Introductory econometrics for finance:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge Univ. Press
2008
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später ersch., unveränd. Nachdr.; Literaturverz. S. 629 - 640 |
| Beschreibung: | XXIII, 648 S. Ill., graph. Darst. |
| ISBN: | 9780521694681 052169468X 9780521873062 |
Internformat
MARC
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| 100 | 1 | |a Brooks, Chris |d 1971- |e Verfasser |0 (DE-588)1012858766 |4 aut | |
| 245 | 1 | 0 | |a Introductory econometrics for finance |c Chris Brooks |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Cambridge |b Cambridge Univ. Press |c 2008 | |
| 300 | |a XXIII, 648 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später ersch., unveränd. Nachdr.; Literaturverz. S. 629 - 640 | ||
| 650 | 7 | |a Finanzmarkt |2 stw | |
| 650 | 7 | |a Multivariate Analyse |2 stw | |
| 650 | 7 | |a Regression |2 stw | |
| 650 | 7 | |a Simulation |2 stw | |
| 650 | 7 | |a Theorie |2 stw | |
| 650 | 7 | |a Zeitreihenanalyse |2 stw | |
| 650 | 7 | |a Ökonometrie |2 stw | |
| 650 | 7 | |a Ökonometrisches Modell |2 stw | |
| 650 | 4 | |a Ökonometrisches Modell | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Finance |x Econometric models | |
| 650 | 0 | 7 | |a Finanzmathematik |0 (DE-588)4017195-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kreditmarkt |0 (DE-588)4073788-3 |2 gnd |9 rswk-swf |
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| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804137346225930240 |
|---|---|
| adam_text | Contents
List of figures page
xii
List of tables
xiv
List of boxes
xvi
List of
Screenshots xvii
Preface to the second edition
xix
Acknowledgements
xxiv
1
Introduction
1
1.1
What is econometrics?
1
1.2
Is financial econometrics different from economic econometrics?
2
1.3
Types of data
3
1.4
Returns in financial modelling
7
1.5
Steps involved in formulating an econometric model
9
1.6
Points to consider when reading articles in empirical finance
10
1.7
Econometric packages for modelling financial data
11
1.8
Outline of the remainder of this book
22
1.9
Further reading
25
Appendix: Econometric software package suppliers
26
2
A brief overview of the classical linear regression model
27
2.1
What is a regression model?
27
2.2
Regression versus correlation
28
2.3
Simple regression
28
2.4
Some further terminology
37
2.5
Simple linear regression in EViews
-
estimation of an optimal
hedge ratio
40
2.6
The assumptions underlying the classical linear regression model
43
2.7
Properties of the OLS estimator
44
2.8
Precision and standard errors
46
2.9
An introduction to statistical inference
51
2.10
A special type of hypothesis test: the t-ratio
2.11
An example of the use of a simple t-test to test a theory in finance:
can US mutual funds beat the market?
2.12
Can UK unit trust managers beat the market?
2.13
The overreaction hypothesis and the UK stock market
2.14
The exact significance level
2.15
Hypothesis testing in EViews
-
example
1:
hedging revisited
2.16
Estimation and hypothesis testing in EViews
-
example
2:
the CAPM
Appendix: Mathematical derivations of CLRM results
3
Further development and analysis of the classical linear
regression model
3.1
Generalising the simple model to multiple linear regression
3.2
The constant term
3.3
How are the parameters (the elements of the
β
vector) calculated
in the generalised case?
3.4
Testing multiple hypotheses: the F-test
3.5
Sample EViews output for multiple hypothesis tests
3.6
Multiple regression in EViews using an APT-style model
3.7
Data mining and the true size of the test
3.8
Goodness of fit statistics
3.9
Hedonic pricing models
3.10
Tests of non-nested hypotheses
Appendix
3.1:
Mathematical derivations of CLRM results
Appendix
3.2:
A brief introduction to factor models and principal
components analysis
4
Classical linear regression model assumptions and
diagnostic tests
4.1
Introduction
4.2
Statistical distributions for diagnostic tests
4.3
Assumption
1:
E{ut)
= 0
4.4
Assumption
2:
var(«t)
=
σ2
<
oo
4.5
Assumption
3:
covO,·,
u¡)
= 0
for
і
φ
j
4.6
Assumption
4:
the xt are non-stochastic
4.7
Assumption
5:
the disturbances are normally distributed
4.8
Multicollinearity
4.9
Adopting the wrong functional form
4.10
Omission of an important variable
4.11
Inclusion of an irrelevant variable
4.12 Parameter
stability tests
180
4.13
A strategy for constructing econometric models and a discussion
of model-building philosophies
191
4.14
Determinants of sovereign credit ratings
194
5
Univariate time series modelling and forecasting
206
5.1
Introduction
206
5.2
Some notation and concepts
207
5.3
Moving average processes
211
5.4
Autoregressive
processes
215
5.5
The partial autocorrelation function
222
5.6
ARMA
processes
223
5.7
Building
ARMA
models: the Box-Jenkins approach
230
5.8
Constructing
ARMA
models in EViews
234
5.9
Examples of time series modelling in finance
239
5.10
Exponential smoothing
241
5.11
Forecasting in econometrics
243
5.12
Forecasting using
ARMA
models in EViews
256
5.13
Estimating exponential smoothing models using EViews
258
6
IVI ulti variate
models
265
6.1
Motivations
265
6.2
Simultaneous equations bias
268
6.3
So how can simultaneous equations models be validly estimated?
269
6.4
Can the original coefficients be retrieved from the
жѕ7
269
6.5
Simultaneous equations in finance
272
6.6
A definition of exogeneity
273
6.7
Triangular systems
275
6.8
Estimation procedures for simultaneous equations systems
276
6.9
An application of a simultaneous equations approach to
modelling bid-ask spreads and trading activity
279
6.10
Simultaneous equations modelling using EViews
285
6.11
Vector
autoregressive
models
290
6.12
Does the
VAR
include contemporaneous terms?
295
6.13
Block significance and causality tests
297
6.14
VARs with exogenous variables
298
6.15
Impulse responses and variance decompositions
298
6.16
VAR
model example: the interaction between property returns and
the macroeconomy
302
6.17
VAR
estimation in EViews
308
7
Modelling
long-run relationships in finance
318
7.1
Stationarity and unit root testing
318
7.2
Testing for unit roots in EViews
331
7.3
Cointegration
335
7.4
Equilibrium correction or error correction models
337
7.5
Testing for
cointegration in
regression: a residuals-based approach
339
7.6
Methods of parameter estimation in cointegrated systems
341
7.7
Lead-lag and long-term relationships between spot and
futures markets
343
7.8
Testing for and estimating cointegrating systems using the
Johansen technique based on VARs
350
7.9
Purchasing power parity
355
7.10
Cointegration
between international bond markets
357
7.11
Testing the expectations hypothesis of the term structure of
interest rates
362
7.12
Testing for
cointegration
and modelling cointegrated systems
using EViews
365
Modelling volatility and correlation
379
Motivations: an excursion into non-linearity land
379
Models for volatility
383
Historical volatility
383
Implied volatility models
384
Exponentially weighted moving average models
384
Autoregressive
volatility models
385
Autoregressive
conditionally heteroscedastic (ARCH) models
386
Generalised ARCH (GARCH) models
392
Estimation of ARCH/GARCH models
394
Extensions to the basic GARCH model
404
Asymmetric GARCH models
404
The GJR model
405
The EGARCH model
406
GJR and EGARCH in EViews
406
Tests for asymmetries in volatility
408
GARCH-in-mean
409
Uses of GARCH-type models including volatility forecasting
411
Testing non-linear restrictions or testing hypotheses about
non-linear models
417
Volatility forecasting: some examples and results from the
literature
420
Stochastic volatility models revisited
427
8.21
Forecasting covariances and correlations
428
8.22
Covariance modelling and forecasting in finance: some examples
429
8.23
Historical covariance and correlation
431
8.24
Implied covariance models
431
8.25
Exponentially weighted moving average model for covariances
432
8.26
Multivariate GARCH models
432
8.27
A multivariate GARCH model for the
С АРМ
with time-varying
covariances
436
8.28
Estimating a time-varying hedge ratio for FTSE stock index returns
437
8.29
Estimating multivariate GARCH models using EViews
441
Appendix: Parameter estimation using maximum likelihood
444
9
Switching models
451
9.1
Motivations
451
9.2
Seasonalities in financial markets: introduction and
literature review
454
9.3
Modelling seasonality in financial data
455
9.4
Estimating simple piecewise linear functions
462
9.5
Markov switching models
464
9.6
A Markov switching model for the real exchange rate
466
9.7
A Markov switching model for the gilt-equity yield ratio
469
9.8
Threshold
autoregressive
models
473
9.9
Estimation of threshold
autoregressive
models
474
9.10
Specification tests in the context of Markov switching and
threshold
autoregressive
models: a cautionary note
476
9.11
A SETAR model for the French franc-German mark exchange rate
477
9.12
Threshold models and the dynamics of the FTSE
100
index and
index futures markets
480
9.13
A note on regime switching models and forecasting accuracy
484
10
Panel data
487
10.1
Introduction
-
what are panel techniques and why are they used?
487
10.2
What panel techniques are available?
489
10.3
The fixed effects model
490
10.4
Time-fixed effects models
493
10.5
Investigating banking competition using a fixed effects model
494
10.6
The random effects model
498
10.7
Panel data application to credit stability of banks in Central and
Eastern Europe
499
10.8
Panel data with EViews
502
10.9
Further reading
509
11
Limited dependent
variable
models
511
11.1
Introduction and motivation
511
11.2
The linear probability model
512
11.3
The logit model
514
11.4
Using a logit to test the pecking order hypothesis
515
11.5
The
probit
model
517
11.6
Choosing between the logit and
probit
models
518
11.7
Estimation of limited dependent variable models
518
11.8
Goodness of fit measures for linear dependent variable models
519
11.9
Multinomial linear dependent variables
521
11.10
The pecking order hypothesis revisited
-
the choice between
financing methods
525
Ordered response linear dependent variables models
527
Are unsolicited credit ratings biased downwards? An ordered
probit
analysis
528
11.13
Censored and truncated dependent variables
533
11.14
Limited dependent variable models in EViews
537
Appendix: The maximum likelihood estimator for logit and
probit
models
544
12
Simulation methods
546
12.1
Motivations
546
12.2
Monte Carlo simulations
547
12.3
Variance reduction techniques
549
12.4
Bootstrapping
553
12.5
Random number generation
557
12.6
Disadvantages of the simulation approach to econometric or
financial problem solving
558
12.7
An example of Monte Carlo simulation in econometrics: deriving a
set of critical values for a Dickey-Fuller test
559
12.8
An example of how to simulate the price of a financial option
565
12.9
An example of bootstrapping to calculate capital risk requirements
571
13
Conducting empirical research or doing a project or dissertation
in finance
585
13.1
What is an empirical research project and what is it for?
585
13.2
Selecting the topic
586
13.3
Sponsored or independent research?
590
13.4
The research proposal
590
13.5
Working papers and literature on the internet
591
13.6
Getting the data
591
13.7
Choice of computer software
13.8
How might the finished project look?
13.9
Presentational issues
14
Recent and future developments in the modelling
of financial time series
14.1
Summary of the book
14.2
What was not covered in the book
14.3
Financial econometrics: the future?
14.4
The final word
593
593
597
598
598
598
602
606
Appendix
1
A review of some fundamental mathematical and
statistical concepts
Al
Introduction
A2 Characteristics of probability distributions
A3
Properties of logarithms
A4
Differential calculus
A5 Matrices
A6 The eigenvalues of a matrix
Appendix
2
Tables of statistical distributions
Appendix
3
Sources of data used in this book
607
607
607
608
609
611
614
616
628
References
Index
629
641
|
| adam_txt |
Contents
List of figures page
xii
List of tables
xiv
List of boxes
xvi
List of
Screenshots xvii
Preface to the second edition
xix
Acknowledgements
xxiv
1
Introduction
1
1.1
What is econometrics?
1
1.2
Is financial econometrics different from 'economic econometrics?
2
1.3
Types of data
3
1.4
Returns in financial modelling
7
1.5
Steps involved in formulating an econometric model
9
1.6
Points to consider when reading articles in empirical finance
10
1.7
Econometric packages for modelling financial data
11
1.8
Outline of the remainder of this book
22
1.9
Further reading
25
Appendix: Econometric software package suppliers
26
2
A brief overview of the classical linear regression model
27
2.1
What is a regression model?
27
2.2
Regression versus correlation
28
2.3
Simple regression
28
2.4
Some further terminology
37
2.5
Simple linear regression in EViews
-
estimation of an optimal
hedge ratio
40
2.6
The assumptions underlying the classical linear regression model
43
2.7
Properties of the OLS estimator
44
2.8
Precision and standard errors
46
2.9
An introduction to statistical inference
51
2.10
A special type of hypothesis test: the t-ratio
2.11
An example of the use of a simple t-test to test a theory in finance:
can US mutual funds beat the market?
2.12
Can UK unit trust managers beat the market?
2.13
The overreaction hypothesis and the UK stock market
2.14
The exact significance level
2.15
Hypothesis testing in EViews
-
example
1:
hedging revisited
2.16
Estimation and hypothesis testing in EViews
-
example
2:
the CAPM
Appendix: Mathematical derivations of CLRM results
3
Further development and analysis of the classical linear
regression model
3.1
Generalising the simple model to multiple linear regression
3.2
The constant term
3.3
How are the parameters (the elements of the
β
vector) calculated
in the generalised case?
3.4
Testing multiple hypotheses: the F-test
3.5
Sample EViews output for multiple hypothesis tests
3.6
Multiple regression in EViews using an APT-style model
3.7
Data mining and the true size of the test
3.8
Goodness of fit statistics
3.9
Hedonic pricing models
3.10
Tests of non-nested hypotheses
Appendix
3.1:
Mathematical derivations of CLRM results
Appendix
3.2:
A brief introduction to factor models and principal
components analysis
4
Classical linear regression model assumptions and
diagnostic tests
4.1
Introduction
4.2
Statistical distributions for diagnostic tests
4.3
Assumption
1:
E{ut)
= 0
4.4
Assumption
2:
var(«t)
=
σ2
<
oo
4.5
Assumption
3:
covO,·,
u¡)
= 0
for
і
φ
j
4.6
Assumption
4:
the xt are non-stochastic
4.7
Assumption
5:
the disturbances are normally distributed
4.8
Multicollinearity
4.9
Adopting the wrong functional form
4.10
Omission of an important variable
4.11
Inclusion of an irrelevant variable
4.12 Parameter
stability tests
180
4.13
A strategy for constructing econometric models and a discussion
of model-building philosophies
191
4.14
Determinants of sovereign credit ratings
194
5
Univariate time series modelling and forecasting
206
5.1
Introduction
206
5.2
Some notation and concepts
207
5.3
Moving average processes
211
5.4
Autoregressive
processes
215
5.5
The partial autocorrelation function
222
5.6
ARMA
processes
223
5.7
Building
ARMA
models: the Box-Jenkins approach
230
5.8
Constructing
ARMA
models in EViews
234
5.9
Examples of time series modelling in finance
239
5.10
Exponential smoothing
241
5.11
Forecasting in econometrics
243
5.12
Forecasting using
ARMA
models in EViews
256
5.13
Estimating exponential smoothing models using EViews
258
6
IVI ulti variate
models
265
6.1
Motivations
265
6.2
Simultaneous equations bias
268
6.3
So how can simultaneous equations models be validly estimated?
269
6.4
Can the original coefficients be retrieved from the
жѕ7
269
6.5
Simultaneous equations in finance
272
6.6
A definition of exogeneity
273
6.7
Triangular systems
275
6.8
Estimation procedures for simultaneous equations systems
276
6.9
An application of a simultaneous equations approach to
modelling bid-ask spreads and trading activity
279
6.10
Simultaneous equations modelling using EViews
285
6.11
Vector
autoregressive
models
290
6.12
Does the
VAR
include contemporaneous terms?
295
6.13
Block significance and causality tests
297
6.14
VARs with exogenous variables
298
6.15
Impulse responses and variance decompositions
298
6.16
VAR
model example: the interaction between property returns and
the macroeconomy
302
6.17
VAR
estimation in EViews
308
7
Modelling
long-run relationships in finance
318
7.1
Stationarity and unit root testing
318
7.2
Testing for unit roots in EViews
331
7.3
Cointegration
335
7.4
Equilibrium correction or error correction models
337
7.5
Testing for
cointegration in
regression: a residuals-based approach
339
7.6
Methods of parameter estimation in cointegrated systems
341
7.7
Lead-lag and long-term relationships between spot and
futures markets
343
7.8
Testing for and estimating cointegrating systems using the
Johansen technique based on VARs
350
7.9
Purchasing power parity
355
7.10
Cointegration
between international bond markets
357
7.11
Testing the expectations hypothesis of the term structure of
interest rates
362
7.12
Testing for
cointegration
and modelling cointegrated systems
using EViews
365
Modelling volatility and correlation
379
Motivations: an excursion into non-linearity land
379
Models for volatility
383
Historical volatility
383
Implied volatility models
384
Exponentially weighted moving average models
384
Autoregressive
volatility models
385
Autoregressive
conditionally heteroscedastic (ARCH) models
386
Generalised ARCH (GARCH) models
392
Estimation of ARCH/GARCH models
394
Extensions to the basic GARCH model
404
Asymmetric GARCH models
404
The GJR model
405
The EGARCH model
406
GJR and EGARCH in EViews
406
Tests for asymmetries in volatility
408
GARCH-in-mean
409
Uses of GARCH-type models including volatility forecasting
411
Testing non-linear restrictions or testing hypotheses about
non-linear models
417
Volatility forecasting: some examples and results from the
literature
420
Stochastic volatility models revisited
427
8.21
Forecasting covariances and correlations
428
8.22
Covariance modelling and forecasting in finance: some examples
429
8.23
Historical covariance and correlation
431
8.24
Implied covariance models
431
8.25
Exponentially weighted moving average model for covariances
432
8.26
Multivariate GARCH models
432
8.27
A multivariate GARCH model for the
С АРМ
with time-varying
covariances
436
8.28
Estimating a time-varying hedge ratio for FTSE stock index returns
437
8.29
Estimating multivariate GARCH models using EViews
441
Appendix: Parameter estimation using maximum likelihood
444
9
Switching models
451
9.1
Motivations
451
9.2
Seasonalities in financial markets: introduction and
literature review
454
9.3
Modelling seasonality in financial data
455
9.4
Estimating simple piecewise linear functions
462
9.5
Markov switching models
464
9.6
A Markov switching model for the real exchange rate
466
9.7
A Markov switching model for the gilt-equity yield ratio
469
9.8
Threshold
autoregressive
models
473
9.9
Estimation of threshold
autoregressive
models
474
9.10
Specification tests in the context of Markov switching and
threshold
autoregressive
models: a cautionary note
476
9.11
A SETAR model for the French franc-German mark exchange rate
477
9.12
Threshold models and the dynamics of the FTSE
100
index and
index futures markets
480
9.13
A note on regime switching models and forecasting accuracy
484
10
Panel data
487
10.1
Introduction
-
what are panel techniques and why are they used?
487
10.2
What panel techniques are available?
489
10.3
The fixed effects model
490
10.4
Time-fixed effects models
493
10.5
Investigating banking competition using a fixed effects model
494
10.6
The random effects model
498
10.7
Panel data application to credit stability of banks in Central and
Eastern Europe
499
10.8
Panel data with EViews
502
10.9
Further reading
509
11
Limited dependent
variable
models
511
11.1
Introduction and motivation
511
11.2
The linear probability model
512
11.3
The logit model
514
11.4
Using a logit to test the pecking order hypothesis
515
11.5
The
probit
model
517
11.6
Choosing between the logit and
probit
models
518
11.7
Estimation of limited dependent variable models
518
11.8
Goodness of fit measures for linear dependent variable models
519
11.9
Multinomial linear dependent variables
521
11.10
The pecking order hypothesis revisited
-
the choice between
financing methods
525
Ordered response linear dependent variables models
527
Are unsolicited credit ratings biased downwards? An ordered
probit
analysis
528
11.13
Censored and truncated dependent variables
533
11.14
Limited dependent variable models in EViews
537
Appendix: The maximum likelihood estimator for logit and
probit
models
544
12
Simulation methods
546
12.1
Motivations
546
12.2
Monte Carlo simulations
547
12.3
Variance reduction techniques
549
12.4
Bootstrapping
553
12.5
Random number generation
557
12.6
Disadvantages of the simulation approach to econometric or
financial problem solving
558
12.7
An example of Monte Carlo simulation in econometrics: deriving a
set of critical values for a Dickey-Fuller test
559
12.8
An example of how to simulate the price of a financial option
565
12.9
An example of bootstrapping to calculate capital risk requirements
571
13
Conducting empirical research or doing a project or dissertation
in finance
585
13.1
What is an empirical research project and what is it for?
585
13.2
Selecting the topic
586
13.3
Sponsored or independent research?
590
13.4
The research proposal
590
13.5
Working papers and literature on the internet
591
13.6
Getting the data
591
13.7
Choice of computer software
13.8
How might the finished project look?
13.9
Presentational issues
14
Recent and future developments in the modelling
of financial time series
14.1
Summary of the book
14.2
What was not covered in the book
14.3
Financial econometrics: the future?
14.4
The final word
593
593
597
598
598
598
602
606
Appendix
1
A review of some fundamental mathematical and
statistical concepts
Al
Introduction
A2 Characteristics of probability distributions
A3
Properties of logarithms
A4
Differential calculus
A5 Matrices
A6 The eigenvalues of a matrix
Appendix
2
Tables of statistical distributions
Appendix
3
Sources of data used in this book
607
607
607
608
609
611
614
616
628
References
Index
629
641 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Brooks, Chris 1971- |
| author_GND | (DE-588)1012858766 |
| author_facet | Brooks, Chris 1971- |
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| author_sort | Brooks, Chris 1971- |
| author_variant | c b cb |
| building | Verbundindex |
| bvnumber | BV023092575 |
| callnumber-first | H - Social Science |
| callnumber-label | HG173 |
| callnumber-raw | HG173 |
| callnumber-search | HG173 |
| callnumber-sort | HG 3173 |
| callnumber-subject | HG - Finance |
| classification_rvk | QH 237 QH 300 QH 310 QH 330 |
| classification_tum | WIR 175f WIR 017f |
| ctrlnum | (OCoLC)263433100 (DE-599)BVBBV023092575 |
| dewey-full | 332.015195 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.015195 |
| dewey-search | 332.015195 |
| dewey-sort | 3332.015195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV023092575 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:41:37Z |
| indexdate | 2024-07-09T21:10:50Z |
| institution | BVB |
| isbn | 9780521694681 052169468X 9780521873062 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016295424 |
| oclc_num | 263433100 |
| open_access_boolean | |
| owner | DE-739 DE-19 DE-BY-UBM DE-945 DE-384 DE-1049 DE-521 DE-355 DE-BY-UBR DE-83 DE-11 |
| owner_facet | DE-739 DE-19 DE-BY-UBM DE-945 DE-384 DE-1049 DE-521 DE-355 DE-BY-UBR DE-83 DE-11 |
| physical | XXIII, 648 S. Ill., graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | Brooks, Chris 1971- Verfasser (DE-588)1012858766 aut Introductory econometrics for finance Chris Brooks 2. ed. Cambridge Cambridge Univ. Press 2008 XXIII, 648 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später ersch., unveränd. Nachdr.; Literaturverz. S. 629 - 640 Finanzmarkt stw Multivariate Analyse stw Regression stw Simulation stw Theorie stw Zeitreihenanalyse stw Ökonometrie stw Ökonometrisches Modell stw Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Kreditmarkt (DE-588)4073788-3 gnd rswk-swf Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Ökonometrie (DE-588)4132280-0 s Finanzmathematik (DE-588)4017195-4 s Statistik (DE-588)4056995-0 s DE-604 Kreditmarkt (DE-588)4073788-3 s 1\p DE-604 Überarbeitung als 3. ed. 2014 978-1-107-03466-2 978-1-107-66145-5 (DE-604)BV041747422 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016295424&sequence=000002&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 | Brooks, Chris 1971- Introductory econometrics for finance Finanzmarkt stw Multivariate Analyse stw Regression stw Simulation stw Theorie stw Zeitreihenanalyse stw Ökonometrie stw Ökonometrisches Modell stw Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik (DE-588)4017195-4 gnd Statistik (DE-588)4056995-0 gnd Kreditmarkt (DE-588)4073788-3 gnd Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4017195-4 (DE-588)4056995-0 (DE-588)4073788-3 (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Introductory econometrics for finance |
| title_auth | Introductory econometrics for finance |
| title_exact_search | Introductory econometrics for finance |
| title_exact_search_txtP | Introductory econometrics for finance |
| title_full | Introductory econometrics for finance Chris Brooks |
| title_fullStr | Introductory econometrics for finance Chris Brooks |
| title_full_unstemmed | Introductory econometrics for finance Chris Brooks |
| title_short | Introductory econometrics for finance |
| title_sort | introductory econometrics for finance |
| topic | Finanzmarkt stw Multivariate Analyse stw Regression stw Simulation stw Theorie stw Zeitreihenanalyse stw Ökonometrie stw Ökonometrisches Modell stw Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik (DE-588)4017195-4 gnd Statistik (DE-588)4056995-0 gnd Kreditmarkt (DE-588)4073788-3 gnd Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | Finanzmarkt Multivariate Analyse Regression Simulation Theorie Zeitreihenanalyse Ökonometrie Ökonometrisches Modell Econometrics Finance Econometric models Finanzmathematik Statistik Kreditmarkt Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016295424&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT brookschris introductoryeconometricsforfinance |