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Subject Index
Distributed-lag multiplier, 619
Disturbance term, 4
Disturbances:
assumption of no autocorrelation
between, 66–67
heteroscedastic variances of, 544–545
non-normality of, 544
probability distribution of, 97–98
Dividends, 738, 739
“Doing nothing,” 342
Double summation operator (
∑∑
), 801
Double-log model, 159
Downward trend, 164
DPI (
see
Disposable personal income)
Drift parameter, 743
DS stochastic processes (
see
Difference
stationary stochastic processes)
DSP (
see
Difference stationary process)
Dummy variables:
in ANCOVA models, 283–285
in ANOVA models, 278–283
and autocorrelation, 299, 449
Chow-test alternative, 285–288
defined, 278
as dependent variables, 299
example of, 300–304
guidelines for using, 281–282
and heteroscedasticity, 298–299
interaction effects using, 288–290
nature of, 277–278
in panel data models, 297
in piecewise linear regression, 295–297
for seasonal analysis, 290–295
semilogarithmic regressions,
297–298, 314
topics for study, 300
Dummy variables method, 291, 293n,
297–299
Dummy-variable trap, 281, 597
Duration models, 580–581
Durbin
h
test, 637–639
Durbin’s
h
statistic, 465
Durbin’s
M
test, 440
Durbin’s two-step method, 456–457
Durbin–Watson
d
statistic, 434, 477–479
and ARCH effect, 796
p
based on, 445
table of, 888–891
Durbin–Watson
d
test, 434–438
Dynamic regression models, 418, 617
E
ECM (
see
Error correction mechanism)
Econometric model(s):
applications of, 9
of consumption, 4–5
estimation of, 5, 7
example of, 4
Klein’s, 679
selection of, 9, 10
Econometric modeling, 467–513
Chow’s prediction failure test in,
498–499
examples of, 500–509
guidelines for, 511
measurement errors, 482–486
in dependent variable
Y,
482–483
example, 485–486
in explanatory variable
X,
483–485
missing data in, 499–500
model selection criteria, 468, 493–496
adjusted
R
2
, 493
Akaike’s information criterion, 494
caution about criteria, 495–496
forecast chi-square, 496
Mallows’s
C
p
criterion, 494–495
R
2
criterion, 493
Schwarz’s information criterion, 494
nested vs. non-nested models, 487
non-normal error distribution in, 509–510
outliers/leverage/influence in, 496–498
recursive least squares in, 498
specification errors
consequences of, 470–474
tests of, 474–482
types of, 468–470
stochastic error term specification,
486–487
stochastic explanatory variables in,
510–511
tests of non-nested hypotheses, 488–492
Davidson–MacKinnon
J
test,
490–492
discerning approach, 488–492
discrimination approach, 488
non-nested
F
test, 488–489
tests of specification errors, 474–482
and unbiasedness property, 520–521
Econometrics:
computer’s role in, 11–12
definitions, 1
as empirical verification of economic
theory, 2
mathematical prerequisites, 11
methodology of, 2–10
data gathering, 5–7
econometric model specification, 4–5
forecasting, 8
hypothesis statement, 3
hypothesis testing, 7–8
mathematical model specification, 3–4
model applications, 9
model estimation, 5, 7
reading resources about, 12
statistical prerequisites, 11
types of, 10–11
Economic forecasting, 773–775
Economic Statistics Briefing Room, 900
Economic theory, 2
Economics, causality in, 652–658
Efficient capital market hypothesis, 742
Efficient estimators, 72, 100, 827
EG test (
see
Engle–Granger test)
EGARCH (exponential GARCH), 799
EGLS (estimated generalized least
squares), 868
Eigenvalues, 339–340
Elasticity measurement, 159–162
Elasticity of demand, 17
Encompassing
F
test, 488–489
Encompassing model, 468
Encompassing principle, 490
Endogenous variables, 657, 673
Endpoint restrictions, 652
Energy Information Administration, 901
Engel expenditure models, 165
Engle–Granger (EG) test, 763–764
Equal matrices, 840
Equality testing, of two regression
coefficients, 246–248
Equation error term, 483
Error components model (
see
Random
effects model)
Error correction mechanism (ECM),
764–765
Error sum of squares, 528n
Error term, 4, 62–63
Error-learning models, 366
Errors of measurement, 27, 482–486
Errors of measurement bias, 469
ESS (
see
Explained sum of squares)
Estimable function, 325n, 649
Estimate, 44, 823
Estimated generalized least squares
(EGLS), 447, 868
Estimated value, 5n
Estimation, 823–831
of ARIMA model, 782
in classical theory of statistical
inference, 97
of econometric model, 5, 7
interval estimation, 824–825
large-sample properties, 828–831
maximum likelihood method, 102–106
methods, 825–826
point estimation, 823–824
problem of, 823
simultaneous-equation methods, 711–712
bias in indirect least-squares
estimators, 735
examples, 724–729
indirect least squares, 715–718
recursive models and OLS, 712–714
standard errors of 2SLS
estimators, 736
two-stage least squares, 718–724
small-sample properties, 826–828
in VAR model, 785–786
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