Econometrics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Princeton ; Oxford
Princeton University Press
[2000]
|
| Schlagworte: | |
| Online-Zugang: | Publisher description Inhaltsverzeichnis |
| Beschreibung: | xxiii, 683 Seiten Diagramme |
| ISBN: | 0691010188 9780691010182 |
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Datensatz im Suchindex
| _version_ | 1804128761137856512 |
|---|---|
| adam_text | Contents
List of Figures
Preface
1
1.1
The Linearity Assumption
Matrix Notation
The Strict Exogeneity Assumption
Implications of Strict Exogeneity
Strict Exogeneity in Time-Series Models
Other Assumptions of the Model
The Classical Regression Model for Random Samples
Fixed Regressors
1.2
OLS Minimizes the Sum of Squared Residuals
Normal Equations
Two Expressions for the OLS Estimator
More Concepts and Algebra
Influential Analysis (optional)
A Note on the Computation of OLS Estimates
1.3
Finite-Sample Distribution of
Finite-Sample Properties of s1
Estimate of Var(b | X)
1.4
Normally Distributed Error Terms
Testing Hypotheses about Individual Regression Coefficients
Decision Rule for the
Confidence Interval
vj
p-Value 38
Linear
The
A More Convenient Expression for
t
An Example of a Test Statistic Whose Distribution Depends on X
1.5
The Maximum Likelihood Principle
Conditional versus Unconditional Likelihood
The Log Likelihood for the Regression Model
ML via Concentrated Likelihood
Cramer-Rao Bound for the Classical Regression Model
The
Quasi-Maximum Likelihood
1.6
Consequence of Relaxing Assumption
Efficient Estimation with Known V
A Special Case: Weighted Least Squares (WLS)
Limiting Nature of GLS
1.7
The Electricity Supply Industry
The Data
Why Do We Need Econometrics?
The Cobb-Douglas Technology
How Do We Know Things Are Cobb-Douglas?
Are the OLS Assumptions Satisfied?
Restricted Least Squares
Testing the Homogeneity of the Cost Function
Detour: A Cautionary Note on R2
Testing Constant Returns to Scale
Importance of Plotting Residuals
Subsequent Developments
Problem Set
Answers to Selected Questions
2
2.1
Various Modes of Convergence
Three Useful Results
Contents
Viewing Estimators as Sequences of Random Variables
Laws of Large Numbers and Central Limit Theorems
2.2
Need for Ergodic Stationarity
Various Classes of Stochastic Processes
Different Formulation of Lack of Serial Dependence
The CLT for Ergodic Stationary Martingale Differences Sequences
2.3
The Model
Asymptotic Distribution of the OLS Estimator
s2 Is Consistent
2.4
Testing Linear Hypotheses
The Test Is Consistent
Asymptotic Power
Testing Nonlinear Hypotheses
2.5
Using Residuals for the Errors
Data Matrix Representation of
Finite-Sample Considerations
2.6
Conditional versus Unconditional Homoskedasticity
Reduction to Finite-Sample Formulas
Large-Sample Distribution of/ and
Variations of Asymptotic Tests under Conditional
Homoskedasticity
2.7
2.8
(optional)
The Functional Form
WLS with Known a
Regression of ej on
WLS with Estimated a
OLS versus WLS
2.9
Optimally Predicting the Value of the Dependent Variable
Best Linear Predictor
OLS Consistently Estimates the Projection Coefficients
Viii Contents
2.10
Box-Pierce and Ljung-Box
Sample Autocorrelations Calculated from Residuals
Testing with Predetermined, but Not Strictly Exogenous,
Regressors
An Auxiliary Regression-Based Test
2.11
The Efficient Market Hypotheses
Testable Implications
Testing for Serial Correlation
Is the Nominal Interest Rate the Optimal Predictor?
Rt Is Not Strictly Exogenous
Subsequent Developments
2.12
The Asymptotic Distribution of the OLS Estimator
Hypothesis Testing for Time Regressions
Appendix
Appendix 2.B
Problem Set
Answers to Selected Questions
3
3.1
A Simultaneous Equations Model of Market Equilibrium
Endogeneity Bias
Observable Supply Shifters
3.2
A Simple Macroeconometric Model
Errors-in-Variables
Production Function
3.3
Regressors and Instruments
Identification
Order Condition for Identification
The Assumption for Asymptotic Normality
3.4
Method of Moments
Generalized Method of Moments
Sampling Error
Contents
3.5
Asymptotic Distribution of the GMM Estimator
Estimation of Error Variance
Hypothesis Testing
Estimation of
Efficient GMM Estimator
Asymptotic Power
Small-Sample Properties
3.6
Testing Subsets of Orthogonality Conditions
3.7
The LR Statistic for the Regression Model
Variable Addition Test (optional)
3.8
Efficient GMM Becomes 2SLS
J
Small-Sample Properties of 2SLS
Alternative Derivations of 2SLS
When Regressors Are Predetermined
Testing a Subset of Orthogonality Conditions
Testing Conditional Homoskedasticity
Testing for Serial Correlation
3.9
The NLS-Y Data
The Semi-Log Wage Equation
Omitted Variable Bias
IQ as the Measure of Ability
Errors-in-Variables
2SLS to Correct for the Bias
Subsequent Developments
Problem Set
Answers to Selected Questions
4
4.1
Linearity
Stationarity and Ergodicity
Orthogonality Conditions
Identification
Contents
The Assumption for Asymptotic Normality
Connection to the Complete System of Simultaneous Equations
4.2
4.3
4.4
When Are They Equivalent ?
Joint Estimation Can Be Hazardous
4.5
Conditional Homoskedasticity
Full-Information Instrumental Variables Efficient (FIVE)
Three-Stage Least Squares (3SLS)
Seemingly Unrelated Regressions
SUR
4.6
The Model with Common Coefficients
The GMM Estimator
Imposing Conditional Homoskedasticity
Pooled OLS
Beautifying the Formulas
The Restriction That Isn t
4.7
The
Factor Shares
Substitution Elasticities
Properties of Cost Functions
Stochastic Specifications
The Nature of Restrictions
Multivariate Regression Subject to Cross-Equation Restrictions
Which Equation to Delete?
Results
Problem Set
Answers to Selected Questions
Panel Data
5.1
Error Components
Group Means
A Reparameterization
5.2
Contents xi
The Formula
330
Large-Sample Properties
331
Digression: When j/; Is Spherical
333
Random Effects versus Fixed Effects
334
Relaxing Conditional Homoskedasticity
335
5.3
337
Zeroing Out Missing Observations
338
Zeroing Out versus Compression
339
No Selectivity Bias
340
5.4
342
Derivation of the Estimation Equation
342
Appending the Error Term
343
Treatment of a,
344
Consistent Estimation of Speed of Convergence
345
Appendix
346
Problem Set
349
Answers to Selected Questions
363
Serial Correlation
365
6.1
365
MAG?)
366
MA(oo) as a Mean Square Limit
366
Filters
369
Inverting Lag Polynomials
372
6.2
375
AR(1) and Its MA(oo) Representation
376
Autocovariances of AR(
378
ARO)
378
ARMA(p, q)
380
ARMA(p, q) with Common Roots
382
Invertibility
383
Autocovariance-Generating Function and the Spectrum
383
6.3
387
6.4
392
Estimation of AR(
392
Estimation of AR(p)
393
Choice of Lag Length
394
Estimation of VARs
397
Estimation of ARMAC/?, q)
398
x¡j
6.5
LLN for Co variance-Stationary Processes
Two Central Limit Theorems
Multivariate Extension
6.6
The Model and Asymptotic Results
Estimating
Using Kernels to Estimate
VARHAC
6.7
Kernel-Based Estimation of
Data Matrix Representation of Estimated Long-Run Variance
Relation to GLS
6.8
The Market Efficiency Hypothesis
Testing Whether the Unconditional Mean Is Zero
Regression Tests
Problem Set
Answers to Selected Questions
7
7.1
Measurabilit/ ofo
Two Classes of
Maximum Likelihood (ML)
Conditional Maximum Likelihood
Invariance
Nonlinear Least Squares (NLS)
Linear and Nonlinear GMM
7.2
Two Consistency Theorems for
Consistency of M-Estimators
Concavity after Reparameterization
Identification in NLS and ML
Consistency of GMM
7.3
Asymptotic Normality of M-Estimators
Consistent Asymptotic Variance Estimation
Asymptotic Normality of Conditional ML
Contents
Two Examples
Asymptotic Normality of GMM
GMM versus ML
Expressing the Sampling Error in a Common Format
7.4
The Null Hypothesis
The Working Assumptions
The
The
The Likelihood Ratio (LR) Statistic
Summary of the Trinity
7.5
Newton-Raphson
Gauss-Newton
Writing Newton-Raphson and Gauss-Newton in a Common
Format
Equations Nonlinear in Parameters Only
Problem Set
Answers to Selected Questions
8
8.1
Score and Hessian for Observation
Consistency
Asymptotic Normality
8.2
The Model
Truncated Distributions
The Likelihood Function
Reparameterizing the Likelihood Function
Verifying Consistency and Asymptotic Normality
Recovering Original Parameters
8.3
Tobit Likelihood Function
Reparameterization
8.4
The Multivariate Regression Model Restated
The Likelihood Function
Maximizing the Likelihood Function
xiv Contents
Consistency and Asymptotic Normality
8.5
The Multiple-Equation Model with Common Instruments Restated
The Complete System of Simultaneous Equations
Relationship between
The FIML Likelihood Function
The FIML Concentrated Likelihood Function
Testing Overidentifying Restrictions
Properties of the FIML Estimator
ML Estimation of the
8.6
LIML Defined
Computation of LIML
LIML versus 2SLS
8.7
Two Questions
Unconditional ML for Dependent Observations
ML Estimation of AR(
Conditional ML Estimation of AR(1) Processes
Conditional ML Estimation of AR(p) and VAR(p) Processes
Problem Set
9
9.1
Integrated Processes
Why Is It Important to Know if the Process Is
Which Should Be Taken as the Null,
Other Approaches to Modeling Trends
9.2
Linear
Approximating I(
Relation to
The Wiener Process
A Useful Lemma
9.3
The AR(1) Model
Deriving the Limiting Distribution under the I(
Incorporating the Intercept
Incorporating Time Trend
Contents
9.4
The Augmented
Limiting Distribution of the OLS Estimator
Deriving Test Statistics
Testing Hypotheses about
What to Do When
A Suggestion for the Choice of pmax(J)
Including the Intercept in the Regression
Incorporating Time Trend
Summary of the DF and ADF Tests and Other Unit-Root Tests
9.5
Local-to-Unity Asymptotics
Small-Sample Properties
9.6
The Embarrassing Resiliency of the Random Walk Model?
Problem Set
Answers to Selected Questions
10
10.1
Linear Vector
The Beveridge-Nelson Decomposition
Cointegration
10.2
Phillips s Triangular Representation
VAR
The Vector Error-Correction Model (VECM)
Johansen s ML Procedure
10.3
Spurious Regressions
The Residual-Based Test for
Testing the Null of
10.4
The SOLS Estimator
The Bivariate Example
Continuing with the Bivariate Example
Allowing for Serial Correlation
General Case
Other Estimators and Finite-Sample Properties
xvi Contents
10.5 Application:
The Data
(m
DOLS
Unstable Money Demand?
Problem Set
Appendix A: Partitioned Matrices and
Addition and Multiplication of Partitioned Matrices
Inverting Partitioned Matrices
Index
|
| any_adam_object | 1 |
| author | Hayashi, Fumio 1952- |
| author_GND | (DE-588)130323136 |
| author_facet | Hayashi, Fumio 1952- |
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| callnumber-first | H - Social Science |
| callnumber-label | HB139 |
| callnumber-raw | HB139.H39 2000 |
| callnumber-search | HB139.H39 2000 |
| callnumber-sort | HB 3139 H39 42000 |
| callnumber-subject | HB - Economic Theory and Demography |
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| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330/.01/5195 21 330.015195 |
| dewey-search | 330/.01/5195 21 330.015195 |
| dewey-sort | 3330 11 45195 221 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| genre_facet | Lehrbuch Lehrbuch - Ökonometrie |
| id | DE-604.BV013918177 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T18:54:22Z |
| institution | BVB |
| isbn | 0691010188 9780691010182 |
| language | English |
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| oclc_num | 247253903 |
| open_access_boolean | |
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| physical | xxiii, 683 Seiten Diagramme |
| publishDate | 2000 |
| publishDateSearch | 2000 |
| publishDateSort | 2000 |
| publisher | Princeton University Press |
| record_format | marc |
| spelling | Hayashi, Fumio 1952- Verfasser (DE-588)130323136 aut Econometrics Fumio Hayashi Princeton ; Oxford Princeton University Press [2000] © 2000 xxiii, 683 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier USA Ökonometrie / Theorie Econometrics Ökonometrie (DE-588)4132280-0 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Lehrbuch - Ökonometrie Ökonometrie (DE-588)4132280-0 s b DE-604 http://www.loc.gov/catdir/description/prin022/00034665.html Publisher description Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009522797&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hayashi, Fumio 1952- Econometrics USA Ökonometrie / Theorie Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4123623-3 |
| title | Econometrics |
| title_auth | Econometrics |
| title_exact_search | Econometrics |
| title_full | Econometrics Fumio Hayashi |
| title_fullStr | Econometrics Fumio Hayashi |
| title_full_unstemmed | Econometrics Fumio Hayashi |
| title_short | Econometrics |
| title_sort | econometrics |
| topic | USA Ökonometrie / Theorie Econometrics Ökonometrie (DE-588)4132280-0 gnd |
| topic_facet | USA Ökonometrie / Theorie Econometrics Ökonometrie Lehrbuch Lehrbuch - Ökonometrie |
| url | http://www.loc.gov/catdir/description/prin022/00034665.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=009522797&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hayashifumio econometrics |