Time series econometrics using Microfit 5.0 |
| title_full | Time series econometrics using Microfit 5.0 Bahram Pesaran and M. Hashem Pesaran |
| title_fullStr | Time series econometrics using Microfit 5.0 Bahram Pesaran and M. Hashem Pesaran |
| title_full_unstemmed | Time series econometrics using Microfit 5.0 Bahram Pesaran and M. Hashem Pesaran |
| title_short | Time series econometrics |
| title_sort | time series econometrics using microfit 5 0 |
| title_sub | using Microfit 5.0 |
| topic | Microfit (Computer program) Econometrics / Computer programs Time-series analysis / Computer programs Microfit (computerprogramma) gtt Tijdreeksen gtt Econometrie gtt Zeitreihenanalyse stw Ökonometrie stw Software stw Computergestütztes Verfahren stw Anwendungssoftware (DE-588)4120906-0 gnd Ökonometrie (DE-588)4132280-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Microfit (Computer program) Econometrics / Computer programs Time-series analysis / Computer programs Microfit (computerprogramma) Tijdreeksen Econometrie Zeitreihenanalyse Ökonometrie Software Computergestütztes Verfahren Anwendungssoftware |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=022562830&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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