Introductory econometrics: using Monte Carlo simulation with Microsoft Excel
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2013
|
| Edition: | repr. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Repr. 2013 keine CD-ROM als Beilage! |
| Physical Description: | XXIII, 774 S. graf. Darst. 26 cm |
| ISBN: | 0521843197 9780521843195 |
Staff View
MARC
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| 100 | 1 | |a Barreto, Humberto |d 1960- |e Verfasser |0 (DE-588)135561744 |4 aut | |
| 245 | 1 | 0 | |a Introductory econometrics |b using Monte Carlo simulation with Microsoft Excel |c Humberto Barreto ; Frank M. Howland |
| 250 | |a repr. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2013 | |
| 300 | |a XXIII, 774 S. |b graf. Darst. |c 26 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Repr. 2013 keine CD-ROM als Beilage! | ||
| 630 | 0 | 4 | |a Microsoft Excel (Computer file) |
| 650 | 7 | |a Econometrie |2 gtt | |
| 650 | 7 | |a Microsoft Excel |2 gtt | |
| 650 | 7 | |a Monte Carlo-methode |2 gtt | |
| 650 | 4 | |a Monte-Carlo, Méthode de - Informatique | |
| 650 | 4 | |a Économétrie | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Monte Carlo method |x Data processing | |
| 650 | 0 | 7 | |a Ökonometrie |0 (DE-588)4132280-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Monte-Carlo-Simulation |0 (DE-588)4240945-7 |2 gnd |9 rswk-swf |
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Record in the Search Index
| _version_ | 1804174965088452608 |
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| adam_text | Contents
Preface page xv
Preface to the Second Printing xxiii
User Guide 1
0.1. Conventions and Organization of Files 1
0. 2. Preparing and Working with Microsoft ExcelK 3
1. Introduction 10
1.1. Definition of Econometrics 10
1.2. Regression Analysis 11
Cig.xls
1.3. Conclusion 2K
1.4. Exercises 20
References 30
PART 1. DESCRIPTION
2. Correlation 33
2.1. Introduction 33
2.2. Correlation Basics 33
Correlation.xls
2.3. Correlation Dangers 40
Correlation.xls
lMRGDP.xls
2.4. Ecological Correlation 44
EcolCorr.xls
EcolCorrC PS.xls
2.5. Conclusion 50
2.6. Exercises 51
References 51
v։ Ct Clients
3. PivotTables
.VI. Introduction 33
3.2. The Basic Pivot Iablc 53
Indiana FI Work ers. xLs(in Ba.su Too
Histogram.xla(Excel add-in)
3.3. The Crosstab and Conditional Average
Indiana FtWorkers-xIs (in Basic Too
3.4. Pivot Tables and the Conditional Mean Function 65
EastNorthCentralFI~Workers.xls
3.5. Conclusion 69
3.6. Exercises 70
References 71
4. Computing the OLS Regression Line 72
4.1. Introduction 72
4.2. Fitting the Ordinary Least Squares Regression Line 72
Reg. x Is
4.3. Least Squares Formulas 82
Reg.xls
4.4. Fitting the Regression Line in Practice 84
Reg.xls
4.5. Conclusion 90
4.6. Exercises 90
References 91
Appendix: Deriving the Least Squares Formulas 91
5. Interpreting OLS Regression 95
5.1. Introduction 95
5.2. Regression as Double Compression 95
DouhleC ompression.xls
East .Worth C entra! FTWork ers. xls
5.3. Gallon and I vo Regression Lines 104
IWoRegression Lines, xls
5.4. Properties of the Sample Average and the Regression Line 107
OLS Formula, xls
5.5. Residuals and the Root-Mean-Square Error 114
Residual Plot, xls
RMSE.xls
5.6. Л-Squared (/?-’) ^
RSquared.xls
5.7. Limitations of Data Description with Regression 126
Arise om be֊ xls
IMRGDPReg.xls
Contents
vii
SameRegLineDifferentData.xls
Hourly Earnings, xls
5.8. Conclusion 135
5.9. Exercises 135
References 136
Appendix: Proof that the Sample Average is a Least Squares
Estimator 136
6. Functional Form of the Regression ] 38
6.1. Introduction 138
6.2. Understanding Functional Form via an Econometric Fable 139
Galileo.xls
6.3. Exploring Two Other Functional Forms 144
IMRG DP Fun Form, xls
6.4. The Earnings Function 148
SemiLogEarningsFn.xls
6.5. Elasticity 155
6.6. Conclusion 159
6.7. Exercises 160
References 160
Appendix: A Catalog of Functional Forms 161
FuncFormCatalog.xls
7. Multiple Regression 164
7.1. Introduction 164
7.2. Introducing Multiple Regression 165
MultiReg.xls
7.3. Improving Description via Multiple Regression 174
MultiReg.xls
7.4. Multicollinearitv 184
M ulticollinearity. xls
7.5. Conclusion 191
7.6. Exercises 192
References 194
Appendix: The Multivariate Least Squares Formula and the
Omitted Variable Rule 19**
8. Dummy Variables 198
8.1. Introduction 198
8.2. Defining and Using Dummy Variables 199
Female, xls
8.3. Properties of Dummy Variables 202
Fernaie.xls
VIH
Contents
H.4. Dummy Variables as Intercept Shifters
Fetmite, x is
s.5. Dummy Variable Interaction Ierms
Female, xls
K.6. Conclusion
K.7. Exercises
References
PART 2. INFERENCE
9. Monte Carlo Simulation
9.1. Introduction
9.2. Random Number Generation Theory
RNG Theory. x is
9.3. Random Number Generation in Practice
RNG Tract ice. xls
9.4. Monte Carlo Simulation: An Example
MtmteCarlo.xls
9.5. The Monte Carlo Simulation Add-In
MtmteCarlo.xls
MCStm.xIa (Exceladd-in)
MCSunSolver.xla(Excel add-in)
9.6. Conclusion
9.7. Exercises
References
10. Review of Statistical Inference
10.1. Introduction
10.2. Introducing Box Models for Chance Processes
10.3. The Coin-Rip Box Model
BttxModel.xls
10.4. The Polling Box Model
Presidential Heights xls
10.5. Hypothesis Testing
PValue.xla (Exceladd-in)
10.6. Consistent Estimators
Consistency, xb
10.7. The Algebra of Expectations
A IgebraofExpectations. xls
10.H. Conclusion
10.9. Exercises
References
Appendix: The Normal Approximation
205
208
211
212
212
215
215
216
220
225
232
235
236
236
238
238
239
242
251
257
262
265
277
277
278
278
Contents ix
11. The Measurement Box Model 281
11.1. Introduction 281
11.2. Introducing the Problem 283
11.3. The Measurement Box Model 285
11.4. Monte Carlo Simulation 290
Measure.xls
11.5. Applying the Box Model 293
Measure.xls
11.6. Hooke’s Law 296
HookesLaw.xls
11.7. Conclusion 301
11.8. Exercises 301
References 302
12. Comparing Two Populations 303
12.1. Introduction 303
12.2. Two Boxes 303
12.3. Monte Carlo Simulation of a Two Box Model 306
TwoBoxModel.xls
12.4. A Real Example: Education and Wages 309
C PS90 Wo rkers. xls
12.5. Conclusion 314
12.6. Exercises 315
C PS90Exp Workers.xls
References 315
13. The Classical Econometric Model 316
13.1. Introduction 316
13.2. Introducing the CEM via a Skiing Example 316
Skiing.xls
13.3. Implementing the CEM via a Skiing Example 322
Skiing.xls
13.4. CEM Requirements 328
13.5. Conclusion 332
13.6. Exercises 333
References 334
14. The Gauss-Markov Theorem 335
14.1. Introduction 335
14.2. Linear Estimators 336
Gauss Markov Univariate, xls
14.3. Choosing an Estimator 342
Gauss Markov Univariate, x Is
x Con tents
14.4. Proving the (iauss-Markov Theorem in the Univariate Case 347
GaussMarkovUntvanute.xls
14.5. Linear Estimators in Regression Analysis 352
GaussMarkovRivariate.xls
14.6. OLS is BLUE: Hie C iauss-Markov Theorem for the
Bivariate Case
( ¡aussMurk ov Hi variate, x Is
14.7. Using the Algebra of Expectations 366
(¡auss Markov Uni variate. xls
GaussMarkovHivariate.xls
14.8. Conclusion 374
14.9. Exercises 375
References 376
15. Understanding the Standard Error 378
15.1. Introduction 378
15.2. SE Intuition 378
SEbl()LS.xls
15.3. The Estimated SE 383
SEbt( l.S.xls
15.4. The Determinants of the SE of the OLS Sample Slope 387
SEblOESxls
15.5. Estimating the SD of the Errors 392
Estimatings l) Hrrorxxls
15.6. The Standard Error of the Forecast and the Standard Error
of the Forecast Error 398
S E Forecast.xls
15.7. Conclusion 408
15.8. Exercises 409
References 420
16. Confldence Intervals and Hypothesis Testing 411
16.1. Introduction 422
16.2. Distributions of OLS Regression Statistics 412
l.mest Random Variables, xls
16.3. Understanding Confidence Intervals 421
Confiden ce Inters a Is. xls
16.4. The Logic of Hypothesis Testing 430
Hypothesis Test.xls
16-5. Z and T-Tcsts 434
Confidence Inierxatsxb
ZandTTestxxb
Cun tents
xi
16.6. A Practical Example 443
C ig Data Inference.xls
16.7. Conclusion 450
16.8. Exercises 450
Semi LogEarningsFn.xls
References 451
17. Joint Hypothesis Testing 453
17.1. Introduction 453
17.2. Restricted Regression 456
No Intercept Bug. xls
17.3. The Chi-Square Distribution 45X
ChiSquareDisl.xls
17.4. The F-Distribution 461
FDist.xls
17.5. An F-Test: The Galileo Example 462
FDistGalileo.xls
17.6. F- and F-Tests for Equality of Two Parameters 46X
FDistFoodStamps.xls
17.7. F-Test for Multiple Parameters 475
FDistEarningsFn.xls
17.8. The Consequences of Multicollinearity 478
CorrelatedEstimates.xls
17.9. Conclusion 487
17.10. Exercises 487
My M onte Ca rlo. xis
References 488
18. Omitted Variable Bias 490
18.1. Introduction 490
1X.2. Why Omitted Variable Bias Is important 491
18.3. Omitted Variable Bias Defined and
Demonstrated 493
SkitngOVB.xls
18.4. A Real Example of Omitted Variable Bias 498
Computer Use! 997.xls
18.5. Random A s: A More Realistic Data
Generation Process 502
Computer Use 1997. xls
18.6. Conclusion 506
18.7. Exercises 506
References 507
XII
Contents
19. Heteroskedasticity
19.1. Introduction
19.2. A Univariate Example of Heteroskedasticity
Неї. xls
19.3 A Bivariate Example of Heteroskedasticity
Het.xls
19.4 Diagnosing Heteroskedasticity with the В-P Test
Het.xls
BPSampDist.xls
19.5. Dealing with Heteroskedasticity: Robust Standard Errors
l feiRobusS E.xls
()l.SRegression.xla (Excel add-in)
19.6. Correcting for Heteroskedasticity: Generalized Least Squares
Het(il.S.xls
19.7. A Real Example of Heteroskedasticity: The Earnings
Function
Wages OctV 7. xls
19.H. Conclusion
19.9. Exercises
References
508
508
510
518
527
533
542
549
555
556
557
20. Autocorrelation
20.1. Introduction
20.2. Understanding Autocorrelation
AutoCorr.xls
20.3. Consequences of Autocorrelation
AutoCorr.xls
20.4. Diagnosing Autocorrelation
AutoCorr.xls
20.5. Correcting Autocorrelation
AutoCorr.xls
20.6. Conclusion
CPIMZM.xts
l .ui etnt z mg. xls
20.7. Exercises
W ixspeaficanon.xls
Free Throw AutoCorr.xls
References
558
558
560
566
576
588
600
602
603
21. Topics in Hat Series
21.1. Introduction
21 -2- Trends in Tune Series Models
India PopuiantMuds
F֊xpGrowthModeLxb
604
604
605
Contents
xiii
AnnualGDP.xls
Spuriousxls
21.3. Dummy Variables in Time Series Models 613
TimeSeriesDummyVariables.xls
CoalMining.xls
21.4. Seasonal Adjustment 617
SeasonalTheory.xls
SeasonalPractice.xls
21.5. Stationarity 624
Stationarity.xls
21.6. Weak Dependence 633
Stationarity.xls
Spurious.xls
21.7. Lagged Dependent Variables 638
PartialAdjustment.xls
21.8. Monev Demand 645
*
Money Demand.xls
Lagged DepVar.xls
21.9. Comparing Forecasts Using Different Models of the DGP 652
AnnualGDP.xls
ForecastingGDP.xls
21.10. Conclusion 658
21.11. Exercises 659
References 661
22. Dummy Dependent Variable Models 663
22.1. Introduction 663
22.2. Developing Intuition about Dummy Dependent
Variable Models 666
Raid.xls
22.3. The Campaign Contributions Example 669
CampCont.xls
22.4. A DDV Box Model 671
Raid.xls
CampCont.xls
22.5. The Linear Probability Model (OLS with a Dummy
Dependent Variable) 674
CampCont.xls
L PMMonteCarlo. x Is
22.6. Nonlinear Least Squares Applied to Dummy Dependent
Variable Models 680
SLLSFit.xls
S LLSMCSim.xls
Contents
XIV
22.7. Interpreting NLLS Estimates
SU.SEit.xls
22. K. Is There Mortgage Discrimination?
MortDisc.xls
MonDi.scMCSim.xis
DDV.xUt (Excel add-in)
DDVGN.xIa(Excel add-in)
22.9. Conclusion
22.10. Exercises
References
23. Bootstrap
23.1. Introduction
23.2. Bootstrapping the Sample Percentage
PercentageBootstrap.xls
23.3. Paired XY Bootstrap
Paired X Y Bootstrap.xls
23.4. The Bootstrap Add-in
Paired X Y Bootstrap.xls
Bootstrap.xla (Excel add-in)
23.5. Bootstrapping R2
Bootstrap R2. xls
23.6. Conclusion
23.7. Exercises
References
24. Simultaneous Equations
24.1. Introduction
24.2. Simultaneous Equations Model Example
24.3. Simultaneity Bias with OLS
StmEq.xls
24.4. Two Stage Least Squares
StmEq.xls
24.5. Conclusion
24.6. Exercises
References
690
695
706
707
707
709
709
710
713
718
721
726
728
728
730
730
731
735
741
745
746
747
Glossary
Index
749
761
|
| any_adam_object | 1 |
| author | Barreto, Humberto 1960- Howland, Frank M. 1958- |
| author_GND | (DE-588)135561744 (DE-588)135561728 |
| author_facet | Barreto, Humberto 1960- Howland, Frank M. 1958- |
| author_role | aut aut |
| author_sort | Barreto, Humberto 1960- |
| author_variant | h b hb f m h fm fmh |
| building | Verbundindex |
| bvnumber | BV042750284 |
| 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 SK 980 |
| classification_tum | WIR 017f DAT 304f |
| ctrlnum | (OCoLC)917901662 (DE-599)BVBBV042750284 |
| dewey-full | 330/.01/518282 330.01518282 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330/.01/518282 330.01518282 |
| dewey-search | 330/.01/518282 330.01518282 |
| dewey-sort | 3330 11 6518282 |
| dewey-tens | 330 - Economics |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| edition | repr. |
| format | Book |
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| id | DE-604.BV042750284 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:08:46Z |
| institution | BVB |
| isbn | 0521843197 9780521843195 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028181049 |
| oclc_num | 917901662 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-92 |
| owner_facet | DE-473 DE-BY-UBG DE-92 |
| physical | XXIII, 774 S. graf. Darst. 26 cm |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | Barreto, Humberto 1960- Verfasser (DE-588)135561744 aut Introductory econometrics using Monte Carlo simulation with Microsoft Excel Humberto Barreto ; Frank M. Howland repr. Cambridge [u.a.] Cambridge Univ. Press 2013 XXIII, 774 S. graf. Darst. 26 cm txt rdacontent n rdamedia nc rdacarrier Repr. 2013 keine CD-ROM als Beilage! Microsoft Excel (Computer file) Econometrie gtt Microsoft Excel gtt Monte Carlo-methode gtt Monte-Carlo, Méthode de - Informatique Économétrie Datenverarbeitung Econometrics Monte Carlo method Data processing Ökonometrie (DE-588)4132280-0 gnd rswk-swf Monte-Carlo-Simulation (DE-588)4240945-7 gnd rswk-swf EXCEL (DE-588)4138932-3 gnd rswk-swf Ökonometrie (DE-588)4132280-0 s Monte-Carlo-Simulation (DE-588)4240945-7 s EXCEL (DE-588)4138932-3 s b DE-604 Howland, Frank M. 1958- Verfasser (DE-588)135561728 aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028181049&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Barreto, Humberto 1960- Howland, Frank M. 1958- Introductory econometrics using Monte Carlo simulation with Microsoft Excel Microsoft Excel (Computer file) Econometrie gtt Microsoft Excel gtt Monte Carlo-methode gtt Monte-Carlo, Méthode de - Informatique Économétrie Datenverarbeitung Econometrics Monte Carlo method Data processing Ökonometrie (DE-588)4132280-0 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd EXCEL (DE-588)4138932-3 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4240945-7 (DE-588)4138932-3 |
| title | Introductory econometrics using Monte Carlo simulation with Microsoft Excel |
| title_auth | Introductory econometrics using Monte Carlo simulation with Microsoft Excel |
| title_exact_search | Introductory econometrics using Monte Carlo simulation with Microsoft Excel |
| title_full | Introductory econometrics using Monte Carlo simulation with Microsoft Excel Humberto Barreto ; Frank M. Howland |
| title_fullStr | Introductory econometrics using Monte Carlo simulation with Microsoft Excel Humberto Barreto ; Frank M. Howland |
| title_full_unstemmed | Introductory econometrics using Monte Carlo simulation with Microsoft Excel Humberto Barreto ; Frank M. Howland |
| title_short | Introductory econometrics |
| title_sort | introductory econometrics using monte carlo simulation with microsoft excel |
| title_sub | using Monte Carlo simulation with Microsoft Excel |
| topic | Microsoft Excel (Computer file) Econometrie gtt Microsoft Excel gtt Monte Carlo-methode gtt Monte-Carlo, Méthode de - Informatique Économétrie Datenverarbeitung Econometrics Monte Carlo method Data processing Ökonometrie (DE-588)4132280-0 gnd Monte-Carlo-Simulation (DE-588)4240945-7 gnd EXCEL (DE-588)4138932-3 gnd |
| topic_facet | Microsoft Excel (Computer file) Econometrie Microsoft Excel Monte Carlo-methode Monte-Carlo, Méthode de - Informatique Économétrie Datenverarbeitung Econometrics Monte Carlo method Data processing Ökonometrie Monte-Carlo-Simulation EXCEL |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028181049&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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