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Maddala G.S. — Introduction to Econometrics

Maddala G.S. — Introduction to Econometrics

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Название: Introduction to Econometrics

Автор: Maddala G.S.

Аннотация:

Introduction to Econometrics has been significantly revised to include new developments in the field. The previous editions of this text were renowned for Maddala's clear exposition and the presentation of concepts in an easily accessible manner.
Features:
* New chapters have been included on panel data analysis, large sample inference and small sample inference
* Chapter 14 Unit Roots and Cointegration has been rewritten to reflect recent developments in the Dickey-Fuller (DF), the Augmented Dickey-Fuller (ADF) tests and the Johansen procedure
* A selection of data sets and the instructor's manual for the book can be found on our web site Comments on the previous edition: "Maddala is an outstanding econometrician who has a deep understaning of the use and potential abuse of econometrics... The strengths of the Maddala book are its simplicity, its accessibility and the large number of examples the book contains... The second edition is well written and the chapters are focused and easy to follow from beginning to end. Maddala has an oustanding grasp of the issues, and the level of mathematics and statistics is appropriate as well."

Язык:

Рубрика: Экономика и финансы/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Издание: second edition

Год издания: 1992

Количество страниц: 631

Добавлена в каталог: 09.03.2006

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

Предметный указатель

Adaptive expectations model      409—412
Adjusted $R^{2}((\bar{R})^{2})$       166—168
Akaike's information criterion (AIC)      500—501
Almon lag      424—427
Alternative functional forms      96—98
Amemiya's PC criterion      498
Analysis of variance      84 156—157
Anscombe's test for heteroskedasticity      204
ARCH model      264—265 478
ARIMA models      538—541
Asymptotic unbiasedness      25
Asymptotic variance      26
Autocorrelation Berenblut — Webb test      246
Autocorrelation caused by omitted variables      253
Autocorrelation Cochrane — Orcutt procedure      239
Autocorrelation Durbin — Watson test      230
Autocorrelation Durbin's alternative test      249
Autocorrelation Durbin's h-test      249
Autocorrelation effect on OLS estimators      241—244
Autocorrelation estimation in levels vs. first differences      232—233
Autocorrelation estimation with AR(1) errors      237
Autocorrelation extensions of DW tables      247
Autocorrelation function      528
Autocorrelation grid search methods      239
Autocorrelation in rational expectations models      443—444
Autocorrelation LM test      250
Autocorrelation Sargan's test      255
Autocorrelation von Neumann ratio      245
Autocorrelation with dummy variables      321—322
Autoregressive form of adaptive expectations model      411
Autoregressive processes      533—535
Bayes' theorem      15 503
Bayesian inference      21—22
Bayesian posterior odds      503—505
Berenblut — Webb test      246
Bivariate normal distribution      104—105
BLUE property of least squares estimators      114—115
BM test      222
Bounded influence estimation      488
Box — Cox test      221
Box — Jenkins methods      542—549
Breusch and Pagan test      207—209
Cagan's model, estimation      413—415
Causality      393—394
Causation in regression      75—76
Censored regression model      339—342
Chi-square distribution      20
Cointegrating regression      590—591
Cointegration      262 588 589
Cointegration and error correction models      597
Cointegration and vector autoregressions      592—597
Cointegration tests      598—600
Condition number (in multicollinearity)      275
Conditional probability      14
Confidence intervals defined      27—28 79—80
Confidence intervals joint and separate intervals      138—139
Confidence intervals relationship with testing of hypotheses      32—33
Consistency      24
Correlations relationships with t-ratios      145—147
Correlations simple, partial, and multiple      147—148
Correlogram      528
Cramer's rule      50
Cross-validation      504
Davidson and MacKinnon's J-test      515—516
Deletion of variables pitfalls in using t-ratios      168
Deletion of variables use of F-ratios      167
Determinants      44—47
Detrending      258—259
DFFITS criterion for outliers      487—488
Diagnostic checking      476
Dickey — Fuller test      259 583
Disproportionate sampling      330—331
Distributed lags      408—412
Dropping variables dummy dependent variables      322—338
Dropping variables for changes in slopes      313—315
Dropping variables for cross-equation constraints      315—317
Dropping variables for predictions and standard errors of prediction      319—320
Dropping variables for testing stability      318—319
Dropping variables in multicollinearity      289—291
Dropping variables linear discriminant function      325—326
Dropping variables linear probability model      323—325
Dropping variables logit model      327—329
Dropping variables measures of $R^{2}$       333—334
Dropping variables probit model      327—329
Dropping variables under heteroskedasticity and autocorrelation      321—322
Dropping variables use of F-ratios      167—168 500—503
DSP model      259
Dummy variables for changes in intercepts      307—312
Econometrics aims and methodology      4—7
Econometrics definition      1—2
Econometrics tests of economic theories      7
Econometrics types of models      2—4
Encompassing test      517
Endogenous variables      357
Error correction models      420 597
Errors in variables classical model      449—450
Errors in variables correlated errors      470
Errors in variables general model      451—458
Errors in variables grouping methods      463
Errors in variables Hausman's test for errors in variables      508
Errors in variables instrumental variable methods      461—462
Errors in variables Krasker — Pratt criterion      467
Errors in variables multiple equations      469
Errors in variables proxy variables      464—466
Errors in variables reverse regression      459—461
Estimation asymptotic variance      25
Estimation BLUE property of least squares      114— 115
Estimation consistency      24
Estimation efficiency      24
Estimation interval estimation      27—28
Estimation method of least squares      69—72 130—132
Estimation method of maximum likelihood (ML)      118
Estimation method of moments      66
Estimation point estimation      22—26
Estimation unbiasedness      23
Estimator properties asymptotic unbiasedness      25
Exogeneity defined      390—391
Exogeneity relationship with causality      394
Exogeneity test for exogeneity      395
Exogenous variables      357
Expectations adaptive expectations      408—410
Expectations model-consistent expectations      433
Expectations naive models      406—408
Expectations, expectations and adjustment lags      415
Expectations, rational expectations      431—433
Expectations, sufficient expectations      433
Extraneous estimators      293
F-distribution      21
F-ratios implied by different criteria for model selection      500—503
F-ratios relationship to higher      2 167—168
Generalized least squares      212—213 227 238
Glejser's test      204
Goldfeld — Quandt test      206
Goodness of fit      131 332—334 369 540—541 550—552
Granger casuality      393
Granger's test      393
Grid-search method      239
Grouping methods      463
Hausman's test      506—509
Hendry's approach to model construction      494—495
Heteroskedasticity Anscombe's test      204
Heteroskedasticity Breusch and Pagan test      207—209
Heteroskedasticity consequences      209—211
Heteroskedasticity deflators      215—217
Heteroskedasticity estimation of correct variance      211
Heteroskedasticity Glejser's test      204
Heteroskedasticity Goldfeld and Quandt test      206
Heteroskedasticity likelihood ratio test      206
Heteroskedasticity maximum likelihood method      213
Heteroskedasticity Ramsey's RESET test      204

Heteroskedasticity weighted least squares      212—213
Heteroskedasticity White's test      204
Hocking's $S_{p}$ criterion      498—499
Hypothesis testing general theory      27—32
Hypothesis testing relationship with confidence intervals      32—33
Idempotent matrices      55
Identification necessary and sufficient conditions      363—364
Identification through reduced form      358—360
Identification Working's concept.      385
Inference classical      22—26
Inference multiple regression      134—135
Inference simple regression      76—78
Instrumental variables      112 367—369 461—462
Inverse of a matrix      48
Inverse prediction      101—102
Irrelevant variables      164—165
J-test      515—516
Joint and separate confidence intervals      139
Joint, marginal, and conditional distributions      18
Koyck model estimation in autoregressive form      411
Koyck model estimation in distribution lag form      412
Lagrangian multiplier (LM) test      119—124 177 251
Levels vs. first differences      232—234 259—263
Likelihood ratio (LR) test      119—124 206
Limited information maximum likelihood (LIML)      381
Limiting distribution      25
Linear and quadratic forms      52
Linear probability (LP) model      323—325
Linear vs. log-linear forms      96—97 220—223
Liu critique      389
Logit model      327—329
Lucas critique      389
Mallows' $C_{p}$ criterion      498
Matrix addition      42
Matrix multiplication      43
Methods of estimation generalized least squares      212—213 227 238
Methods of estimation indirect least squares      359—360
Methods of estimation instrumental variables      112 367—369 461—462
Methods of estimation least squares      69—72 130—132
Methods of estimation limited information maximum likelihood (LIML)      381
Methods of estimation maximum likelihood      118
Methods of estimation moments      66
Model selection      492—496
Moving average process      531
Multicollinearity condition number      275
Multicollinearity definition      270
Multicollinearity dropping variables      289—291
Multicollinearity extraneous estimates      293
Multicollinearity principal component regression      284—285
Multicollinearity problems in measuring      276
Multicollinearity ridge regression      281—283
Multicollinearity Theil's measure      275
Multiple regression analysis of variance      157
Multiple regression degrees of freedom and R2      165—168
Multiple regression inclusion of irrelevant variables      164—165
Multiple regression interpretation      143—144
Multiple regression omission of relevant variables      161—162
Multiple regression partial correlation and multiple correlation      147—148
Multiple regression prediction      154—155
Multiple regression statistical inference      134—135
Multiple regression test for linear functions of parameters      159
Multiple regression tests for stability      170—177
Multiple regression tests of hypotheses      156—159
Multivariate normal distribution      54
Nonnested hypothesis tests      514—518
Normal distribution      19
Normalization in simultaneous equations      377
Omitted variables autocorrelation      253
Omitted variables bias in least squares estimators      161—163
Omitted variables DFFITS criterion      487—488
Omitted variables illustrated      89—95
Omitted variables interpretation of Hausman's test      510— 512
Omitted variables test for      477
Orthognal matrices      49
Outliers bounded influence estimation      488
Partial adjustment models      419—423
PE test      223
Piecewise regression      185
Polynomial lags      423—427
Positive and negative definite matrices      53
Posterior odds for model selection      503-505
Principal component regression      284—285
probability      12—14
Probability distributions      17—21
Probit model      327—329
proxy variables      464—466
PSW test      513—514
Q-statistics      540—541
Qualitative variables      see “Dummy variables”
R-square in dummy dependent variable models      332—334
R-square in multiple regression models      131
R-square in simultaneous equation models      369
R-square in time-series models      550
R-square relationship with F-ratios      167—168
Ramsey's RESET test      478
Rao's score test      119—124
Rational expectations defined      431—433
Rational expectations estimation of a demand and supply model      436—442
Rational expectations serial correlation problem      443
Rational expectations tests for rationality      434—435 599
Rational lags      429
Recursive systems      387
Reduced form      358—360
Regression analysis of variance      84 156—157
Regression assumptions and specification      65
Regression causation in regression      75—76
Regression censored, regression model      339—342
Regression cointegrating regression      590—591
Regression fallacy      105
Regression interpretation      143—145
Regression inverse prediction      101—102
Regression irrelevant variables      164—165
Regression method of least squares      69—71
Regression method of moments      66
Regression outliers      89—95
Regression prediction      85 154—155 319—320
Regression statistical inference      76—78 134—135
Regression tests of hypotheses      80
Regression truncated regression model      342—343
Regression with no constant term      83
Residuals BLUS residuals      483
Residuals predicted residuals      481
Residuals problems with least squares residuals      479—480
Residuals recursive residuals      483
Residuals studentized residuals      482
Reverse regression      71—72 459—461
Ridge regression      281—283
Sampling distributions from normal populations      26
Sampling distributions multiple regression      134—135
Sampling distributions simple regression      76—78
Sargan's test      255
Selection of regressors      496—502
Serial correlation in autoregressive models      248
Serial correlation in rational expectations models      443—444
Serial correlation in unit root tests      583
Serial correlation LM test      250
Significance levels criticism      32
Significance levels defined      29
Sims' test      394
Specification errors Hausman's test      506—509
Specification errors irrelevant variables      164—165
Specification errors omitted variables      161—162
Specification errors Ramsey's test      478
Specification errors Sargan's test in dynamic models      255
Specification searches      491
Spurious trends      260—261
Stationary time-series      527—530
Stochastic regressors      126
Structural change and unit roots      587
t-distribution      21

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