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| Àâòîðèçàöèÿ |
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| Ïîèñê ïî óêàçàòåëÿì |
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| Salvatore D., Reagle D. — Statistics and econometrics |
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| Ïðåäìåòíûé óêàçàòåëü |
 see “Determination coefficient
2SLS (two-stage least squares) 230 237—238 241
A priori (classical) probability 37 42—44 51
A priori theoretical criteria 6
Absolute dispersion 29
Acceptance region in hypothesis testing 87—89 95—104
Acceptance region in multiple regression analysis 171—172
Adjusted  (R, adjusted coefficient of multiple determination) 157 170—171
Aikake’s information criteria (AIC) 244 248 253—254 261—262 265
Almon lag model 183 194—196 205
Alternative hypothesis, in hypothesis testing 87—89 95—96 99—101
Alternative hypothesis, in multiple regression analysis 171
Alternative hypothesis, in simple regression analysis 143
Analysis of variance see “ANOVA”
ANOVA (analysis of variance) tables 92—93 109—115
Arithmetic mean (average) 11
ARMA 242—245 249—254
Asymptotic unbiasedness 148—149
Augmeted Dickey — Fuller (ADF) 246—247 257
Autocorrelation (serial correlation), and errors in variables 217
Autocorrelation (serial correlation), as problem in regression analysis 208—209 215—220 242
Autoregression 242 249—251
Autoregression function (ACF) 244—245 251—253
Average (arithmetic mean) 11
Average deviation 13 24—25
Bayes’ Theorem 39 49—50
Behavioral (structural) equations 228 231—233
Best linear unbiased estimators see “BLUE”
Best unbiased (efficient) estimators 133—134 147—149
Bias 133—134 147—149 228 231
Biased estimates 147—149 183
Biased estimates, and errors in variables 221
Biased estimates, heteroscedasticity and 207
Biased estimates, Koyck lag model and 194
Bimodal distribution 20
Binary choice models 184—185 198—200
Binomial distribution, as discrete probability distribution 39—40 51—55 64—65
Binomial distribution, in estimation 70 79—81
Binomial distribution, in hypothesis testing 88—89 90—92 98—99 105—106
Binomial distribution, normal distribution and 60
Binomial distribution, Poisson distribution distinguished from 55—56
Binomial probabilities 300—305
BLUE (best linear unbiased estimators), in multiple regression analysis 162
BLUE (best linear unbiased estimators), in simple regression analysis 133—134 147—149
Box — Pierce statistic 244 253
Causality 248—249 260—262 264—265
Central tendency 19—24 34
Central-limit theorem 68 75 84
Chebyshev’s theorem (inequality) 42 62 66 71 83—84 86
Chi-square test, of goodness of fit and independence 90—92 104—109 120—122
Chi-square test, proportions of area of 311—312
Class boundaries (exact limits) 18
Class intervals, in descriptive statistics 9 11 16—18
Class intervals, in hypothesis testing 106—107
Classical (a priori) probability 37 42—44 51
cluster sampling 72
Co variance 16 129 145
Cobb — Douglas production function 187 210—211
Coding 22 27
Coefficients 5—7 (see also “Specific coefficients”)
Cointegration 247—248 258—260 264
Collection of data 2
Collinear independent variable 210
Column (sample) mean 92 109—114
Combinations 50
Conditional forecast 197
Conditional probability 38 47—48
Confidence intervals, and efficient estimator 147—148
Confidence intervals, autocorrelation and 208 216
Confidence intervals, for the mean using t distribution 70—71 81—84 86
Confidence intervals, in estimation 69—70 76—81 83—85
Confidence intervals, in forecast 183—184 197—198
Confidence intervals, in multiple regression analysis 165—169
Confidence intervals, in simple regression analysis 144
Confidence level, in estimation 69 76—80 83—85
Confidence level, in forecast 197—198
Confidence level, in hypothesis testing 87—88 95—99
Confidence limits 77
Consistency 148—149
Consistent estimators 134 148—149 186—187
Contingency-table tests 90—92
Continuity, correction for 92
Continuous distribution 41 57—62 105
Continuous probability distribution 41—42 57—62 65—66
Continuous random variables see “Probability distribution”
Continuous variables 41 51 57 61
Correlation, coefficient of, in simple regression analysis 132—133 132 144—147
Correlation, coefficient of, multicollinearity and 210—211
Correlation, coefficient of, partial, in multiple regression analysis 158—159 172—173 179
Correlation, coefficient of, rank 132—133 146—147
Correlation, coefficient of, simple, in multiple regression analysis 158—159
Correlogram 244
Counting techniques 39 50 64
Critical region see “Rejection region”
Cross-sectional analysis 135
Cross-sectional data 6 213
Cumulative frequency distribution 9 19
Cumulative normal function (probit model) 184 199
Data formats 266 271 292
Deciles 23—24
Degrees of freedom heteroscedasticity and 207 213
Degrees of freedom in distributed lag model 193
Degrees of freedom in dummy variable 189—190
Degrees of freedom in estimation 70—71 81—84
Degrees of freedom in forecast 183—184 197
Degrees of freedom in hypothesis testing 88 92—93 102—103 109—115
Degrees of freedom in multiple regression analysis 158 171—172
Degrees of freedom in simple regression analysis 131 143
delimiters 266 271
Demand function 5—7
Density function see “Probability distribution”
Dependent variable see also “Simultaneous-equations methods”
dependent variables 1 3—6 44 49
Dependent variables, and errors in variables 221—222
Dependent variables, autocorrelation and 216—217
Dependent variables, endogenous variable as 228—229
Dependent variables, in distribution lag model 193
Dependent variables, in forecasting 197 (see also “Forecasting”)
Dependent variables, in multiple regression analysis 154 (see also “Multiple regression analysis”)
Dependent variables, in simple regression analysis 128 134—136
Dependent variables, multiplication for 38 44—49
Dependent variables, qualitative 184 198—199
Descriptive statistics 1—3 9—35
Descriptive statistics, frequency distributions in 9—10 16—19 33
Descriptive statistics, in multiple regression analysis 157 169—171 179
Descriptive statistics, in simple regression analysis 132—133 144—145
Descriptive statistics, measures of central tendency in 11—12 19—24 34
Descriptive statistics, measures of dispersion in 13—15 24—29 35
Descriptive statistics, multicollinearity and 206 210
Determination, coefficient of (), and autocorrelation 218
Discrete distribution 17 39—40 51—57 64—65
Discrete random variables 39 51
Disjoint (mutually exclusive) events 37—38 44—46 63
dispersion 13—15 24—29 35
Distributed lag models 182—183 193—196 204—205
Distribution curve (ogive) 9 17 19
Distribution, central tendency of 11
Distribution, central tendency of, in simple regression analysis 143 (see also “Specific distributions”)
Disturbance see “Error term”
Double-lag form (model) 181—182 186—187 202
Double-lag linear model (form) 181—182 186—189
Dummy variables 182 189—193 203—204
Durbin two-stage method 217
Durbin-Watson statistic 208 216—217 318
Econometric criteria 6—7
Econometrics examination 294—299
Econometrics, methodology of 1 2 5—8
Econometrics, statistics and 1—5 7—8
Economic theory 1 4
Efficient (best unbiased) estimators 147—149 183
| Empirical probability see “Relative frequency distribution”
Empirical sampling distribution of the mean 74
Endogenous variables 228—229
Error correction 247—248 258—260 264
Error sum of squares (ESS) 110—115
Error sum of squares (ESS), heteroscedasticity and 207—208 213—214
Error sum of squares (ESS), in simple regression analysis 132 144
Error term (stochastic term, disturbance) 1 3—6
Error term (stochastic term, disturbance), and errors in variables 209
Error term (stochastic term, disturbance), and qualitative dependent variable 199
Error term (stochastic term, disturbance), autocorrelation and 208—209 215—220
Error term (stochastic term, disturbance), forecasting errors and 197
Error term (stochastic term, disturbance), in distributed lag model 193—194
Error term (stochastic term, disturbance), in multiple regression analysis 165
Error term (stochastic term, disturbance), in recursive models 232—233
Error term (stochastic term, disturbance), in simple regression analysis 128 134—136 137—138
Error term, variance of, and heteroscedasticity 207 212
Errors in variables 209—210 221—222 226—227
ESS see “Error sum of squares”
Estimate(s), defined 76
Estimate(s), error of the 79 130—131 155
Estimate(s), in descriptive statistics 25 27
Estimate(s), in simple regression analysis 128—130 (see also “Specific types of estimates and estimators”)
Estimated demand function 6
Estimated parameters, functional form and 186—187
Estimated parameters, in multiple regression analysis 172—173
Estimation 1 2 67—86
Estimation, confidence intervals for the mean, using t distribution 70—71 81—84 86
Estimation, indirect least squares 229—230 235—237 240—241
Estimation, sampling 67 71—72 84
Estimation, sampling distribution of the mean 67—69 72—76 84
Estimation, two-stage least squares in 230 237—238 241
Estimation, using normal distribution 69—70 76—81 85
Estimator(s), defined 76
Estimator(s), in multiple regression analysis 154
Estimator(s), in simple regression analysis 140—141 (see also “Specific types of estimates and estimators”)
Eviews 268—269 277—282 292
Exact limits (class boundaries) 18
Exact linear relationship 128 172—173
Exactly identified equations 229—230 233—235
Exogenous variables 228—230
Expected frequencies 90—92 104—109
Expected value, in binomial distribution 40 51 54—55 64
Expected value, of continuous probability distribution 57
Expected value, of error term in simple regression analysis 128
Expected value, of Poisson distribution 55 65
Explained variation (regression sum of squares) 110—115 132 144 157
Explanatory variables see “Independent variables”
Exponential distribution 42 61—62
F distribution 110
F distribution, heteroscedasticity and 207—208
F distribution, in hypothesis testing 92—93 109—110
F distribution, in multiple regression analysis 158
F ratio 92—93 109—110 158 171—172
F, value of 313
Finite correlation factor 68 73
Finite population 67 73
First-order autocorrelation 208—209 215—220
Fitting a line 128—129 134—135
Fixed format 266 271
Forecast-error variance 183—184 197—198
Forecasting 4 6 7 183—184 197—198 205
Fourth moment 15
Frequency distributions 1 9—10 16—19 33 104—106
Frequency polygon 1 9 17—19
Functional form 181—182 186—189 202
Gauss — Markov theorem 133 148
Geometric mean 11 22—23
Goldfield — Quandt test for heteroscedasticity 213
Goodness of fit, chi-square test of independence and 90—92 104—109 120—121
Goodness of fit, in hypothesis testing 109
Goodness of fit, in simple regression analysis 132—133 144—147 153
Grand mean 92 109—115
Granger causality 248—249 260—262 264—265
Grouped data 11—14 19—29 51—52
Harmonic mean 11 23
Heteroscedasticity 207—208 212—215 223—225
High multicollinearity 210
histogram 1 9 16—19
Homoscedastic disturbances 212
Hypergeometric distribution 40 55
Hypothesis testing 71—72 87—127
Hypothesis testing, about population mean and proportion 87—89 96—101 119—120 “Simple
Hypothesis testing, analysis of variance in 92—93 109—115 122
Hypothesis testing, chi-square test of goodness of fit and independence in 90—92 104—109 120—122
Hypothesis testing, defined 1 2 71—72 87 95—96 119
Hypothesis testing, for differences between two means or proportions 89—90 101—104 120
Hypothesis testing, overall significance of regression in 171
Identification 229 233—235 239—240
ILS (indirect least squares) 229—230 235—237 240—241
Income elasticity 140—141 175—178 181—182 187
Inconsistent estimators see “Biased estimates”
Independent (explanatory) variables 1 4 6 38
Independent (explanatory) variables, and errors in variables 221—222
Independent (explanatory) variables, autocorrelation and 208 216—217
Independent (explanatory) variables, binomial and Poisson distributions and 55—56
Independent (explanatory) variables, exogenous variables as 228—229
Independent (explanatory) variables, heteroscedasticity and 207—208 212—215
Independent (explanatory) variables, hypothesis testing and 90—92 104—105
Independent (explanatory) variables, in distributed lag models 193
Independent (explanatory) variables, in forecasting 197 (see also “Forecasting”)
Independent (explanatory) variables, in simple regression analysis 128 134—136
Independent (explanatory) variables, lagged 209—210 221—222
Independent (explanatory) variables, multicollinearity and 206—207 210—212
Independent (explanatory) variables, multiple regression analysis 154 161
Independent (explanatory) variables, multiplication for 38 44—49
Independent (explanatory) variables, qualitative dependent variables and 184
Independent (explanatory) variables, qualitative, dummy variables as 182 189—193 203—204
Indirect least squares (ILS) 229—230 235—237 240—241
Inductive reasoning 2
Inferential statistics 1—3 (see also “Estimation” “Hypothesis
Infinite population 74 84
Instrumental variables 209—210 221—222
Interquartile range 13 24
Interval estimates 69—70 76—81
Inverse least squares 222
Joint moment 16
Joint probability 38
Kolmogorov — Smirnov test 94—95 118—119 123
Koyck lag model 183 193—194 204
Kruskal — Wallis test 94 117—118 123
kurtosis 15—16 31 35
Lack of bias 147—149
Lagged variables 209—210 221—222
Left-tail test 88 98 102
Leptokurtic curve 15 31
Likelihood function 199
Likelihood ratio index 185—186 200
Linear regression analysis 128 134
Linear relationship 154
Log-likelihood function 184 198—199
Logistic function (logit) 184—185
Logit model (logistic function) 184—185 199—200
Marginal effect 185 200 205
Mathematics 1 4 7
Matrix notation 159—160 173—175 179
Maximum likelihood 184 199
Mean absolute deviation (MAD) 13
Mean(s) 11 12
Mean(s), and analysis of variance 92
Mean(s), confidence interval for the, using t distribution 70—71 81—84 86
Mean(s), hypothesis testing for differences between two proportions or 89—90 101—104 120
Mean(s), in binomial distribution 39 54—55
Mean(s), in descriptive statistics 15—16 19—24
Mean(s), in normal distribution 41—42
Mean(s), in Poisson distribution 40 57—58
Mean(s), in simple regression analysis 141—142 (see also “Estimation Expected “Specific
Mean(s), of error term in simple regression analysis 128
Mean(s), of normal distribution as a continuous probability distribution 57—58
Mean(s), sampling distribution of the see “Sampling distribution of the mean”
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