Fundamentals of applied econometrics:
Gespeichert in:
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| 245 | 1 | 0 | |a Fundamentals of applied econometrics |c by Richard A. Ashley |
| 264 | 1 | |a Hoboken |b Wiley |c 2012 | |
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| adam_text | BRIEF
CONTENTS
What s Different about This Book
xiii
Working with Data in the Active Learning Exercises
xxii
Acknowledgments
xxiii
Notation
xxiv
Part I INTRODUCTION AND STATISTICS REVIEW
1
Chapter
1
INTRODUCTION
3
Chapter
2
A REVIEW OF PROBABILITY THEORY I
1
Chapter
3
ESTIMATING THE MEAN OF A NORMALLY DISTRIBUTED RANDOM VARIABLE
46
Chapter
4
STATISTICAL INFERENCE ON THE MEAN OF A NORMALLY
DISTRIBUTED RANDOM VARIABLE
68
Part II REGRESSION ANALYSIS
97
Chapter
5
THE BIVARIATE REGRESSION MODEL: INTRODUCTION, ASSUMPTIONS.
AND PARAMETER ESTIMATES
99
Chapter
6
THE BIVARIATE LINEAR REGRESSION MODEL: SAMPLING DISTRIBUTIONS
AND ESTIMATOR PROPERTIES 1
3 !
Chapter
7
THE BIVARIATE LINEAR REGRESSION MODEL: INFERENCE ON
β
1
50
Chapter
8
THE BIVARIATE REGRESSION MODEL: R2 AND PREDICTION
178
Chapter
9
THE MULTIPLE REGRESSION MODEL
191
Chapter
1 0
DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE
REGRESSION MODEL: DEALING WITH POTENTIAL OUTUERS AND
HETEROSCEDASTICITY IN THE CROSS-SECTIONAL DATA CASE
224
Chapter
1 1
STOCHASTIC REGRESSORS AND ENDOGENEITY
259
Chapter
12
INSTRUMENTAL VARIABLES ESTIMATION
303
Chapter
1 3
DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE
REGRESSION MODEL: THE TIME-SERIES DATA CASE (PART A)
342
Chapter
14
DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE
REGRESSION MODEL: THE TIME-SERIES DATA CASE (PART B)
389
Part III ADDITIONAL TOPICS IN REGRESSION ANALYSIS
455
Chapter
1 5
REGRESSION MODELING WITH PANEL DATA (PART A)
459
Chapter
! 6
REGRESSION MODELING WITH PANEL DATA (PART B)
507
Chapter
1 7
A CONCISE INTRODUCTION TO TIME-SERIES ANALYSIS AND
FORECASTING (PART A)
536
Chapter
1 8
A CONCISE INTRODUCTION TO TIME-SERIES ANALYSIS AND
FORECASTING (PART B)
595
Chapter
19
PARAMETER ESTIMATION BEYOND CURVE-FITTING:
MLE (WITH AN APPLICATION TO BINARY-CHOICE MODELS)
AND GMM (WITH AN APPLICATION TO IV REGRESSION)
647
Chapter
20
CONCLUDING COMMENTS
681
Mathematics Review
693
iv
TABLE
OF
CONTENTS
What s Different about This Book
xiii
Working with Data in the Active Learning Exercises
xxii
Acknowledgments
xxiii
Notation
xxiv
Part I INTRODUCTION AND STATISTICS REVIEW
1
Chapter
1
INTRODUCTION
3
1.1
Preliminaries
3
1.2
Example: Is Growth Good for the Poor?
4
1.3
What s to Come
7
ALE
1
a: An Econometrics Time Capsule
8
ALE
1
b: Investigating the Slope Graphically Using a Scatterplot (Online)
ALE I c: Examining Some Disturbing Variations on Dollar
&
Kraay s Model (Online)
ALE I d: The Pitfalls of Making Scatterplots with Trended Time-Series Data (Online)
Chapter
2
A REVIEW OF PROBABILITY THEORY
1
I
2.1
Introduction
1
I
2.2
Random Variables 1
2
2.3
Discrete Random Variables 1
3
2.4
Continuous Random Variables
17
2.5
Some Initial Results on Expectations 1
9
2.6
Some Results on Variances
20
2.7
A Pair of Random Variables
22
2.8
The Linearity Properly of Expectations
24
2.9
Statistical Independence
26
2.10
Normally Distributed Random Variables
29
2.
1
1
Three Special Properties of Normally Distributed Variables
ЗІ
2.12
Distribution of a Linear Combination of Normally Distributed Random Variables
32
VI
TABLE OF CONTENTS
Chapter
3
Chapter
4
2.13
Conclusion
36
Exercises
37
ALE 2a: The Normal Distribution
42
ALE 2b: Central Limit Theorem Simulators on the Web [Online)
Appendix
2.1 :
The Conditional Mean of a Random Variable
44
Appendix
2.2:
Proof of the Linearity Property for the Expectation of a Weighted
Sum of Two Discretely Distributed Random Variables
45
ESTIMATING THE MEAN OF A NORMALLY DISTRIBUTED RANDOM VARIABLE
46
3.1
Introduction
46
3.2
Estimating
μ
by Curve Fitting
48
3.3
The Sampling Distribution of
F
51
3.4
Consistency -A First Pass
54
3.5
Unbiasedness and the Optimal Estimator
55
3.6
The Squared Error Loss Function and the Optimal Estimator
56
3.7
The Feasible Optimality Properties: Efficiency and BLUness
58
3.8
Summary
61
3.9
Conclusions and Lead-in to Next Chapter
62
Exercises
62
ALE 3a: Investigating the Consistency of the Sample Mean and Sample
Variance Using Computer-Generated Data
64
ALE 3b: Estimating Means and Variances Regarding the Standard
&
Poor s
SP5OO Stock Index
(Online)
STATISTICAL INFERENCE ON THE MEAN OF A NORMALLY DISTRIBUTED
RANDOM VARIABLE
4.1
Introduction
4.2
Standardizing the distribution of
?
4.3
Confidence Intervals for
μ
When
σ2
Is Known
4.4
Hypothesis Testing when
σ2
Is Known
4.5
Using S2 to Estimate
σ2
(and Introducing the Chi-Squared Distribution)
4.6
Inference Results on
μ
When
σ2
Is Unknown (and Introducing the Student s
t
Distribution)
4.7
Application: State-Level U.S. Unemployment Rates
4.8
Introduction to Diagnostic Checking: Testing the Constancy of
μ
across the
Sample
4.9
Introduction to Diagnostic Checking: Testing the Constancy of
σ2
across the
Sample
4.10
Some General Comments on Diagnostic Checking
4.
11 Closing Comments
Exercises
ALE 4a: Investigating the Sensitivity of Hypothesis Test /7-Values to Departures
from the
ΝΙΙΟ(μ, σ2)
Assumption Using Computer-Generated Data
ALE 4b: Individual Income Data from the Panel Study on Income Dynamics
(PSID)
-
Does Birth-Month
Matteû
(Online)
68
68
69
69
71
75
78
82
84
87
89
90
91
93
TABLE
OF
CONTENTS
VII
Partii
Chapter
5
Chapter
6
Chapter
7
REGRESSION ANALYSIS
97
THE BIVARIATE REGRESSION MODEL: INTRODUCTION, ASSUMPTIONS,
AND PARAMETER ESTIMATES
99
5.1
Introduction
99
5.2
The Transition from Mean Estimation to Regression: Analyzing the Variation of
Per Capita Real Output across Countries
100
5.3
The
Divariate
Regression Model
-
Its Form and the Fixed in Repeated
Samples Causality Assumption
105
5.4
The Assumptions on the Model Error Term,
Ц
1
06
5.5
Least Squares Estimation of
α
and
β
109
5.6
Interpreting the Least Squares Estimates of
α
and
β
118
5.7
Bivariate Regression with a Dummy Variable: Quantifying the Impact of
College Graduation on Weekly Earnings
120
Exercises
127
ALE 5a: Exploring the Penn World Table Data
128
ALE 5b: Verifying
â*ls
and
ß*ls
over a Very Small Data Set |Online)
ALE 5c: Extracting and Downloading CPS Data from the Census Bureau
Web Site (Online)
ALE 5d: Verifying That
ß*)s
on a Dummy Variable Equals the
Difference in the Sample Means (Online)
Appendix
5.1: ß*ls
When
x¡
Is a Dummy Variable
130
THE BIVARIATE LINEAR REGRESSION MODEL: SAMPUNG DISTRIBUTIONS
AND ESTIMATOR PROPERTIES
1
31
6.1
Introduction
1
31
6.2
Estimates and Estimators
132
6.3
β
as a Linear Estimator and the Least Squares Weights 1
32
6.4
The Sampling Distribution of
β
134
6.5
Properties of
β:
Consistency 1
40
6.6
Properties of
ß: Best
Linear Unbiasedness
140
6.7
Summary
143
Exercises
144
ALE 6a: Outliers and Other Perhaps Overly Influential Observations: Investigating
the Sensitivity of
β
to an Outlier Using Computer-Generated Data
147
ALE 6b: Investigating the Consistency of
β
Using Computer-Generated Data (Online)
THE BIVARIATE UNEAR REGRESSION MODEL: INFERENCE ON
β
7.1
Introduction
7.2
A Statistic for
β
with a Known Distribution
7.3
A
95%
Confidence Interval for
β
with
σ2
Given
7.4
Estimates versus Estimators and the Role of the Model Assumptions
7.5
Testing a Hypothesis about
β
with
σ2
Given
7.6
Estimating
σ2
7.7
Properties of
S2
7.8
A Statistic for
β
Not Involving
σ2
150
150
152
152
154
156
158
159
160
VIII
TABLE OF
CONTENTS
Chapter
8
Chapter
9
Chapter
10
7.9
A
95%
Confidence Interval for
β
with
σ2
Unknown
160
7.10
Testing a Hypothesis about
β
with
σ2
Unknown
162
7.11
Application: The Impact of College Graduation on Weekly Earnings (Inference
Results)
164
7.12
Application: Is Growth Good for the Poor?
168
7.13
Summary
169
Exercises
169
ALE 7a: Investigating the Sensitivity of Slope Coefficient Inference to Departures
from the
Ц
~
N110(0,
σ2)
Assumption Using Computer-Generated Data
172
ALE 7b: Distorted Inference in Time-Series Regressions with Serially Correlated
Model Errors: An Investigation Using Computer-Generated Data (Online)
Appendix
7.1:
Proof That S2 Is Independent of
β
177
THE BIVARIATE REGRESSION MODEL: R2 AND PREDICTION
178
8.1
Introduction
178
8.2
Quantifying How Well the Model Fits the Data
179
8.3
Prediction as a Tool for Model Validation 1
82
8.4
Predicting
^,
given
λά^ι
184
Exercises
188
ALE 8a: On the Folly of Trying Too Hard: A Simple Example of Data Mining
189
THE MULTIPLE REGRESSION MODEL
191
9.1
Introduction
191
9.2
The Multiple Regression Model 1
91
9.3
Why the Multiple Regression Model Is Necessary and Important 1
92
9.4
Multiple Regression Parameter Estimates via Least Squares Fitting
193
9.5
Properties and Sampling Distribution of
ßots
ι
--· ßois,
t 195
9.6
Overelaborate Multiple Regression Models
202
9.7
Underelaborate Multiple Regression Models
205
9.8
Application: The Curious Relationship between Marriage and Death
206
9.9
Multicollinearity
208
9.10
Application: The Impact of College Graduation and Gender on
Weekly Earnings
210
9.11
Application: Vote Fraud in Philadelphia Senatorial Elections
214
Exercises
218
ALE 9a: A Statistical Examination of the Florida Voting in the
November
2000
Presidential Election
-
Did Mistaken Votes for Pat
Buchanan Swing the Election from Gore to Bush?
220
ALE 9b: Observing and Interpreting the Symptoms of Multicollinearity (Online)
ALE 9c: The Market Value of a Bathroom in Georgia (Online}
Appendix
9.1 :
Prediction Using the Multiple Regression Model
222
DIAGNOSTICALLY CHECKING AND RESPECIFYING THE MULTIPLE REGRESSION
MODEL: DEALING WITH POTENTIAL OUTLIERS AND HETEROSCEDASTICITY
IN THE CROSS-SECTIONAL DATA CASE
224
10.1
Introduction
224
10.2
The Fitting Errors as Large-Sample Estimates of the Model Errors, U ...UN
227
TABLE
OF CONSENTS
¡χ
10.3
Reasons for Checking the Normality of the Model Errors, Uy...UN
228
1
0.4
Heteroscedasticity and Its Consequences
237
10.5
Testing for Heteroscedasticity
239
1
0.6
Correcting for Heteroscedasticity of Known Form
243
1
0.7
Correcting for Heteroscedasticity of Unknown Form
248
1
0.8
Application: Is Growth Good for the Poor? Diagnostically Checking the
Dollar/Kraay
(2002)
Model.
252
Exercises
256
ALE
1
0a: The Fitting Errors as Approximations for the Model Errors
257
ALE
1
0b: Does Output Per Person Depend on Human Capital? (A Test of the
Augmented Solow Model of Growth)2 (Online)
ALE
1
0c: Is Trade Good or Bad for the Environment? (First Pass)3 (Online)
Chapter
11
STOCHASTIC REGRESSORS AND ENDOGENEITY
259
11.1
Introduction
259
11.2
Unbiasedness of the OLS Slope Estimator with a Stochastic Regressor
Independent of the Model Error
261
11.3
A Brief Introduction to Asymptotic Theory
264
1
1.4
Asymptotic Results for the OLS Slope Estimator with a Stochastic Regressor
269
11.5
Endogenous Regressors: Omitted Variables
272
1
1.6
Endogenous Regressors: Measurement Error
273
11.7
Endogenous Regressors: Joint Determination
-
Introduction to Simultaneous
Equation Macroeconomic and Microeconomic Models
274
1
1
.8
How Large a Sample Is Large Enough ? The Simulation Alternative
278
11.9
An Example: Bootstrapping theAngrist-Krueger
(1991)
Model
282
Exercises
290
-
OLS
ALE
11
a: Central Limit Theorem Convergence for
β
in the Bivariate Regression
Model
293
ALE
1
1b: Bootstrap Analysis of the Convergence of the Asymptotic Sampling
Distributions for Multiple Regression Model Parameter Estimators (Online)
Appendix
11.1:
The Algebra of Probability Limits
298
Appendix
11.2:
Derivation of the Asymptotic Sampling Distribution of the
OLS Slope Estimator
299
Chapter
12
INSTRUMENTAL VARIABLES ESTIMATION
303
12.1
Introduction
-
Why It Is Challenging to Test for Endogeneity
303
1
2.2
Correlation versus Causation -Two Ways to Untie the Knot
305
1
2.3
The Instrumental Variables Slope Estimator (and Proof of Its Consistency)
in the Bivariate Regression Model
311
1
Uses data from Dollar, D., and A. Kraay
(2002),
Growth Is Good for the Poor, Journal of Economic Growth
7, 195-225.
2Uses data from Mankiw, G. N., D.
Romer,
and D.
N.
Weil
(1992),
A Contribution to the Empirics of Economic Growth,
The Quarterly Journal of Economics
107(2), 407-37.
Mankiw
et al.
estimate and test a Solow growth model, augmenting it
with a measure of human capital, quantified by the percentage of the population in secondary school.
3Uses data from
Frankéi,
J.
Α.,
and
Α. Κ.
Rose
(2005),
Is Trade Good or Bad for the Environment? Sorting Out the
Causality, The Review of Economics and Statistics 87( I
), 85-91.
Frankéi
and Rose quantify and test the effect of trade openness
{
(X+M)IY] on three measures of environmental damage (SO2, NO2, and total suspended particulates). Since trade openness may
well be endogenous.
Frankéi
and Rose also obtain 2SLS estimates; these are examined in Active Learning Exercise 12b.
TABLE OF CONTENTS
12.4
Inference Using the Instrumental Variables Slope Estimator
ЗІЗ
12.5
The Two-Stage Least Squares Estimator for the Overidentified Case
317
12.6
Application: The Relationship between Education and Wages
(Angrist and Krueger,
1991) 321
Exercises
330
ALE 12a: The Role of Institutions Rule of Law in Economic Growth4
332
ALE
1
2b: Is Trade Good or Bad for the Environment? (Completion)5 (Online)
ALE
1
2c: The Impact of Military Service on the Smoking Behavior of Veterans6 (Online)
ALE
1
2d: The Effect of Measurement-Error Contamination on OLS Regression
Estimates and the Durbin/Bartlett IV Estimators (Online)
Appendix
12.1:
Derivation of the Asymptotic Sampling Distribution of the
Instrumental Variables Slope Estimator
336
Appendix 1
2.2:
Proof That the 2SLS Composite Instrument Is Asymptotically
Uncorrelated with the Model Error Term
340
Chapter 1
3
DIAGNOSTICALLY CHECKING AND RESPECTING THE MULTIPLE
REGRESSION MODEL: THE TIME-SERIES DATA CASE (PART A)
342
1
3.
1 An Introduction to Time-Series Data, with a Road Map for This Chapter
342
13.2
The Bivariate Time-Series Regression Model with Fixed Regressors but Serially
Correlated Model Errors, U,
...
UT
348
13.3
Disastrous Parameter Inference with Correlated Model Errors: Two Cautionary
Examples Based on U.S. Consumption Expenditures Data
353
13.4
The AR( I
)
Model for Serial Dependence in a Time-Series
363
13.5
The Consistency of
ф°^
as an Estimator of
φ,
in theAR(l) Model and Its
Asymptotic Distribution
367
1
3.6
Application of theAR(l) Model to the Errors of the (Detrended) U.S.
Consumption Function
-
and a Straightforward Test for Serially Correlated
Regression Errors
370
13.7
Dynamic Model Respecification: An Effective Response to Serially Correlated
Regression Model Errors, with an Application to the (Detrended)
U.S. Consumption Function
374
Exercises
382
Appendix
13.1 :
Derivation of the Asymptotic Sampling Distribution of
φ°^
in the
AR(1) Model
384
Chapter
14
DIAGNOSTICALLY CHECKING AND RESPECTING THE MULTIPLE REGRESSION
MODEL: THE TIME-SERIES DATA CASE (PART B)
389
14.1
Introduction: Generalizing the Results to Multiple Time-Series
389
14.2
The Dynamic Multiple Regression Model
390
4
Uses data from Acemoglu, D., S. Johnson, and J. A. Robinson
(2001 ),
The Colonial Origins of Comparative Development,
The American Economic Review
91(5), 1369-1401.
These authors argue that the European mortality rate in colonial times is a
valid instrument for current institutional quality because Europeans settled (and imported their cultural institutions) only in
colonies with climates they found healthy.
5
See footnote for Active Learning Exercise 10c.
6
Uses data from Bedard, K., and O.
Deschênes
(2006),
The
Long-Term
Impact of Military Service on Health: Evidence from
World War II and Korean War Veterans. The American Economic Review
96(1), 176-194.
These authors quantify the impact
of the provision of free and/or low-cost tobacco products to servicemen on smoking and (later) on mortality rates, using
instrumental variable methods to control for the nonrandom selection into military service.
TABLE
OF
CONTENTS
xi
] 4.3
If
t J
or Random Walk Time-Series
395
14.4
Capstone Example Part
1 :
Modeling Monthly U.S. Consumption Expenditures
in Growth Rates
404
1
4.5
Capstone Example Part
2:
Modeling Monthly U.S. Consumption Expenditures
in Growth Rates and Levels (Cointegrated Model)
424
14.6
Capstone Example Part
3:
Modeling the Level of Monthly U.S. Consumption
Expenditures
431
14.7
Which Is Better: To Model in Levels or to Model in Changes?
447
Exercises
449
ALE 14a: Analyzing the Food Price Sub-Index of the Monthly U.S.
Consumer Price Index
45
1
ALE 14b: Estimating Taylor Rules for How the U.S. Fed Sets Interest Rates (Online)
Part III ADDITIONAL TOPICS IN REGRESSION ANALYSIS
455
Chapter
1 5
REGRESSION MODEUNG WITH PANEL DATA (PART A)
459
15.1
Introduction: A Source of Large (but Likely Heterogeneous) Data Sets
459
15.2
Revisiting the Chapter
5
Illustrative Example Using Data from the
Penn World Table
460
! 5.3
A Multivariate Empirical Example
462
1
5.4
The Fixed Effects and the Between Effects Models
469
15.5
The Random Effects Model
478
1
5.6
Diagnostic Checking of an Estimated Panel Data Model
490
Exercises
500
Appendix
15.1:
Stata Code
for the Generalized Hausman Test
503
Chapter
1 6
REGRESSION MODEUNG WITH PANEL DATA (PART B)
507
16.1
Relaxing Strict Exogeneity: Dynamics and Lagged Dependent Variables
507
1
6.2
Relaxing Strict Exogeneity: The First-Differences Model
5
1
5
16.3
Summary
528
Exercises
529
ALE
1
6a: Assessing the Impact of 4-H Participation on the Standardized Test
Scores of Florida Schoolchildren
53
1
ALE 16b: Using Panel Data Methods to Reanalyze Data from a Public
Goods Experiment (Online)
Chapter
1 7
A CONCISE INTRODUCTION TO TIME-SERIES ANALYSIS AND
FORECASTING (PART A)
536
1
7.1
Introduction: The Difference between Time-Series Analysis and
Time-Series Econometrics
536
17.2
Optimal Forecasts: The Primacy of the Conditional-Mean Forecast and
When It Is Better to Use a Biased Forecast
538
17.3
The Crucial Assumption (Stationarity) and the Fundamental Tools:
The Time-Plot and the Sample Correlogram
543
17.4
A Polynomial in the Lag Operator and Its Inverse: The Key to Understanding
and Manipulating Linear Time-Series Models
559
17.5
Identification/Estimation/Checking/Forecasting of an Invertible MA(<7)
Model
563
XII
TABLE OF
CONTENTS
Chapter
18
Chapter
J
9
Chapter
20
17.6
Identification/Estimation/Checking/Forecasting of a Stationary
AR)/?)
Model
575
17.7
ARMAf/?,
q)
Models and a Summary of the Box-Jenkins Modeling Algorithm
581
Exercises
586
ALE 17a: Conditional Forecasting Using a Large-Scale Macroeconometric Model
589
ALE
1
7b: Modeling U.S. GNP (Online)
A CONCISE INTRODUCTION TO TIME-SERIES ANALYSIS AND FORECASTING
(PART B)
18.
1 Integrated
-
ARIMA(/?,
d,q)
-
Models and Trend like Behavior
18.2
A Univariate Application: Modeling the Monthly U.S. Treasury Bill Rate
18.3
Seasonal Time-Series Data and
ARMA Deseasonalization
of the U.S. Total
Nonfarm Payroll Time-Series
18.4
Multivariate Time-Series Models
1
8.5
Post-Sample Model Forecast Evaluation and Testing for Granger-Causation
18.6
Modeling Nonlinear Serial Dependence in a Time-Series
18.7
Additional Topics in Forecasting
Exercises
ALE
1
8a: Modeling the South Korean Won
-
U.S. Dollar Exchange Rate
ALE
1
8b: Modeling the Daily Returns to Ford Motor Company Stock
PARAMETER ESTIMATION BEYOND CURVE-FITTING: MLE (WITH AN
APPLICATION TO BINARY-CHOICE MODELS) AND GMM (WITH AN
APPLICATION TO IV REGRESSION)
19.1
Introduction
19.2
Maximum Likelihood Estimation of a Simple Bivariate Regression Model
19.3
Maximum Likelihood Estimation of Binary-Choice Regression Models
19.4
Generalized Method of Moments (GMM) Estimation
Exercises
ALE
1
9a:
Probit
Modeling of the Determinants of Labor Force Participation
Appendix
19.1:
GMM Estimation of
β
in the Bivariate Regression Model
(Optimal Penalty-Weights and Sampling Distribution)
CONCLUDING COMMENTS
20.1
The Goals of This Book
20.2
Diagnostic Checking and Model Respecification
20.3
The Four Big Mistakes
Mathematics Review
Index
595
595
604
611
617
622
623
637
645
645
(Online)
647
647
648
653
658
671
674
678
681
681
683
685
693
699
|
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| discipline | Wirtschaftswissenschaften |
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| indexdate | 2024-07-10T00:11:52Z |
| institution | BVB |
| isbn | 9780470591826 |
| language | English |
| lccn | 2011041421 |
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| physical | XXV, 710 S. graph. Darst. |
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| spelling | Ashley, Richard A. 1950- Verfasser (DE-588)170031152 aut Fundamentals of applied econometrics by Richard A. Ashley Hoboken Wiley 2012 XXV, 710 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Anwendung (DE-588)4196864-5 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s Anwendung (DE-588)4196864-5 s b DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024666401&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ashley, Richard A. 1950- Fundamentals of applied econometrics Anwendung (DE-588)4196864-5 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4196864-5 (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Fundamentals of applied econometrics |
| title_auth | Fundamentals of applied econometrics |
| title_exact_search | Fundamentals of applied econometrics |
| title_full | Fundamentals of applied econometrics by Richard A. Ashley |
| title_fullStr | Fundamentals of applied econometrics by Richard A. Ashley |
| title_full_unstemmed | Fundamentals of applied econometrics by Richard A. Ashley |
| title_short | Fundamentals of applied econometrics |
| title_sort | fundamentals of applied econometrics |
| topic | Anwendung (DE-588)4196864-5 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Anwendung Ökonometrie Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024666401&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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