Applied econometrics: a modern approach using EViews and Microfit
This new econometrics text deals specifically with the use of econometric software. The text takes the reader from the various forms of econometric data (time series, cross sectional and panel), through their formatting in electronic media (eg ASCII) to their transfer to and use in widely used softw...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, N.Y.
Palgrave Macmillan
2007
|
| Ausgabe: | Rev. ed. |
| Schlagworte: | |
| Online-Zugang: | Contributor biographical information Publisher description Table of contents only Inhaltsverzeichnis |
| Zusammenfassung: | This new econometrics text deals specifically with the use of econometric software. The text takes the reader from the various forms of econometric data (time series, cross sectional and panel), through their formatting in electronic media (eg ASCII) to their transfer to and use in widely used software packages--Excel, Microfit and Eviews. Most economics degrees now require students to use relevant software to test econometric models and this text illustrates clearly how this is to be done. |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXV, 397 S. graph. Darst. |
| ISBN: | 0230506402 9780230506404 |
Internformat
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| 100 | 1 | |a Asteriou, Dimitrios |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Applied econometrics |b a modern approach using EViews and Microfit |c Dimitrios Asteriou and Stephen G. Hall |
| 250 | |a Rev. ed. | ||
| 264 | 1 | |a New York, N.Y. |b Palgrave Macmillan |c 2007 | |
| 300 | |a XXV, 397 S. |b graph. Darst. | ||
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| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 520 | 3 | |a This new econometrics text deals specifically with the use of econometric software. The text takes the reader from the various forms of econometric data (time series, cross sectional and panel), through their formatting in electronic media (eg ASCII) to their transfer to and use in widely used software packages--Excel, Microfit and Eviews. Most economics degrees now require students to use relevant software to test econometric models and this text illustrates clearly how this is to be done. | |
| 650 | 4 | |a matematična ekonomija - ekonometrija - modeli - podatki - statistika - linearni modeli - regresijske analize - časovne vrste - informacijska tehnologija - Microfit | |
| 650 | 4 | |a mathematical economy - econometrics - models - data - statistics - linear models - regression analysis - time series - information technology | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Economics |x Statistical methods | |
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Datensatz im Suchindex
| _version_ | 1804137316777721856 |
|---|---|
| adam_text | CONTENTS LIST OF FIGURES XVII LIST OF TABLES XIX PREFACE XXIII
ACKNOWLEDGEMENTS XXV 1 INTRODUCTION 1 WHAT IS ECONOMETRICS? 2 THE STAGES
OF APPLIED ECONOMETRIC WORK 2 PART I STATISTICAL BACKGROUND AND BASIC
DATA HANDLING 5 2 THE STRUCTURE OF ECONOMIC DATA 7 CROSS-SECTIONAL DATA
8 TIME SERIES DATA 8 PANEL DATA 9 3 WORKING WITH DATA: BASIC DATA
HANDUNG 11 LOOKING AT RAW DATA 12 GRAPHICAL ANALYSIS 12 GRAPHS IN MFIT
12 GRAPHS IN EVIEWS 13 SUMMARY STATISTICS 15 SUMMARY STATISTICS IN
MFIT 15 SUMMARY STATISTICS IN EVIEWS 15 COMPONENTS OF A TIME SERIES 16
INDICES AND BASE DATES 1 6 SPLICING TWO INDICES AND CHANGING THE BASE
DATE OF AN INDEX 16 DATA TRANSFORMATIONS * ? CHANGING THE FREQUENCY OF
TIME SERIES DATA 17 NOMINAL VERSUS REAL DATA 17 LOGS 18 DIFFERENCING 18
GROWTH RATES 19 VII VIII CONTENTS PART II THE CLASSICAL LINEAR
REGRESSION MODEL 21 4 SIMPLE REGRESSION 23 INTRODUCTION TO REGRESSION:
THE CLASSICAL LINEAR REGRESSION MODEL (CLRM) 24 WHY DO WE DO
REGRESSIONS? 24 THE CLASSICAL LINEAR REGRESSION MODEL 24 THE ORDINARY
LEAST SQUARES (OLS) METHOD OF ESTIMATION 26 ALTERNATIVE EXPRESSIONS FOR
/3 28 THE ASSUMPTIONS OF THE CLRM 29 GENERAL 29 THE ASSUMPTIONS 30
VIOLATIONS OF THE ASSUMPTIONS 31 PROPERTIES OF THE OLS ESTIMATORS 31
LINEARITY 32 UNBIASEDNESS 33 EFFICIENCY AND BLUENESS 34 CONSISTENCY 36
THE OVERALL GOODNESS OF FIT 37 PROBLEMS ASSOCIATED WITH R 2 38
HYPOTHESIS TESTING AND CONFIDENCE INTERVALS 39 TESTING THE SIGNIFICANCE
OF THE OLS COEFFICIENTS 40 CONFIDENCE INTERVALS 41 HOW TO ESTIMATE A
SIMPLE REGRESSION IN MICROFIT AND EVIEWS 42 SIMPLE REGRESSION IN
MICROFIT 42 SIMPLE REGRESSION IN EVIEWS 42 READING THE EVIEWS SIMPLE
REGRESSION RESULTS OUTPUT 43 PRESENTATION OF REGRESSION RESULTS 44
APPLICATIONS 44 APPLICATION 1: THE DEMAND FUNCTION 44 APPLICATION 2: A
PRODUCTION FUNCTION 45 APPLICATION 3: OKUN S LAW 46 APPLICATION 4: THE
KEYNESIAN CONSUMPTION FUNCTION 46 COMPUTER EXAMPLE: THE KEYNESIAN
CONSUMPTION FUNCTION 47 SOLUTION 47 QUESTIONS AND EXERCISES 52 5
MULTIPLE REGRESSION 56 DERIVATION OF THE MULTIPLE REGRESSION
COEFFICIENTS 57 THE THREE-VARIABLE MODEL 57 THE JT-VARIABLES CASE 58
DERIVATION OF THE COEFFICIENTS WITH MATRIX ALGEBRA 59 THE STRUCTURE OF
THE X X AND X Y MATRICES 60 THE ASSUMPTIONS OF THE MULTIPLE REGRESSION
MODEL 61 THE VARIANCE-COVARIANCE MATRIX OF THE ERRORS 62 PROPERTIES OF
THE MULTIPLE REGRESSION MODEL OLS ESTIMATORS 62 LINEARITY 62
UNBIASEDNESS 63 CONSISTENCY 63 BLUENESS 63 CONTENTS IX R 2 AND ADJUSTED
R 2 65 GENERAL CRITERIA FOR MODEL SELECTION 66 MULTIPLE REGRESSION
ESTIMATION IN MICROFIT AND EVIEWS 67 MULTIPLE REGRESSION IN MICROFIT 67
MULTIPLE REGRESSION IN EVIEWS 67 READING THE EVIEWS MULTIPLE REGRESSION
RESULTS OUTPUT 68 HYPOTHESIS TESTING 68 TESTING INDIVIDUAL COEFFICIENTS
68 TESTING LINEAR RESTRICTIONS 68 THE F-FORM OF THE LIKELIHOOD RATIO
TEST 70 TESTING THE JOINT SIGNIFICANCE OF THE XS 71 F-TEST FOR OVERALL
SIGNIFICANCE IN MICROFIT AND EVIEWS 71 ADDING OR DELETING EXPLANATORY
VARIABLES 72 OMITTED AND REDUNDANT VARIABLES TEST IN EVIEWS 72 OMITTED
AND REDUNDANT VARIABLES TEST IN MICROFIT 73 HOW TO PERFORM THE WALD TEST
IN EVIEWS AND MICROFIT 73 THE R TEST (A SPECIAL CASE OF THE WALD
PROCEDURE) 74 THE LM TEST 74 THE LM TEST IN MICROFIT AND EVIEWS 75
COMPUTER EXAMPLE: WALD, OMITTED AND REDUNDANT VARIABLES TESTS 75 A WALD
TEST OF COEFFICIENT RESTRICTIONS 76 A REDUNDANT VARIABLE TEST 77 AN
OMITTED VARIABLE TEST 78 QUESTIONS AND EXERCISES 79 PART III VIOLATING
THE ASSUMPTIONS OF THE CLRM 83 6 MULTICOLLINEARITY 85 PERFECT
MULTICOLLINEARITY 86 CONSEQUENCES OF PERFECT MULTICOLLINEARITY 87
IMPERFECT MULTICOLLINEARITY 88 CONSEQUENCES OF IMPERFECT
MULTICOLLINEARITY 89 DETECTING PROBLEMATIC MULTICOLHNEARITY 91 SIMPLE
CORRELATION COEFFICIENT 91 R FROM AUXILIARY REGRESSIONS 91 COMPUTER
EXAMPLES 92 EXAMPLE 1: INDUCED MULTICOLLINEARITY 92 EXAMPLE 2: WITH THE
USE OF REAL ECONOMIC DATA 94 QUESTIONS AND EXERCISES 97 7
HETEROSKEDASTICITY 100 INTRODUCTION: WHAT IS HETEROSKEDASTICITY? 101
CONSEQUENCES OF HETEROSKEDASTICITY ON OLS ESTIMATORS 103 A GENERAL
APPROACH 103 A MATHEMATICAL APPROACH 104 DETECTING HETEROSKEDASTICITY
107 THE INFORMAL WAY 107 THE BREUSCH-PAGAN LM TEST 108 X CONTENTS THE
GLESJER LM TEST 111 THE HARVEY-GODFREY LM TEST 112 THE PARK LM TEST 113
THE GOLDFELD-QUANDT TEST 114 WHITE S TEST 116 COMPUTER EXAMPLE:
HETEROSKEDASTICITY TESTS 117 THE BREUSCH-PAGAN TEST 117 THE GLESJER TEST
120 THE HARVEY-GODFREY TEST 120 THE PARK TEST 121 THE GOLDFELD-QUANDT
TEST 121 THE WHITE TEST 123 ENGIE S ARCH TEST 124 COMPUTER EXAMPLE OF
THE ARCH-LM TEST 126 RESOLVING HETEROSKEDASTICITY 126 GENERALIZED (OR
WEIGHTED) LEAST SQUARES 127 COMPUTER EXAMPLE: RESOLVING
HETEROSKEDASTICITY 129 QUESTIONS AND EXERCISES 131 8 AUTOCORRELATION 133
INTRODUCTION: WHAT IS AUTOCORRELATION? 134 WHAT CAUSES AUTOCORRELATION?
134 FIRST AND HIGHER ORDER AUTOCORRELATION 135 CONSEQUENCES OF
AUTOCORRELATION ON THE OLS ESTIMATORS 136 A GENERAL APPROACH 136 A MORE
MATHEMATICAL APPROACH 137 DETECTING AUTOCORRELATION 139 THE GRAPHICAL
METHOD 139 EXAMPLE: DETECTING AUTOCORRELATION USING THE GRAPHICAL METHOD
139 THE DURBIN-WATSON TEST 140 COMPUTER EXAMPLE OF THE DW TEST 143 THE
BREUSCH-GODFREY LM TEST FOR SERIAL CORRELATION 143 COMPUTER EXAMPLE OF
THE BREUSCH-GODFREY TEST 145 DURBIN S H TEST IN THE PRESENCE OF LAGGED
DEPENDENT VARIABLES 145 COMPUTER EXAMPLE OF DURBIN S H TEST 147
RESOLVING AUTOCORRELATION 148 WHEN P IS KNOWN 149 COMPUTER EXAMPLE OF
THE GENERALIZED DIFFERENCING APPROACH 151 WHEN P IS UNKNOWN 151 COMPUTER
EXAMPLE OF THE ITERATIVE PROCEDURE 153 QUESTIONS AND EXERCISES 154
APPENDIX 155 9 MISSPECIFICATION: WRONG REGRESSORS, MEASUREMENT ERRORS
AND WRONG FUNCTIONAL FORMS 156 OMITTING INFLUENTIAL OR INCLUDING
NON-INFLUENTIAL EXPLANATORY VARIABLES 157 CONSEQUENCES OF OMITTING
INFLUENTIAL VARIABLES 157 INCLUDING A NON-INFLUENTIAL VARIABLE 158
CONTENTS XI OMISSION AND INCLUSION OF RELEVANT AND IRRELEVANT VARIABLES
AT THE SAME TIME 159 THE PLUG-IN SOLUTION IN THE OMITTED VARIABLE BIAS
159 VARIOUS FUNCTIONAL FORMS 161 INTRODUCTION 161 LINEAR-LOG FUNCTIONAL
FORM 161 RECIPROCAL FUNCTIONAL FORM 162 POLYNOMIAL FUNCTIONAL FORM 162
FUNCTIONAL FORM INCLUDING INTERACTION TERMS 163 LOG-LINEAR FUNCTIONAL
FORM 164 THE DOUBLE-LOG FUNCTIONAL FORM 164 THE BOX-COX TRANSFORMATION
165 MEASUREMENT ERRORS 166 MEASUREMENT ERROR IN THE DEPENDENT VARIABLE
167 MEASUREMENT ERROR IN THE EXPLANATORY VARIABLE 167 TESTS FOR
MISSPECIFICATION 169 NORMALITY OF RESIDUALS 169 THE RAMSEY RESET TEST
FOR GENERAL MISSPECIFICATION 171 TESTS FOR NON-NESTED MODELS 173
EXAMPLE: THE BOX-COX TRANSFORMATION IN EVIEWS 174 APPROACHES IN CHOOSING
AN APPROPRIATE MODEL 177 THE TRADITIONAL VIEW: AVERAGE ECONOMIC
REGRESSION 177 THE HENDRY GENERAL TO SPECIFIC APPROACH 178 EXERCISES
179 PART IV TOPICS IN ECONOMETRICS 181 10 DUMMY VARIABLES 183
INTRODUCTION: THE NATURE OF QUALITATIVE INFORMATION 184 THE USE OF DUMMY
VARIABLES 185 INTERCEPT DUMMY VARIABLES 185 SLOPE DUMMY VARIABLES 187
THE COMBINED EFFECT OF INTERCEPT AND SLOPE DUMMIES 188 COMPUTER EXAMPLE
OF THE USE OF DUMMY VARIABLES 189 USING A CONSTANT DUMMY _ 190 USING A
SLOPE DUMMY 190 USING BOTH DUMMIES TOGETHER 191 SPECIAL CASES OF THE USE
OF DUMMY VARIABLES 192 USING DUMMY VARIABLES WITH MULTIPLE CATEGORIES
192 USING MORE THAN ONE DUMMY VARIABLE 194 USING SEASONAL DUMMY
VARIABLES 195 COMPUTER EXAMPLE OF DUMMY VARIABLES WITH MULTIPLE
CATEGORIES 196 APPLICATION: THE JANUARY EFFECT IN EMERGING STOCKMARKETS
198 TESTS FOR STRUCTURAL STABILITY 201 THE DUMMY VARIABLE APPROACH 201
THE CHOW TEST FOR STRUCTURAL STABILITY 201 QUESTIONS 202 XII CONTENTS 11
DYNAMIC ECONOMETRIC MODELS 203 DISTRIBUTED LAG MODELS 204 THE KOYCK
TRANSFORMATION 205 THE ALMON TRANSFORMATION 207 OTHER MODELS OF LAG
STRUCTURES 208 AUTOREGRESSIVE MODELS 208 THE PARTIAL ADJUSTMENT MODEL
208 A COMPUTER EXAMPLE OF THE PARTIAL ADJUSTMENT MODEL 209 THE ADAPTIVE
EXPECTATIONS MODEL 211 TESTS OF AUTOCORRELATION IN AUTOREGRESSIVE MODELS
213 EXERCISES 213 12 SIMULTANEOUS EQUATION MODELS 215 INTRODUCTION:
BASIC DEFINITIONS 216 CONSEQUENCES OF IGNORING SIMULTANEITY 217 THE
IDENTIFICATION PROBLEM 217 BASIC DEFINITIONS 217 CONDITIONS FOR
IDENTIFICATION 218 EXAMPLE OF THE IDENTIFICATION PROCEDURE 219 A SECOND
EXAMPLE: THE MACROECONOMIC MODEL OF A CLOSED ECONOMY 219 ESTIMATION OF
SIMULTANEOUS EQUATION MODELS 220 ESTIMATION OF AN EXACTLY IDENTIFIED
EQUATION: THE METHOD OF INDIRECT LEAST SQUARES 221 ESTIMATION OF AN
OVERIDENTINED EQUATION: THE METHOD OF TWO-STAGE LEAST SQUARES 221
EXAMPLE: THE IS-LM MODEL 222 PART V TIME SERIES ECONOMETRICS 227 13
ARIMA MODELS AND THE BOX-JENKINS METHODOLOGY 229 AN INTRODUCTION TO TIME
SERIES ECONOMETRICS 230 ARIMA MODELS 230 STATIONARITY 231 AUTOREGRESSIVE
TIME SERIES MODELS 231 THE AR(1) MODEL 231 THE AR (P) MODEL 233
PROPERTIES OF THE AR MODELS 235 MOVING AVERAGE MODELS 236 THE MA(1)
MODEL 236 THE MA(Q) MODEL 236 INVERTIBILITY IN MA MODELS 237 PROPERTIES
OF THE MA MODELS 238 ARMA MODELS 239 INTEGRATED PROCESSES AND THE ARIMA
MODELS 239 AN INTEGRATED SERIES 239 ARIMA MODELS 240 BOX-JENKINS MODEL
SELECTION 240 IDENTIFICATION 241 CONTENTS XIII ESTIMATION 242 DIAGNOSTIC
CHECKING 242 THE BOX-JENKINS APPROACH STEP BY STEP 243 EXAMPLE: THE
BOX-JENKINS APPROACH 243 QUESTIONS AND EXERCISES 247 14 MODELLING THE
VARIANCE: ARCH-GARCH MODELS 248 INTRODUCTION 249 THE ARCH MODEL 250 THE
ARCH(L) MODEL 251 THE ARCH( 7) MODEL 251 TESTING FOR ARCH EFFECTS 252
ESTIMATION OF ARCH MODELS BY ITERATION 252 ESTIMATING ARCH MODELS IN
EVIEWS 253 A MORE MATHEMATICAL APPROACH 257 THE GARCH MODEL 260 THE
GARCH(P, Q) MODEL 260 THE GARCH(1,1) AS AN INFINITE ARCH(/ ) PROCESS 260
ESTIMATING GARCH MODELS IN EVIEWS 261 ALTERNATIVE SPECIFICATIONS 262 THE
GARCH IN MEAN OR GARCH-M MODEL 263 ESTIMATING GARCH-M MODELS IN EVIEWS
264 THE THRESHOLD GARCH (TGARCH) MODEL 267 ESTIMATING TGARCH MODELS IN
EVIEWS 267 THE EXPONENTIAL GARCH (EGARCH) MODEL 268 ESTIMATING EGARCH
MODELS IN EVIEWS 269 ADDING EXPLANATORY VARIABLES IN THE MEAN EQUATION
270 ADDING EXPLANATORY VARIABLES IN THE VARIANCE EQUATION 270 EMPIRICAL
ILLUSTRATIONS OF ARCH /GARCH MODELS 271 A GARCH MODEL OF UK GDP AND THE
EFFECT OF SOCIO-POLITICAL INSTABILITY 271 QUESTIONS AND EXERCISES 276 15
VECTOR AUTOREGRESSIVE (VAR) MODELS AND CAUSALITY TESTS 278 VECTOR
AUTOREGRESSIVE (VAR) MODELS 279 THE VAR MODEL 279 PROS AND CONS OF THE
VAR MODELS 280 CAUSALITY TESTS 281 THE GRANGER CAUSALITY TEST 281 THE
SIMS CAUSALITY TEST 283 COMPUTER EXAMPLE: FINANCIAL DEVELOPMENT AND
ECONOMIC GROWTH, WHAT IS THE CAUSAL RELATIONSHIP? 283 16
NON-STATIONARITY AND UNIT-ROOT TESTS 287 UNIT ROOTS AND SPURIOUS
REGRESSIONS 288 WHAT IS A UNIT ROOT? 288 SPURIOUS REGRESSIONS 291
EXPLANATION OF THE SPURIOUS REGRESSION PROBLEM 293 TESTING FOR UNIT
ROOTS 295 TESTING FOR THE ORDER OF INTEGRATION 295 XIV CONTENTS THE
SIMPLE DICKEY-FULLER TEST FOR UNIT ROOTS 295 THE AUGMENTED DICKEY-FULLER
(ADF) TEST FOR UNIT ROOTS 297 THE PHILLIPS-PERRON TEST 297 UNIT-ROOT
TESTS IN EVIEWS AND MICROFIT 299 PERFORMING UNIT-ROOT TESTS IN EVIEWS
299 PERFORMING UNIT-ROOT TESTS IN MICROFIT 300 COMPUTER EXAMPLE:
UNIT-ROOT TESTS ON VARIOUS MACROECONOMIC VARIABLES 302 COMPUTER EXAMPLE:
UNIT-ROOT TESTS FOR THE FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH
EXAMPLE 303 QUESTIONS AND EXERCISES 305 17 COINTEGRATION AND
ERROR-CORRECTION MODELS 306 INTRODUCTION: WHAT IS COINTEGRATION? 307
COINTEGRATION: A GENERAL APPROACH 307 COINTEGRATION: A MORE MATHEMATICAL
APPROACH 308 COINTEGRATION AND THE ERROR-CORRECTION MECHANISM (ECM): A
GENERAL APPROACH 309 THE PROBLEM 309 COINTEGRATION (AGAIN) 310 THE
ERROR-CORRECTION MODEL (ECM) 310 ADVANTAGES OF THE ECM 310 COINTEGRATION
AND THE ERROR-CORRECTION MECHANISM: A MORE MATHEMATICAL APPROACH 311 A
SIMPLE MODEL FOR ONLY ONE LAGGED TERM OF X AND Y 311 A MORE GENERAL
MODEL FOR LARGE NUMBERS OF LAGGED TERMS 313 TESTING FOR COINTEGRATION
315 COINTEGRATION IN SINGLE EQUATIONS: THE ENGLE-GRANGER APPROACH 315
DRAWBACKS OF THE EG APPROACH 317 THE EG APPROACH IN EVIEWS AND MICROFIT
318 COINTEGRATION IN MULTIPLE EQUATIONS AND THE JOHANSEN APPROACH 319
ADVANTAGES OF THE MULTIPLE EQUATION APPROACH 320 THE JOHANSEN APPROACH
(AGAIN) 320 THE STEPS OF THE JOHANSEN APPROACH IN PRACTICE 321 THE
JOHANSEN APPROACH IN EVIEWS AND MICROFIT 326 COMPUTER EXAMPLES OF
COINTEGRATION 331 MONETIZATION RATIO 332 TURNOVER RATIO 335 CLAIMS AND
CURRENCY RATIOS 335 A MODEL WITH MORE THAN ONE FINANCIAL DEVELOPMENT
PROXY VARIABLE 337 QUESTIONS AND EXERCISES 340 PART VI PANEL DATA
ECONOMETRICS 341 18 TRADITIONAL PANEL DATA MODELS 343 INTRODUCTION: THE
ADVANTAGES OF PANEL DATA 344 THE LINEAR PANEL DATA MODEL 345 DIFFERENT
METHODS OF ESTIMATION 345 THE COMMON CONSTANT METHOD 345 CONTENTS XV THE
FIXED EFFECTS METHOD 346 THE RANDOM EFFECTS METHOD 347 THE HAUSMAN TEST
348 COMPUTER EXAMPLES WITH PANEL DATA 349 INSERTING PANEL DATA IN EVIEWS
349 ESTIMATING A PANEL DATA REGRESSION 353 19 DYNAMIC HETEROGENEOUS
PANELS 355 BIAS IN DYNAMIC PANELS 356 BIAS IN THE SIMPLE OLS ESTIMATOR
356 BIAS IN THE FIXED EFFECTS MODEL 357 BIAS IN THE RANDOM EFFECTS MODEL
357 SOLUTIONS TO THE BIAS PROBLEM 357 BIAS OF HETEROGENEOUS SLOPE
PARAMETERS 358 SOLUTIONS TO HETEROGENEITY BIAS: ALTERNATIVE METHODS OF
ESTIMATION 359 THE MEAN GROUP ESTIMATOR 359 THE POOLED MEAN GROUP (PMG)
ESTIMATOR 360 APPLICATION: THE EFFECTS OF UNCERTAINTY IN ECONOMIC GROWTH
AND INVESTMENTS 362 EVIDENCE FROM TRADITIONAL PANEL DATA ESTIMATION 362
MEAN GROUP AND POOLED MEAN GROUP ESTIMATES 362 20 NON-STATIONARY PANELS
365 PANEL UNIT-ROOT TESTS 366 THE LEVIN AND LIN (LL) TEST 367 THE IM,
PESARAN AND SHIN (IPS) TEST 368 THE MADDALA AND WU (MW) TEST 369
COMPUTER EXAMPLES OF PANEL UNIT-ROOT TESTS 369 PANEL COINTEGRATION TESTS
371 INTRODUCTION 371 THE KAO TEST 372 THE MCCOSKEY AND KAO TEST 373 THE
PEDRONI TESTS 373 THE LARSSON ET A . TEST 375 COMPUTER EXAMPLES OF PANEL
COINTEGRATION TESTS 376 21 PRACTICALITIES IN USING EVIEWS AND MICROFIT
378 ABOUT MICROFIT * 379 CREATING A FILE AND IMPORTING DATA 379 ENTERING
VARIABLE NAMES 379 COPYING/PASTING DATA 380 DESCRIPTION OF MFIT TOOLS
380 CREATING A CONSTANT TERM 381 BASIC COMMANDS IN MFIT 381 ABOUT EVIEWS
381 CREATING A WORKFILE AND IMPORTING DATA 383 COPYING AND PASTING DATA
383 COMMANDS, OPERATORS AND FUNCTIONS 385 BIBLIOGRAPHY 387 INDEX 419
PPN: 260025402 TITEL: APPLIED ECONOMETRICS : A MODERN APPROACH USING
EVIEWS AND MICROFIT / DIMITRIOS ASTERIOU AND STEPHEN G. HALL. - . -
BASINGSTOKE [U.A.] : PALGRAVE MACMILLAN, 2007 ISBN:
0-230-50640-2(PBK)19.99 PBK : 19.99 : CIP ENTRY (FEB.);
978-0-230-50640-4 BIBLIOGRAPHISCHER DATENSATZ IM SWB-VERBUND
|
| adam_txt |
CONTENTS LIST OF FIGURES XVII LIST OF TABLES XIX PREFACE XXIII
ACKNOWLEDGEMENTS XXV 1 INTRODUCTION 1 WHAT IS ECONOMETRICS? 2 THE STAGES
OF APPLIED ECONOMETRIC WORK 2 PART I STATISTICAL BACKGROUND AND BASIC
DATA HANDLING 5 2 THE STRUCTURE OF ECONOMIC DATA 7 CROSS-SECTIONAL DATA
8 TIME SERIES DATA 8 PANEL DATA 9 3 WORKING WITH DATA: BASIC DATA
HANDUNG 11 LOOKING AT RAW DATA 12 GRAPHICAL ANALYSIS 12 GRAPHS IN MFIT
12 GRAPHS IN EVIEWS ' 13 SUMMARY STATISTICS 15 SUMMARY STATISTICS IN
MFIT 15 SUMMARY STATISTICS IN EVIEWS 15 COMPONENTS OF A TIME SERIES 16
INDICES AND BASE DATES 1 6 SPLICING TWO INDICES AND CHANGING THE BASE
DATE OF AN INDEX 16 DATA TRANSFORMATIONS * ? CHANGING THE FREQUENCY OF
TIME SERIES DATA 17 NOMINAL VERSUS REAL DATA 17 LOGS 18 DIFFERENCING 18
GROWTH RATES 19 VII VIII CONTENTS PART II THE CLASSICAL LINEAR
REGRESSION MODEL 21 4 SIMPLE REGRESSION 23 INTRODUCTION TO REGRESSION:
THE CLASSICAL LINEAR REGRESSION MODEL (CLRM) 24 WHY DO WE DO
REGRESSIONS? 24 THE CLASSICAL LINEAR REGRESSION MODEL 24 THE ORDINARY
LEAST SQUARES (OLS) METHOD OF ESTIMATION 26 ALTERNATIVE EXPRESSIONS FOR
/3 28 THE ASSUMPTIONS OF THE CLRM 29 GENERAL 29 THE ASSUMPTIONS 30
VIOLATIONS OF THE ASSUMPTIONS 31 PROPERTIES OF THE OLS ESTIMATORS 31
LINEARITY 32 UNBIASEDNESS 33 EFFICIENCY AND BLUENESS 34 CONSISTENCY 36
THE OVERALL GOODNESS OF FIT 37 PROBLEMS ASSOCIATED WITH R 2 38
HYPOTHESIS TESTING AND CONFIDENCE INTERVALS 39 TESTING THE SIGNIFICANCE
OF THE OLS COEFFICIENTS 40 CONFIDENCE INTERVALS 41 HOW TO ESTIMATE A
SIMPLE REGRESSION IN MICROFIT AND EVIEWS 42 SIMPLE REGRESSION IN
MICROFIT 42 SIMPLE REGRESSION IN EVIEWS 42 READING THE EVIEWS SIMPLE
REGRESSION RESULTS OUTPUT 43 PRESENTATION OF REGRESSION RESULTS 44
APPLICATIONS 44 APPLICATION 1: THE DEMAND FUNCTION 44 APPLICATION 2: A
PRODUCTION FUNCTION 45 APPLICATION 3: OKUN'S LAW 46 APPLICATION 4: THE
KEYNESIAN CONSUMPTION FUNCTION 46 COMPUTER EXAMPLE: THE KEYNESIAN
CONSUMPTION FUNCTION 47 SOLUTION 47 QUESTIONS AND EXERCISES 52 5
MULTIPLE REGRESSION 56 DERIVATION OF THE MULTIPLE REGRESSION
COEFFICIENTS 57 THE THREE-VARIABLE MODEL 57 THE JT-VARIABLES CASE 58
DERIVATION OF THE COEFFICIENTS WITH MATRIX ALGEBRA 59 THE STRUCTURE OF
THE X'X AND X'Y MATRICES 60 THE ASSUMPTIONS OF THE MULTIPLE REGRESSION
MODEL 61 THE VARIANCE-COVARIANCE MATRIX OF THE ERRORS 62 PROPERTIES OF
THE MULTIPLE REGRESSION MODEL OLS ESTIMATORS 62 LINEARITY 62
UNBIASEDNESS 63 CONSISTENCY 63 BLUENESS 63 CONTENTS IX R 2 AND ADJUSTED
R 2 65 GENERAL CRITERIA FOR MODEL SELECTION 66 MULTIPLE REGRESSION
ESTIMATION IN MICROFIT AND EVIEWS 67 MULTIPLE REGRESSION IN MICROFIT 67
MULTIPLE REGRESSION IN EVIEWS 67 READING THE EVIEWS MULTIPLE REGRESSION
RESULTS OUTPUT 68 HYPOTHESIS TESTING 68 TESTING INDIVIDUAL COEFFICIENTS
68 TESTING LINEAR RESTRICTIONS 68 THE F-FORM OF THE LIKELIHOOD RATIO
TEST 70 TESTING THE JOINT SIGNIFICANCE OF THE XS 71 F-TEST FOR OVERALL
SIGNIFICANCE IN MICROFIT AND EVIEWS 71 ADDING OR DELETING EXPLANATORY
VARIABLES 72 OMITTED AND REDUNDANT VARIABLES TEST IN EVIEWS 72 OMITTED
AND REDUNDANT VARIABLES TEST IN MICROFIT 73 HOW TO PERFORM THE WALD TEST
IN EVIEWS AND MICROFIT 73 THE R TEST (A SPECIAL CASE OF THE WALD
PROCEDURE) 74 THE LM TEST 74 THE LM TEST IN MICROFIT AND EVIEWS 75
COMPUTER EXAMPLE: WALD, OMITTED AND REDUNDANT VARIABLES TESTS 75 A WALD
TEST OF COEFFICIENT RESTRICTIONS 76 A REDUNDANT VARIABLE TEST 77 AN
OMITTED VARIABLE TEST 78 QUESTIONS AND EXERCISES 79 PART III VIOLATING
THE ASSUMPTIONS OF THE CLRM 83 6 MULTICOLLINEARITY 85 PERFECT
MULTICOLLINEARITY 86 CONSEQUENCES OF PERFECT MULTICOLLINEARITY 87
IMPERFECT MULTICOLLINEARITY 88 CONSEQUENCES OF IMPERFECT
MULTICOLLINEARITY 89 DETECTING PROBLEMATIC MULTICOLHNEARITY 91 SIMPLE
CORRELATION COEFFICIENT 91 R FROM AUXILIARY REGRESSIONS ' 91 COMPUTER
EXAMPLES 92 EXAMPLE 1: INDUCED MULTICOLLINEARITY 92 EXAMPLE 2: WITH THE
USE OF REAL ECONOMIC DATA 94 QUESTIONS AND EXERCISES 97 7
HETEROSKEDASTICITY 100 INTRODUCTION: WHAT IS HETEROSKEDASTICITY? 101
CONSEQUENCES OF HETEROSKEDASTICITY ON OLS ESTIMATORS 103 A GENERAL
APPROACH 103 A MATHEMATICAL APPROACH 104 DETECTING HETEROSKEDASTICITY
107 THE INFORMAL WAY 107 THE BREUSCH-PAGAN LM TEST 108 X CONTENTS THE
GLESJER LM TEST 111 THE HARVEY-GODFREY LM TEST 112 THE PARK LM TEST 113
THE GOLDFELD-QUANDT TEST 114 WHITE'S TEST 116 COMPUTER EXAMPLE:
HETEROSKEDASTICITY TESTS 117 THE BREUSCH-PAGAN TEST 117 THE GLESJER TEST
120 THE HARVEY-GODFREY TEST 120 THE PARK TEST 121 THE GOLDFELD-QUANDT
TEST 121 THE WHITE TEST 123 ENGIE'S ARCH TEST 124 COMPUTER EXAMPLE OF
THE ARCH-LM TEST 126 RESOLVING HETEROSKEDASTICITY 126 GENERALIZED (OR
WEIGHTED) LEAST SQUARES 127 COMPUTER EXAMPLE: RESOLVING
HETEROSKEDASTICITY 129 QUESTIONS AND EXERCISES 131 8 AUTOCORRELATION 133
INTRODUCTION: WHAT IS AUTOCORRELATION? 134 WHAT CAUSES AUTOCORRELATION?
134 FIRST AND HIGHER ORDER AUTOCORRELATION 135 CONSEQUENCES OF
AUTOCORRELATION ON THE OLS ESTIMATORS 136 A GENERAL APPROACH 136 A MORE
MATHEMATICAL APPROACH 137 DETECTING AUTOCORRELATION 139 THE GRAPHICAL
METHOD 139 EXAMPLE: DETECTING AUTOCORRELATION USING THE GRAPHICAL METHOD
139 THE DURBIN-WATSON TEST 140 COMPUTER EXAMPLE OF THE DW TEST 143 THE
BREUSCH-GODFREY LM TEST FOR SERIAL CORRELATION 143 COMPUTER EXAMPLE OF
THE BREUSCH-GODFREY TEST 145 DURBIN'S H TEST IN THE PRESENCE OF LAGGED
DEPENDENT VARIABLES 145 COMPUTER EXAMPLE OF DURBIN'S H TEST 147
RESOLVING AUTOCORRELATION 148 WHEN P IS KNOWN 149 COMPUTER EXAMPLE OF
THE GENERALIZED DIFFERENCING APPROACH 151 WHEN P IS UNKNOWN 151 COMPUTER
EXAMPLE OF THE ITERATIVE PROCEDURE 153 QUESTIONS AND EXERCISES 154
APPENDIX 155 9 MISSPECIFICATION: WRONG REGRESSORS, MEASUREMENT ERRORS
AND WRONG FUNCTIONAL FORMS 156 OMITTING INFLUENTIAL OR INCLUDING
NON-INFLUENTIAL EXPLANATORY VARIABLES 157 CONSEQUENCES OF OMITTING
INFLUENTIAL VARIABLES 157 INCLUDING A NON-INFLUENTIAL VARIABLE 158
CONTENTS XI OMISSION AND INCLUSION OF RELEVANT AND IRRELEVANT VARIABLES
AT THE SAME TIME 159 THE PLUG-IN SOLUTION IN THE OMITTED VARIABLE BIAS
159 VARIOUS FUNCTIONAL FORMS 161 INTRODUCTION 161 LINEAR-LOG FUNCTIONAL
FORM 161 RECIPROCAL FUNCTIONAL FORM 162 POLYNOMIAL FUNCTIONAL FORM 162
FUNCTIONAL FORM INCLUDING INTERACTION TERMS 163 LOG-LINEAR FUNCTIONAL
FORM 164 THE DOUBLE-LOG FUNCTIONAL FORM 164 THE BOX-COX TRANSFORMATION
165 MEASUREMENT ERRORS 166 MEASUREMENT ERROR IN THE DEPENDENT VARIABLE
167 MEASUREMENT ERROR IN THE EXPLANATORY VARIABLE 167 TESTS FOR
MISSPECIFICATION 169 NORMALITY OF RESIDUALS 169 THE RAMSEY RESET TEST
FOR GENERAL MISSPECIFICATION 171 TESTS FOR NON-NESTED MODELS 173
EXAMPLE: THE BOX-COX TRANSFORMATION IN EVIEWS 174 APPROACHES IN CHOOSING
AN APPROPRIATE MODEL 177 THE TRADITIONAL VIEW: AVERAGE ECONOMIC
REGRESSION 177 THE HENDRY 'GENERAL TO SPECIFIC APPROACH' 178 EXERCISES
179 PART IV TOPICS IN ECONOMETRICS 181 10 DUMMY VARIABLES 183
INTRODUCTION: THE NATURE OF QUALITATIVE INFORMATION 184 THE USE OF DUMMY
VARIABLES 185 INTERCEPT DUMMY VARIABLES 185 SLOPE DUMMY VARIABLES 187
THE COMBINED EFFECT OF INTERCEPT AND SLOPE DUMMIES 188 COMPUTER EXAMPLE
OF THE USE OF DUMMY VARIABLES 189 USING A CONSTANT DUMMY _ 190 USING A
SLOPE DUMMY 190 USING BOTH DUMMIES TOGETHER 191 SPECIAL CASES OF THE USE
OF DUMMY VARIABLES 192 USING DUMMY VARIABLES WITH MULTIPLE CATEGORIES
192 USING MORE THAN ONE DUMMY VARIABLE 194 USING SEASONAL DUMMY
VARIABLES 195 COMPUTER EXAMPLE OF DUMMY VARIABLES WITH MULTIPLE
CATEGORIES 196 APPLICATION: THE JANUARY EFFECT IN EMERGING STOCKMARKETS
198 TESTS FOR STRUCTURAL STABILITY 201 THE DUMMY VARIABLE APPROACH 201
THE CHOW TEST FOR STRUCTURAL STABILITY 201 QUESTIONS 202 XII CONTENTS 11
DYNAMIC ECONOMETRIC MODELS 203 DISTRIBUTED LAG MODELS 204 THE KOYCK
TRANSFORMATION 205 THE ALMON TRANSFORMATION 207 OTHER MODELS OF LAG
STRUCTURES 208 AUTOREGRESSIVE MODELS 208 THE PARTIAL ADJUSTMENT MODEL
208 A COMPUTER EXAMPLE OF THE PARTIAL ADJUSTMENT MODEL 209 THE ADAPTIVE
EXPECTATIONS MODEL 211 TESTS OF AUTOCORRELATION IN AUTOREGRESSIVE MODELS
213 EXERCISES 213 12 SIMULTANEOUS EQUATION MODELS 215 INTRODUCTION:
BASIC DEFINITIONS 216 CONSEQUENCES OF IGNORING SIMULTANEITY 217 THE
IDENTIFICATION PROBLEM 217 BASIC DEFINITIONS 217 CONDITIONS FOR
IDENTIFICATION 218 EXAMPLE OF THE IDENTIFICATION PROCEDURE 219 A SECOND
EXAMPLE: THE MACROECONOMIC MODEL OF A CLOSED ECONOMY 219 ESTIMATION OF
SIMULTANEOUS EQUATION MODELS 220 ESTIMATION OF AN EXACTLY IDENTIFIED
EQUATION: THE METHOD OF INDIRECT LEAST SQUARES 221 ESTIMATION OF AN
OVERIDENTINED EQUATION: THE METHOD OF TWO-STAGE LEAST SQUARES 221
EXAMPLE: THE IS-LM MODEL 222 PART V TIME SERIES ECONOMETRICS 227 13
ARIMA MODELS AND THE BOX-JENKINS METHODOLOGY 229 AN INTRODUCTION TO TIME
SERIES ECONOMETRICS 230 ARIMA MODELS 230 STATIONARITY 231 AUTOREGRESSIVE
TIME SERIES MODELS 231 THE AR(1) MODEL 231 THE AR (P) MODEL 233
PROPERTIES OF THE AR MODELS 235 MOVING AVERAGE MODELS 236 THE MA(1)
MODEL 236 THE MA(Q) MODEL 236 INVERTIBILITY IN MA MODELS 237 PROPERTIES
OF THE MA MODELS 238 ARMA MODELS 239 INTEGRATED PROCESSES AND THE ARIMA
MODELS 239 AN INTEGRATED SERIES 239 ARIMA MODELS 240 BOX-JENKINS MODEL
SELECTION 240 IDENTIFICATION 241 CONTENTS XIII ESTIMATION 242 DIAGNOSTIC
CHECKING 242 THE BOX-JENKINS APPROACH STEP BY STEP 243 EXAMPLE: THE
BOX-JENKINS APPROACH 243 QUESTIONS AND EXERCISES 247 14 MODELLING THE
VARIANCE: ARCH-GARCH MODELS 248 INTRODUCTION 249 THE ARCH MODEL 250 THE
ARCH(L) MODEL 251 THE ARCH( 7) MODEL 251 TESTING FOR ARCH EFFECTS 252
ESTIMATION OF ARCH MODELS BY ITERATION 252 ESTIMATING ARCH MODELS IN
EVIEWS 253 A MORE MATHEMATICAL APPROACH 257 THE GARCH MODEL 260 THE
GARCH(P, Q) MODEL 260 THE GARCH(1,1) AS AN INFINITE ARCH(/ ) PROCESS 260
ESTIMATING GARCH MODELS IN EVIEWS 261 ALTERNATIVE SPECIFICATIONS 262 THE
GARCH IN MEAN OR GARCH-M MODEL 263 ESTIMATING GARCH-M MODELS IN EVIEWS
264 THE THRESHOLD GARCH (TGARCH) MODEL 267 ESTIMATING TGARCH MODELS IN
EVIEWS 267 THE EXPONENTIAL GARCH (EGARCH) MODEL 268 ESTIMATING EGARCH
MODELS IN EVIEWS 269 ADDING EXPLANATORY VARIABLES IN THE MEAN EQUATION
270 ADDING EXPLANATORY VARIABLES IN THE VARIANCE EQUATION 270 EMPIRICAL
ILLUSTRATIONS OF ARCH /GARCH MODELS 271 A GARCH MODEL OF UK GDP AND THE
EFFECT OF SOCIO-POLITICAL INSTABILITY 271 QUESTIONS AND EXERCISES 276 15
VECTOR AUTOREGRESSIVE (VAR) MODELS AND CAUSALITY TESTS 278 VECTOR
AUTOREGRESSIVE (VAR) MODELS 279 THE VAR MODEL 279 PROS AND CONS OF THE
VAR MODELS 280 CAUSALITY TESTS ' 281 THE GRANGER CAUSALITY TEST 281 THE
SIMS CAUSALITY TEST 283 COMPUTER EXAMPLE: FINANCIAL DEVELOPMENT AND
ECONOMIC GROWTH, WHAT IS THE CAUSAL RELATIONSHIP? 283 16
NON-STATIONARITY AND UNIT-ROOT TESTS 287 UNIT ROOTS AND SPURIOUS
REGRESSIONS 288 WHAT IS A UNIT ROOT? 288 SPURIOUS REGRESSIONS 291
EXPLANATION OF THE SPURIOUS REGRESSION PROBLEM 293 TESTING FOR UNIT
ROOTS 295 TESTING FOR THE ORDER OF INTEGRATION 295 XIV CONTENTS THE
SIMPLE DICKEY-FULLER TEST FOR UNIT ROOTS 295 THE AUGMENTED DICKEY-FULLER
(ADF) TEST FOR UNIT ROOTS 297 THE PHILLIPS-PERRON TEST 297 UNIT-ROOT
TESTS IN EVIEWS AND MICROFIT 299 PERFORMING UNIT-ROOT TESTS IN EVIEWS
299 PERFORMING UNIT-ROOT TESTS IN MICROFIT 300 COMPUTER EXAMPLE:
UNIT-ROOT TESTS ON VARIOUS MACROECONOMIC VARIABLES 302 COMPUTER EXAMPLE:
UNIT-ROOT TESTS FOR THE FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH
EXAMPLE 303 QUESTIONS AND EXERCISES 305 17 COINTEGRATION AND
ERROR-CORRECTION MODELS 306 INTRODUCTION: WHAT IS COINTEGRATION? 307
COINTEGRATION: A GENERAL APPROACH 307 COINTEGRATION: A MORE MATHEMATICAL
APPROACH 308 COINTEGRATION AND THE ERROR-CORRECTION MECHANISM (ECM): A
GENERAL APPROACH 309 THE PROBLEM 309 COINTEGRATION (AGAIN) 310 THE
ERROR-CORRECTION MODEL (ECM) 310 ADVANTAGES OF THE ECM 310 COINTEGRATION
AND THE ERROR-CORRECTION MECHANISM: A MORE MATHEMATICAL APPROACH 311 A
SIMPLE MODEL FOR ONLY ONE LAGGED TERM OF X AND Y 311 A MORE GENERAL
MODEL FOR LARGE NUMBERS OF LAGGED TERMS 313 TESTING FOR COINTEGRATION
315 COINTEGRATION IN SINGLE EQUATIONS: THE ENGLE-GRANGER APPROACH 315
DRAWBACKS OF THE EG APPROACH 317 THE EG APPROACH IN EVIEWS AND MICROFIT
318 COINTEGRATION IN MULTIPLE EQUATIONS AND THE JOHANSEN APPROACH 319
ADVANTAGES OF THE MULTIPLE EQUATION APPROACH 320 THE JOHANSEN APPROACH
(AGAIN) 320 THE STEPS OF THE JOHANSEN APPROACH IN PRACTICE 321 THE
JOHANSEN APPROACH IN EVIEWS AND MICROFIT 326 COMPUTER EXAMPLES OF
COINTEGRATION 331 MONETIZATION RATIO 332 TURNOVER RATIO 335 CLAIMS AND
CURRENCY RATIOS 335 A MODEL WITH MORE THAN ONE FINANCIAL DEVELOPMENT
PROXY VARIABLE 337 QUESTIONS AND EXERCISES 340 PART VI PANEL DATA
ECONOMETRICS 341 18 TRADITIONAL PANEL DATA MODELS 343 INTRODUCTION: THE
ADVANTAGES OF PANEL DATA 344 THE LINEAR PANEL DATA MODEL 345 DIFFERENT
METHODS OF ESTIMATION 345 THE COMMON CONSTANT METHOD 345 CONTENTS XV THE
FIXED EFFECTS METHOD 346 THE RANDOM EFFECTS METHOD 347 THE HAUSMAN TEST
348 COMPUTER EXAMPLES WITH PANEL DATA 349 INSERTING PANEL DATA IN EVIEWS
349 ESTIMATING A PANEL DATA REGRESSION 353 19 DYNAMIC HETEROGENEOUS
PANELS 355 BIAS IN DYNAMIC PANELS 356 BIAS IN THE SIMPLE OLS ESTIMATOR
356 BIAS IN THE FIXED EFFECTS MODEL 357 BIAS IN THE RANDOM EFFECTS MODEL
357 SOLUTIONS TO THE BIAS PROBLEM 357 BIAS OF HETEROGENEOUS SLOPE
PARAMETERS 358 SOLUTIONS TO HETEROGENEITY BIAS: ALTERNATIVE METHODS OF
ESTIMATION 359 THE MEAN GROUP ESTIMATOR 359 THE POOLED MEAN GROUP (PMG)
ESTIMATOR 360 APPLICATION: THE EFFECTS OF UNCERTAINTY IN ECONOMIC GROWTH
AND INVESTMENTS 362 EVIDENCE FROM TRADITIONAL PANEL DATA ESTIMATION 362
MEAN GROUP AND POOLED MEAN GROUP ESTIMATES 362 20 NON-STATIONARY PANELS
365 PANEL UNIT-ROOT TESTS 366 THE LEVIN AND LIN (LL) TEST 367 THE IM,
PESARAN AND SHIN (IPS) TEST 368 THE MADDALA AND WU (MW) TEST 369
COMPUTER EXAMPLES OF PANEL UNIT-ROOT TESTS 369 PANEL COINTEGRATION TESTS
371 INTRODUCTION 371 THE KAO TEST 372 THE MCCOSKEY AND KAO TEST 373 THE
PEDRONI TESTS 373 THE LARSSON ET A\. TEST 375 COMPUTER EXAMPLES OF PANEL
COINTEGRATION TESTS 376 21 PRACTICALITIES IN USING EVIEWS AND MICROFIT
378 ABOUT MICROFIT * 379 CREATING A FILE AND IMPORTING DATA 379 ENTERING
VARIABLE NAMES 379 COPYING/PASTING DATA 380 DESCRIPTION OF MFIT TOOLS
380 CREATING A CONSTANT TERM 381 BASIC COMMANDS IN MFIT 381 ABOUT EVIEWS
381 CREATING A WORKFILE AND IMPORTING DATA 383 COPYING AND PASTING DATA
383 COMMANDS, OPERATORS AND FUNCTIONS 385 BIBLIOGRAPHY 387 INDEX 419
PPN: 260025402 TITEL: APPLIED ECONOMETRICS : A MODERN APPROACH USING
EVIEWS AND MICROFIT / DIMITRIOS ASTERIOU AND STEPHEN G. HALL. - . -
BASINGSTOKE [U.A.] : PALGRAVE MACMILLAN, 2007 ISBN:
0-230-50640-2(PBK)19.99 PBK : 19.99 : CIP ENTRY (FEB.);
978-0-230-50640-4 BIBLIOGRAPHISCHER DATENSATZ IM SWB-VERBUND |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Asteriou, Dimitrios |
| author_GND | (DE-588)124941907 |
| author_facet | Asteriou, Dimitrios |
| author_role | aut |
| author_sort | Asteriou, Dimitrios |
| author_variant | d a da |
| building | Verbundindex |
| bvnumber | BV023072776 |
| callnumber-first | H - Social Science |
| callnumber-label | HB139 |
| callnumber-raw | HB139 |
| callnumber-search | HB139 |
| callnumber-sort | HB 3139 |
| callnumber-subject | HB - Economic Theory and Demography |
| classification_rvk | QH 300 QH 330 |
| ctrlnum | (OCoLC)441828722 (DE-599)BVBBV023072776 |
| dewey-full | 330.01/5195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.01/5195 |
| dewey-search | 330.01/5195 |
| dewey-sort | 3330.01 45195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | Rev. ed. |
| format | Book |
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| genre | 1\p (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV023072776 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:33:45Z |
| indexdate | 2024-07-09T21:10:22Z |
| institution | BVB |
| isbn | 0230506402 9780230506404 |
| language | English |
| lccn | 2006051708 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016275919 |
| oclc_num | 441828722 |
| open_access_boolean | |
| owner | DE-739 DE-1047 DE-83 DE-11 |
| owner_facet | DE-739 DE-1047 DE-83 DE-11 |
| physical | XXV, 397 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Palgrave Macmillan |
| record_format | marc |
| spelling | Asteriou, Dimitrios Verfasser aut Applied econometrics a modern approach using EViews and Microfit Dimitrios Asteriou and Stephen G. Hall Rev. ed. New York, N.Y. Palgrave Macmillan 2007 XXV, 397 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index This new econometrics text deals specifically with the use of econometric software. The text takes the reader from the various forms of econometric data (time series, cross sectional and panel), through their formatting in electronic media (eg ASCII) to their transfer to and use in widely used software packages--Excel, Microfit and Eviews. Most economics degrees now require students to use relevant software to test econometric models and this text illustrates clearly how this is to be done. matematična ekonomija - ekonometrija - modeli - podatki - statistika - linearni modeli - regresijske analize - časovne vrste - informacijska tehnologija - Microfit mathematical economy - econometrics - models - data - statistics - linear models - regression analysis - time series - information technology Wirtschaft Econometrics Economics Statistical methods Software (DE-588)4055382-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf Anwendung (DE-588)4196864-5 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s Statistik (DE-588)4056995-0 s Software (DE-588)4055382-6 s Anwendung (DE-588)4196864-5 s DE-604 Hall, Stephen G. 1953- Sonstige (DE-588)124941907 oth http://www.loc.gov/catdir/enhancements/fy0703/2006051708-b.html Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0703/2006051708-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0703/2006051708-t.html Table of contents only SWBplus Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016275919&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Asteriou, Dimitrios Applied econometrics a modern approach using EViews and Microfit matematična ekonomija - ekonometrija - modeli - podatki - statistika - linearni modeli - regresijske analize - časovne vrste - informacijska tehnologija - Microfit mathematical economy - econometrics - models - data - statistics - linear models - regression analysis - time series - information technology Wirtschaft Econometrics Economics Statistical methods Software (DE-588)4055382-6 gnd Statistik (DE-588)4056995-0 gnd Ökonometrie (DE-588)4132280-0 gnd Anwendung (DE-588)4196864-5 gnd |
| subject_GND | (DE-588)4055382-6 (DE-588)4056995-0 (DE-588)4132280-0 (DE-588)4196864-5 (DE-588)4123623-3 |
| title | Applied econometrics a modern approach using EViews and Microfit |
| title_auth | Applied econometrics a modern approach using EViews and Microfit |
| title_exact_search | Applied econometrics a modern approach using EViews and Microfit |
| title_exact_search_txtP | Applied econometrics a modern approach using EViews and Microfit |
| title_full | Applied econometrics a modern approach using EViews and Microfit Dimitrios Asteriou and Stephen G. Hall |
| title_fullStr | Applied econometrics a modern approach using EViews and Microfit Dimitrios Asteriou and Stephen G. Hall |
| title_full_unstemmed | Applied econometrics a modern approach using EViews and Microfit Dimitrios Asteriou and Stephen G. Hall |
| title_short | Applied econometrics |
| title_sort | applied econometrics a modern approach using eviews and microfit |
| title_sub | a modern approach using EViews and Microfit |
| topic | matematična ekonomija - ekonometrija - modeli - podatki - statistika - linearni modeli - regresijske analize - časovne vrste - informacijska tehnologija - Microfit mathematical economy - econometrics - models - data - statistics - linear models - regression analysis - time series - information technology Wirtschaft Econometrics Economics Statistical methods Software (DE-588)4055382-6 gnd Statistik (DE-588)4056995-0 gnd Ökonometrie (DE-588)4132280-0 gnd Anwendung (DE-588)4196864-5 gnd |
| topic_facet | matematična ekonomija - ekonometrija - modeli - podatki - statistika - linearni modeli - regresijske analize - časovne vrste - informacijska tehnologija - Microfit mathematical economy - econometrics - models - data - statistics - linear models - regression analysis - time series - information technology Wirtschaft Econometrics Economics Statistical methods Software Statistik Ökonometrie Anwendung Lehrbuch |
| url | http://www.loc.gov/catdir/enhancements/fy0703/2006051708-b.html http://www.loc.gov/catdir/enhancements/fy0703/2006051708-d.html http://www.loc.gov/catdir/enhancements/fy0703/2006051708-t.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016275919&sequence=000004&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT asterioudimitrios appliedeconometricsamodernapproachusingeviewsandmicrofit AT hallstepheng appliedeconometricsamodernapproachusingeviewsandmicrofit |