A Guide to Econometrics / Edition 5

A Guide to Econometrics / Edition 5

by Peter Kennedy, Sidney Ed. Kennedy
     
 

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ISBN-10: 1405115017

ISBN-13: 9781405115018

Pub. Date: 06/28/2003

Publisher: Wiley

This is the perfect (and essential) supplement for all econometrics classes-from a rigorous first undergraduate course, to a first master's, to a PhD course. It explains what is going on in textbooks full of proofs and formulas. Kennedy's A Guide to Econometrics offers intuition, skepticism, insights, humor, and practical advice (do's and don'ts). The 6E contains new

Overview

This is the perfect (and essential) supplement for all econometrics classes-from a rigorous first undergraduate course, to a first master's, to a PhD course. It explains what is going on in textbooks full of proofs and formulas. Kennedy's A Guide to Econometrics offers intuition, skepticism, insights, humor, and practical advice (do's and don'ts). The 6E contains new chapters on instrumental variables and on computation considerations, more information on GMM and nonparametrics, and an introduction to wavelets.

About the Author:
Peter Kennedy is Professor of Economics at Simon Fraser University

Product Details

ISBN-13:
9781405115018
Publisher:
Wiley
Publication date:
06/28/2003
Edition description:
REV
Pages:
640
Product dimensions:
6.14(w) x 9.21(h) x 1.38(d)

Related Subjects

Table of Contents


Preface     x
Dedication     xii
Introduction     1
What is Econometrics?     1
The Disturbance Term     2
Estimates and Estimators     4
Good and Preferred Estimators     5
General Notes     6
Technical Notes     10
Criteria for Estimators     11
Introduction     11
Computational Cost     11
Least Squares     12
Highest R[superscript 2]     13
Unbiasedness     14
Efficiency     16
Mean Square Error     17
Asymptotic Properties     18
Maximum Likelihood     21
Monte Carlo Studies     22
Adding Up     25
General Notes     26
Technical Notes     32
The Classical Linear Regression Model     40
Textbooks as Catalogs     40
The Five Assumptions     41
The OLS Estimator in the CLR Model     43
General Notes     44
Technical Notes     47
Interval Estimation and Hypothesis Testing     51
Introduction     51
Testing a Single Hypothesis: the tTest     51
Testing a Joint Hypothesis: the F Test     52
Interval Estimation for a Parameter Vector     54
LR, W, and LM Statistics     56
Bootstrapping     58
General Notes     59
Technical Notes     67
Specification     71
Introduction     71
Three Methodologies     72
General Principles for Specification     75
Misspecification Tests/Diagnostics     76
R[superscript 2] Again     79
General Notes     81
Technical Notes     89
Violating Assumption One: Wrong Regressors, Nonlinearities, and Parameter Inconstancy     93
Introduction     93
Incorrect Set of Independent Variables     93
Nonlinearity     95
Changing Parameter Values     97
General Notes     100
Technical Notes     106
Violating Assumption Two: Nonzero Expected Disturbance     109
General Notes     111
Violating Assumption Three: Nonspherical Disturbances     112
Introduction     112
Consequences of Violation     113
Heteroskedasticity     115
Autocorrelated Disturbances     118
Generalized Method of Moments     122
General Notes     123
Technical Notes     129
Violating Assumption Four: Instrumental Variable Estimation     137
Introduction     137
The IV Estimator     141
IV Issues     144
General Notes     146
Technical Notes     151
Violating Assumption Four: Measurement Errors and Autoregression     157
Errors in Variables     157
Autoregression     160
General Notes     163
Technical Notes     167
Violating Assumption Four: Simultaneous Equations     171
Introduction     171
Identification     173
Single-Equation Methods     176
Systems Methods     180
General Notes     181
Technical Notes     186
Violating Assumption Five: Multicollinearity     192
Introduction     192
Consequences     193
Detecting Multicollinearity     194
What To Do     196
General Notes     198
Technical Notes     202
Incorporating Extraneous Information      203
Introduction     203
Exact Restrictions     203
Stochastic Restrictions     204
Pre-Test Estimators     204
Extraneous Information and MSE     206
General Notes     207
Technical Notes     211
The Bayesian Approach     213
Introduction     213
What is a Bayesian Analysis?     213
Advantages of the Bayesian Approach     216
Overcoming Practitioners' Complaints     217
General Notes     220
Technical Notes     226
Dummy Variables     232
Introduction     232
Interpretation     233
Adding Another Qualitative Variable     234
Interacting with Quantitative Variables     235
Observation-Specific Dummies     236
General Notes     237
Technical Notes     240
Qualitative Dependent Variables     241
Dichotomous Dependent Variables     241
Polychotomous Dependent Variables     244
Ordered Logit/Probit     245
Count Data     246
General Notes     246
Technical Notes     254
Limited Dependent Variables     262
Introduction     262
The Tobit Model     263
Sample Selection     265
Duration Models     267
General Notes     269
Technical Notes     273
Panel Data     281
Introduction     281
Allowing for Different Intercepts     282
Fixed Versus Random Effects     284
Short Run Versus Long Run     286
Long, Narrow Panels     287
General Notes     288
Technical Notes     292
Time Series Econometrics     296
Introduction     296
ARIMA Models     297
VARs     298
Error Correction Models     299
Testing for Unit Roots     301
Cointegration     302
General Notes     304
Technical Notes     314
Forecasting     331
Introduction     331
Causal Forecasting/Econometric Models     332
Time Series Analysis     333
Forecasting Accuracy     334
General Notes     335
Technical Notes     342
Robust Estimation      345
Introduction     345
Outliers and Influential Observations     346
Guarding Against Influential Observations     347
Artificial Neural Networks     349
Nonparametric Estimation     350
General Notes     352
Technical Notes     356
Applied Econometrics     361
Introduction     361
The Ten Commandments of Applied Econometrics     362
Getting the Wrong Sign     368
Common Mistakes     372
What do Practitioners Need to Know?     373
General Notes     374
Technical Notes     383
Computational Considerations     385
Introduction     385
Optimizing via a Computer Search     386
Estimating Integrals via Simulation     388
Drawing Observations from Awkward Distributions     390
General Notes     392
Technical Notes     397
Sampling Distributions, The Foundation of Statistics     403
All About Variance     407
A Primer on Asymptotics     412
Exercises     417
Answers to Even-Numbered Questions     479
Glossary      503
Bibliography     511
Name Index     563
Subject Index     573

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