Hands-on intermediate econometrics using R: templates for extending dozens of practical examples
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Singapore [u.a.]
World Scientific
2008
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017549051&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXVII, 512 S. graph. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 9789812818850 9812818855 |
Internformat
MARC
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| 008 | 090515s2008 xx d||| |||| 00||| eng d | ||
| 020 | |a 9789812818850 |9 978-981-281-885-0 | ||
| 020 | |a 9812818855 |9 981-281-885-5 | ||
| 035 | |a (OCoLC)228372334 | ||
| 035 | |a (DE-599)BVBBV035492682 | ||
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| 049 | |a DE-355 |a DE-945 |a DE-M382 |a DE-91G |a DE-384 |a DE-739 | ||
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| 084 | |a QH 300 |0 (DE-625)141566: |2 rvk | ||
| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 084 | |a DAT 307f |2 stub | ||
| 084 | |a WIR 017f |2 stub | ||
| 100 | 1 | |a Vinod, Hrishikesh D. |d 1939- |e Verfasser |0 (DE-588)135564700 |4 aut | |
| 245 | 1 | 0 | |a Hands-on intermediate econometrics using R |b templates for extending dozens of practical examples |c Hrishikesh D. Vinod |
| 264 | 1 | |a Singapore [u.a.] |b World Scientific |c 2008 | |
| 300 | |a XXVII, 512 S. |b graph. Darst. |e 1 CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 7 | |a Ökonometrie |2 stw | |
| 650 | 4 | |a Econometrics |x Computer programs | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Ökonometrie |0 (DE-588)4132280-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | 1 | |a Ökonometrie |0 (DE-588)4132280-0 |D s |
| 689 | 0 | |C b |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017549051&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-017549051 | |
Datensatz im Suchindex
| DE-BY-TUM_call_number | 0102 WIR 017f 2010 A 8573 |
|---|---|
| DE-BY-TUM_katkey | 1735755 |
| DE-BY-TUM_location | 01 |
| DE-BY-TUM_media_number | 040010238890 |
| _version_ | 1821932832683982848 |
| adam_text | Contents
Preface vii
Foreword
xv
1.
Production Function and Regression Methods Using
R
1
1.1.
R
and Microeconometric Preliminaries
.......... 2
1.1.1.
Data on Metals Production Available in
R
.... 3
1.1.2.
Descriptive Statistics Using
R
........... 4
1.1.3.
Writing Skewness and Kurtosis Functions in
R
. . 5
1.1.4.
Units of Measurement and Numerical Reliability
of Regressions
.................... 6
1.1.5.
Basic Graphics in
R
................. 7
1.1.6.
The Isoquant
..................... 8
1.1.7.
Total Productivity of an Input
........... 9
1.1.8.
The Marginal Productivity (MP) of an Input
... 9
1.1.9.
Slope of the Isoquant and MRTS
......... 9
1.1.10.
Scale Elasticity as the Returns to Scale
Parameter
...................... 11
1.1.11.
Elasticity of Substitution
.............. 12
1.1.12.
Typical Steps in Empirical Work
.......... 13
1.2.
Preliminary Regression Theory: Results Using
R
..... 13
1.2.1.
Regression as an Object
regi
in
R
........ 16
1.2.2.
Accessing Objects Within an
R
Object by Using
the Dollar Symbol
.................. 17
1.3.
Deeper Regression Theory: Diagonals of the Hat Matrix
. 18
1.4.
Discussion of Four Diagnostic Plots by
R
......... 20
1.5.
Testing Constant Returns and
3D
Scatter Plots
...... 23
1.6.
Homothetic Production and Cost Functions
........ 26
xx
Hands-on Intermediate Econometrics Using
R
1.6.1.
Euler
Theorem and Duality Theorem
....... 29
1.6.2.
Profit Maximizing Solutions
............ 30
1.6.3.
Elasticity of Total Cost w.r.t. Output
....... 31
1.7.
Miscellaneous
Microeconomic
Topics
............ 32
1.7.1.
Analytic Input Demand Function for the
Cobb-Douglas Form
................. 32
1.7.2.
Separability in the Presence of Three
or More Inputs
.................... 32
1.7.3.
Two or More Outputs as Joint Outputs
...... 33
1.7.4.
Economies of Scope
................. 33
1.8.
Nonhomogeneous Production Functions
.......... 34
1.8.1.
Three-Input Production Function for Widgets
. . 34
1.8.2.
Isoquant Plotting for a Bell System Production
Function
....................... 42
1.9.
Collinearity Problem, Singular Value
Decomposition
(SVD),
and Ridge Regression
....... 45
1.9.1.
What is Collinearity?
................ 45
1.9.2.
Consequences of Near Collinearity
......... 48
1.9.3.
Regression Theory Using the Singular Value
Decomposition
.................... 51
1.10.
Near Collinearity Solutions by Coefficient Shrinkage
... 55
1.10.1.
Ridge Regression
.................. 57
1.10.2.
Principal Components Regression
......... 61
1.11.
Bell System Production Function in Anti-Trust Trial
... 62
1.11.1.
Collinearity Diagnostics for Bell Data Trans-Log
. 65
1.11.2.
Shrinkage Solution and Ridge Regression
for Bell Data
..................... 65
1.11.3.
Ridge Regression from Existing
R
Packages
. ... 66
1.12.
Comments on Wrong Signs, Collinearity,
and Ridge Scaling
...................... 69
1.12.1.
Concluding Comments on the
1982
Bell System
Breakup
....................... 75
1.13.
Data Appendix
........................ 75
2.
Univariate Time Series Analysis with
R
77
2.1.
Econometric Univariate Time Series are Ubiquitous
.... 77
2.2.
Stochastic Difference Equations
............... 81
Contents xxi
2.3.
Second-Order Stochastic Difference Equation
and Business Cycles
..................... 85
2.3.1.
Complex Number Solution of the Stochastic AR(2)
Difference Equation
................. 87
2.3.2.
General Solution to
ARMA
(ρ,ρ
- 1)
Stochastic
Difference Equations
................ 89
2.4.
Properties of ARIMA Models
................ 91
2.4.1.
Identification of the Lag Order
........... 93
2.4.2.
ARIMA Estimation
................. 100
2.4.3.
ARIMA Diagnostic Checking
............ 101
2.5.
Stochastic Process and Stationarity
............. 108
2.5.1.
Stochastic Process and Underlying
Probability Space
.................. 108
2.5.2.
Autocovariance of a Stochastic Process
and Ergodicity
.................... 110
2.5.3.
Stationary Process
.................. 112
2.5.4.
Detrending and Differencing to Achieve
Stationarity
..................... 117
2.6.
Mean Reversion
........................ 129
2.7.
Autocovariance Generating Functions (AGF)
and the Power Spectrum
................... 132
2.7.1.
How to Get the Power Spectrum from the AGF?
. 133
2.8.
Explicit Modeling of Variance (ARCH, GARCH Models.)
. 136
2.9.
Tests of Independence, Neglected Nonlinearity,
Turning Points
........................ 139
2.10.
Long Memory Models and Fractional Differencing
..... 143
2.11.
Forecasting
.......................... 147
2.12.
Concluding Remarks and Examples
............. 150
3.
Divariate
Time Series Analysis Including Stochastic
Diffusion and Cointegration
153
3.1.
Autoregressive
Distributed Lag (ARDL) Models
..... 153
3.2.
Economic Interpretations of ARDL
(1,1)
Model
...... 161
3.2.1.
Description of Ml to Mil Model Specifications
. . 162
3.2.2.
ARDL(0,q) as M12 Model, Impact and Long-Run
Multipliers
...................... 166
3.2.3.
Adaptive Expectations Model to Test Rational
Expectations Hypothesis
.............. 167
xxii
Hands-on Intermediate Econometrics Using
R
3.2.4.
Statistical Inference and Estimation with Lagged-
Dependent Variables
................ 168
3.2.5.
Identification Problems Involving Expectational
Variables (I. Fisher Example)
........... 168
3.2.6.
Impulse Response, Mean Lag and Insights from
a Polynomials in
L
................. 169
3.2.7.
Choice Between Ml to Mil Models Using
R
... 170
3.3.
Stochastic Diffusion Models for Asset Prices
........ 176
3.4.
Spurious Regression (R2
>
Durbin
Watson)
and Cointegration
...................... 183
3.4.1.
Definition of a Process Integrated of Order d, I{d)
183
3.4.2.
Cointegration Definition and Discussion
...... 184
3.4.3.
Error Correction Models of Cointegration
..... 185
3.4.4.
Economic Equilibria and Error Reductions
through Learning
.................. 186
3.4.5.
Signs and Significance of Coefficients on Past
Errors while Agents Learn
............. 187
3.5.
Granger Causality Testing
.................. 189
4.
Utility Theory and Empirical Implications
191
4.1.
Utility Theory
........................ 191
4.1.1.
Expected Utility Theory
(EUT)
.......... 192
4.1.2.
Arrrow-Pratt Coefficient of Absolute Risk
Aversion
(CARA)
.................. 197
4.1.3.
Risk Premium Needed to Encourage
Risky Investments
.................. 199
4.1.4.
Taylor Series Links
EUT,
Moments of f(x)
and Derivatives of U(x)
............... 200
4.2.
Non-Expected Utility Theory
................ 202
4.2.1. Lorenz
Curve Scaling over the Unit Square
.... 203
4.2.2.
Mapping From
EUT
to Non-EUT within the Unit
Square to Get Decision Weights
.......... 206
4.3.
Incorporating Utility Theory into Risk
Measurement and Stochastic Dominance
.......... 210
4.3.1.
Class Dl of Utility Functions and Investors
. . . . 210
4.3.2.
Class D2 of Utility Functions and Investors
.... 210
4.3.3.
Explicit Utility Functions and Arrow-Pratt
Measures of Risk Aversion
............. 211
Contents xxiii
4.3.4.
Class D3 of
Utility
Functions and Investors
.... 212
4.3.5.
Class D4 of Utility Functions and Investors
. . . . 212
4.3.6.
First-Order Stochastic Dominance (1SD)
..... 214
4.3.7.
Second-Order Stochastic Dominance (2SD)
.... 216
4.3.8.
Third-Order Stochastic Dominance (3SD)
..... 217
4.3.9.
Fourth-Order Stochastic Dominance (4SD)
.... 218
4.3.10.
Empirical Checking of Stochastic Dominance
Using Matrix Multiplications and Incorporation
of 4DPs of Non-EUT
................ 218
5.
Vector Models for Multivariate Problems
227
5.1.
Introduction and
VAR
Models
............... 227
5.1.1.
Some
R
Packages for Vector Modeling
....... 228
5.1.2.
Vector
Autoregression
or
VAR
Models
...... 228
5.1.3.
Data Collection Tips Using
R
........... 229
5.1.4.
VAR
Estimation of Sims Model
.......... 237
5.1.5.
Granger-Causality Analysis in
VAR
Models
.... 240
5.1.6.
Forecasting Out-of-Sample in
VAR
Models
.... 242
5.1.7.
Impulse Response Analysis in
VAR
Models
.... 243
5.2.
Multivariate Regressions: Canonical Correlations
..... 248
5.2.1.
Why Canonical Correlation is Not Popular So Far
251
5.3.
VAR
Estimation and
Cointegration
Testing
Using Canonical Correlations
................ 257
5.4.
Final Remarks: Multivariate Statisics Using
R
....... 259
6.
Simultaneous Equation Models
261
6.1.
Introduction
.......................... 261
6.1.1.
Simultaneous Equation Notation System with
Stars and Subscripts
................ 263
6.1.2.
Simultaneous Equations Bias and the
Reduced Form
.................... 266
6.1.3.
Successively Weaker Assumptions Regarding
the Nature of the
Z j
Matrix of Regressors
.... 269
6.1.4.
Reduced Form Estimation and Other Alternatives
to OLS
........................ 269
6.1.5.
Assumptions of Simultaneous Equations Models
. 271
xxiv
Hands-on Intermediate Econometrics Using
R
6.2.
Instrumental Variables and Generalized Least Squares
. . 272
6.2.1.
The Instrumental Variables (IV) and Generalized
IV
(GIV)
Estimator
................. 273
6.2.2.
Choice Between OLS and IV by Using
Wu-Hausman Specification Test
.......... 275
6.3.
Limited Information and Two-Stage Least Squares
.... 277
6.3.1.
Two-Stage Least Squares
.............. 277
6.3.2.
The
Ä-class
Estimator
................ 278
6.3.3.
Limited Information Maximum Likelihood
(LIML) Estimator
.................. 280
6.4.
Identification of Simultaneous Equation Models
...... 282
6.4.1.
Identification is Uniquely Going from the
Reduced Form to the Structure
.......... 285
6.5.
Full Information and Three-Stage Least Squares (3SLS)
. 288
6.5.1.
Full Information Maximum Likelihood
...... 293
6.6.
Potential of Simultaneous Equations
Beyond Econometrics
.................... 294
7.
Limited Dependent Variable (GLM) Models
295
7.1.
Problems with Dummy Dependent Variables
....... 295
7.1.1.
Proof of the Claim that
Var(e¿) = P¿(1
-
P¿)
. . . 300
7.1.2.
The General Linear Model from Biostatistics
. . . 304
7.1.3.
Marginal Effects (Partial Derivatives) in
Logit-Type GLM Models
.............. 308
7.1.4.
Further Generalizations of Logit and
Probit
Models
.................... 309
7.1.5.
Ordered Response
.................. 312
7.2.
Quasi-Likelihood Function for Binary Choice Models
... 314
7.2.1.
The ML Estimator in Binary Choice Models
... 315
7.2.2.
Tobit Model for Censored Dependent Variables
. . 317
7.3.
Heekman
Two-Step Estimator for Self-Selection Bias
. . . 322
7.4.
Time Duration Length (Survival) Models
......... 326
7.4.1.
Probability Distributions and Implied Hazard
Functions
....................... 330
7.4.2.
Parametric Survival (Hazard) Models
....... 331
7.4.3.
Semiparametric Including Cox Proportional
Hazard Models
.................... 333
Contents xxv
8. Dynamic
Optimization and Empirical Analysis
of Consumer Behavior
343
8.1.
Introduction
.......................... 343
8.2.
Dynamic Optimization
..................... 344
8.3.
Hall s Random Walk Model
................. 346
8.3.1.
Data from the Internet and an Implementation
. . 349
8.3.2.
OLS Estimation of the Random Walk Model
... 350
8.3.3.
Direct Estimation of Hall s NLHS Specification
. . 352
8.3.4.
Strong Assumptions and Granger-Causality Tests
356
8.4.
Nonparametric Kernel Estimation
............. 358
8.4.1.
Kernel Estimation of Amorphous
Partials
..... 360
8.5. Wiener-Hopf-
Whittle Model if Consumption
Precedes Income
....................... 364
8.5.1.
Determination of Target Consumption
...... 365
8.5.2.
Implications for Various Puzzles
of Consumer Theory
................ 368
8.6.
Final Remarks on Consumer Theory
............ 369
8.7.
Appendix: Additional
R
Code
................ 370
9.
Single, Double and Maximum Entropy Bootstrap and Inference
377
9.1.
The Motivation and Background Behind Bootstrapping
. 377
9.1.1.
Pivotal Quantity and p-Value
........... 378
9.1.2.
Uncertainty Regarding Proper Density for
Regression Errors Illustrated
............ 380
9.1.3.
The Delta Method for Standard Error
of Functions
..................... 382
9.2.
Description of Parametric iid Bootstrap
.......... 383
9.2.1.
Simulated Sampling Distribution for Statistical
Inference Using OLS Residuals
........... 383
9.2.2.
Steps in a Parametric Approximation
....... 386
9.2.3.
Percentile Confidence Intervals
........... 387
9.2.4.
Reflected Percentile Confidence Interval
for Bias Correction
................. 388
9.2.5.
Significance Tests as Duals to Confidence
Intervals
....................... 388
9.3.
Description of Nonparametric iid Bootstrap
........ 391
9.3.1.
Map Data from Time-Domain
to (Numerical Magnitudes) Values-Domain
.... 391
xxvi
Hands-on Intermediate Econometrics Using
R
9.4.
Double Bootstrap Illustrated with a Nonlinear Model
. . 398
9.4.1.
A Digression on the Size of Resamples
...... 399
9.4.2.
Double Bootstrap Theory Involving Roots
and Uniform Density
................ 399
9.4.3.
GNR Implementation of Nonlinear Regression
for Metals Data
................... 401
9.5.
Maximum Entropy Density Bootstrap
for Time-Series Data
..................... 407
9.5.1.
Wiener, Kolmogorov, Khintchine (WKK)
Ensemble of Time Series
..............408
9.5.2.
Avoiding Unrealistic Properties of iid Bootstrap
. 409
9.5.3.
Maximum Entropy Density is Uniform When
Limits are Known
.................. 410
9.5.4.
Quantiles of the Patchwork of the ME Density
. . 412
9.5.5.
Numerical Illustration of Meboot Package in
R
413
9.5.6.
Simple and Size-Corrected Confidence Bounds
. . 418
10.
Generalized Least Squares,
VARMA,
and Estimating
Functions
419
10.1.
Feasible Generalized Least Squares (GLS) to Adjust for
Autocorrelated Errors and/or Heteroscedasticity
.....419
10.1.1.
Consequences of Ignoring Nonspherical
Errors
Ω ψ Ιτ
.................... 419
10.1.2.
Derivation of the GLS and Efficiency Comparison
420
10.1.3.
Computation of the GLS and Feasible GLS
.... 422
10.1.4.
Improved OLS Inference for Nonspherical Errors
. 424
10.1.5.
Efficient Estimation of
β
Coefficients
....... 425
10.1.6.
An Illustration Using Fisher s
Model for Interest Rates
..............426
10.2.
Vector
ARMA
Estimation for Rational
Expectations Models
..................... 429
10.2.1.
Greater Realism of VARMA(p, q) Models
.....431
10.2.2.
Expectational Variables from Conditional
Forecasts in a General Model
............432
10.2.3.
A Rational Expectation Model Using
VARMA
. . 433
10.2.4.
Further Forecasts, Transfer Function Gains,
and Response Analysis
............... 438
Contents xxvii
10.3. Optimal
Estimating Function (OptEF)
and Generalized Method of Moments (GMM)
....... 443
10.3.1.
Derivation of Optimal Estimating Functions
for Regressions
.................... 443
10.3.2.
Finite Sample Optimality of OptEF
........ 445
10.3.3.
Introduction to the GMM
............. 445
10.3.4.
Cases Where OptEF Viewpoint Dominates GMM
447
10.3.5.
Advantages and Disadvantages of
GMM and OptEF
.................. 449
10.4.
Godambe Pivot Functions (GPFs) and Statistical
Inference
............................ 450
10.4.1.
Application of the Frisch-Waugh Theorem
to Constructing CI95
................ 452
10.4.2.
Steps in Application of GPF to Feasible
GLS Estimation
................... 453
11.
Box-Cox, Loess and Projection Pursuit Regression
459
11.1.
Further
R
Tools for Studying Nonlinear Relations
..... 459
11.2.
Box
-Сох
Transformation
.................. 459
11.2.1.
Logarithmic and Square Root Transformations
. . 459
11.3.
Scatterplot Smoothing and Loess Regressions
....... 463
11.3.1.
Improved Fit (Forecasts) by Loess Smoothing
. . 465
11.4.
Projection Pursuit Methods
................. 466
11.5.
Remarks on Nonlinear Econometrics
............ 477
Appendix
479
References
485
Index
505
|
| any_adam_object | 1 |
| author | Vinod, Hrishikesh D. 1939- |
| author_GND | (DE-588)135564700 |
| author_facet | Vinod, Hrishikesh D. 1939- |
| author_role | aut |
| author_sort | Vinod, Hrishikesh D. 1939- |
| author_variant | h d v hd hdv |
| building | Verbundindex |
| bvnumber | BV035492682 |
| callnumber-first | H - Social Science |
| callnumber-label | HB141 |
| callnumber-raw | HB141 |
| callnumber-search | HB141 |
| callnumber-sort | HB 3141 |
| callnumber-subject | HB - Economic Theory and Demography |
| classification_rvk | QH 300 ST 601 |
| classification_tum | DAT 307f WIR 017f |
| ctrlnum | (OCoLC)228372334 (DE-599)BVBBV035492682 |
| dewey-full | 330.0285/5262 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.0285/5262 |
| dewey-search | 330.0285/5262 |
| dewey-sort | 3330.0285 45262 |
| dewey-tens | 330 - Economics |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV035492682 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T13:36:16Z |
| institution | BVB |
| isbn | 9789812818850 9812818855 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017549051 |
| oclc_num | 228372334 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-945 DE-M382 DE-91G DE-BY-TUM DE-384 DE-739 |
| owner_facet | DE-355 DE-BY-UBR DE-945 DE-M382 DE-91G DE-BY-TUM DE-384 DE-739 |
| physical | XXVII, 512 S. graph. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | World Scientific |
| record_format | marc |
| spellingShingle | Vinod, Hrishikesh D. 1939- Hands-on intermediate econometrics using R templates for extending dozens of practical examples Ökonometrie stw Econometrics Computer programs R (Computer program language) R Programm (DE-588)4705956-4 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4132280-0 |
| title | Hands-on intermediate econometrics using R templates for extending dozens of practical examples |
| title_auth | Hands-on intermediate econometrics using R templates for extending dozens of practical examples |
| title_exact_search | Hands-on intermediate econometrics using R templates for extending dozens of practical examples |
| title_full | Hands-on intermediate econometrics using R templates for extending dozens of practical examples Hrishikesh D. Vinod |
| title_fullStr | Hands-on intermediate econometrics using R templates for extending dozens of practical examples Hrishikesh D. Vinod |
| title_full_unstemmed | Hands-on intermediate econometrics using R templates for extending dozens of practical examples Hrishikesh D. Vinod |
| title_short | Hands-on intermediate econometrics using R |
| title_sort | hands on intermediate econometrics using r templates for extending dozens of practical examples |
| title_sub | templates for extending dozens of practical examples |
| topic | Ökonometrie stw Econometrics Computer programs R (Computer program language) R Programm (DE-588)4705956-4 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Ökonometrie Econometrics Computer programs R (Computer program language) R Programm |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017549051&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT vinodhrishikeshd handsonintermediateeconometricsusingrtemplatesforextendingdozensofpracticalexamples |
Inhaltsverzeichnis
Paper/Kapitel scannen lassen
Paper/Kapitel scannen lassen
Teilbibliothek Mathematik & Informatik
| Signatur: |
0102 WIR 017f 2010 A 8573
Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |