Subject Index 1001

Time sequence plot, 462-463 Time series detrended, 821 examples, 793-796 inertia, 443-445 measuring volatility, 856-862 stationary, 448, 792 Time Series Analysis: Forecasting and Control (Box & Jenkins), 837

Time series data, 25-26, 441, 636, 664 ARIMA process, 839-840 ARMA process, 839 autoregressive modeling,

838-839 challenge to econometricians,

792-793 combined with cross-sectional data, 364-365 moving average processes, 839 Time series econometrics, 26, 367, 792-830 cointegration, 822-286 difference stationary stochastic processes, 802-804 economic applications, 826-829 for forecasting, 835-865 integrated stochastic processes,

804-806 key concepts, 796 spurious regression, 806-807 stochastic processes, 796-801 tests of stationarity, 807-813 trend stationary processes,

802-804 unit root stochastic processes, 801-802 unit root tests, 814-820 Time-series regression, 291-292 Time-to-event data analysis, 624 Time variant, 448 Tobit model, 561, 616-619 illustration of, 618-619 Tolerance, 353, 362-363 Total cost function, 227-228 Total sum of squares, 83 and analysis of variance, 140-142 Traditional econometric methodology, 3-12 Transcendental production function, 288 Transposition, 914 t ratio, 354 Treasury bills, 828

Trend, 26

season or cyclical, 312n Trend-stationary processes, 820-821 Trend-stationary stochastic processes, 802-804 Trend variable, 180 Triangular distributed lag model,

705-706 Triangular models, 764 Trichotomous response variables, 581 Truncated sample, 616n t-test, 133, 252-253

in restricted least squares, 267 t values, 129

Two-sided hypothesis, 127 Two-sided/two-tail test, 127-128,

131-132 Two-stage least squares, 753, 770-778 features of, 773-774 numerical example, 775 standard error, 791 Two-stage least squares, 749 2-t rule of thumb, 134-135 Two-variable regression model, 24-25, 37-52 estimation problem classical linear regression model, 65-76 Gauss-Markov theorem, 79-81

ordinary least squares method, 58-65

precision of standard errors,

76-79 extensions functional forms, 175-191 regression through the origin,

164-169 scaling, 169-173 standardized variables,

173-175 units of measurement, 169-173 hypothesis testing confidence interval approach,

127-128 practical aspects, 134-139 statistical prerequisites, 119 terminology, 126-127 test of significance approach, 129-133 hypothetical examples, 37-41 illustrated example, 51

interval estimation basic ideas, 120-121 confidence interval, 121-126 statistical prerequisites, 119 linearity in, 42-43 maximum likelihood estimation of, 114-117 ordinary least squares method,

58-65 population regression function, 41 sample regression function, 47-51

significance of stochastic disturbance, 45-47 stochastic specification of PRF, 43-45

Type I error, 120n, 127n,

136-137, 908 Type II error, 120n, 136-137, 908

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