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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 |
Mean-square error (MSE), in hypothesis testing 92 110—113
Mean-square error (MSE), in simple regression analysis 134 148—149
Measurement errors 221—222
Median 11 12 15 19—24
Mesokurtic curve 15 57
Microsoft Excel 267—268 272—276 292
MODE 11 12 15 19—24
Moving Average 242—244 249—251
MSE see “Mean-square error”
Multicollinearity 206—207 210—212 222—223
Multiple events 37—39 44—50 63—64
multiple regression analysis 4 134 154—180
Multiple regression analysis, coefficient of multiple determination in 157 169—171 179
Multiple regression analysis, forecasting in 183—184
Multiple regression analysis, partial-correlation coefficient in 158—159 172—173 179
Multiple regression analysis, test of overall significance of the regression in 158 171—172 179
Multiple regression analysis, tests of significance of parameter estimates in 155 165—169 179
Multiple regression analysis, three-variable linear model as 154—155 161—165 178
Multiplication, for dependent events 38 45—50
Multiplication, for independent events 38 45 46 49
Mutually exclusive (disjoint) events 37—38 44—46 63
negative correlation 132—133 144—145
Negative linear relationship 172—173
Negatively skewed distribution 15 29—30
Nonlinear estimators 147—148
Nonlinear functions 181
Nonlinear regression analysis 134
Nonoccurrence probability 36
Nonparametric testing 94—95 115—119 122—123
Normal distribution, as continuous probability distribution 41—42 57—62 65—66
Normal distribution, in estimation 69—70 85
Normal distribution, in hypothesis testing 88 90 92 94—95 96—99 106—107
Normal distribution, in simple regression analysis 131 143
Normal distribution, or error term in simple regression analysis 128
Normal distribution, standard 41—42 307
Normal equations 128—129
Null hypothesis, in hypothesis testing 87—89 90 93—94 98 108 110 113—115
Null hypothesis, in multiple regression analysis 171—172
Null hypothesis, in simple regression analysis 143
Observed frequencies 90—92 104—109
OC (operating-characteristic) curve 89 100—101 120
Ogive (distribution curve) 9 17—19
OLS see “Ordinary least-squares method”
One-factor (one-way) analysis of variance 93
One-tail test 88 98 102 104
One-way (one-factor) analysis of variance 93
One-way ANOVA table 109—115
Operating-characteristic (OC) curve 89 100—101 120
Order condition 233
Ordinary least-squares estimators 133—134 147—149 153
Ordinary least-squares method (OLS) 128—130 136—141 148 152 183
Ordinary least-squares method (OLS), Almon lag model and 196
Ordinary least-squares method (OLS), autocorrelation and 215—216
Ordinary least-squares method (OLS), distributed lag model and 193—195
Ordinary least-squares method (OLS), errors in variables and 209—210 221—222
Ordinary least-squares method (OLS), forecast and 198
Ordinary least-squares method (OLS), functional form and 186—189
Ordinary least-squares method (OLS), heteroscedasticity and 207—209 212—215
Ordinary least-squares method (OLS), in multiple regression analysis 161—171
Ordinary least-squares method (OLS), indirect least squares and 229—230
Ordinary least-squares method (OLS), multicollinearity and 206 210
Ordinary least-squares method (OLS), nonlinear functions and 181
Ordinary least-squares method (OLS), qualitative dependent variable and 184
Ordinary least-squares method (OLS), simultaneous equations methods and 228 232—233 237—238 239
Overidentified equations 229—230 233—235
Parameter estimations, in multiple regression analysis 154—155 161—165 178
Parameter estimations, test of, in simple regression analysis 130—132 141—144 152—153
Parameter(s) 1 5—8 67
Parameter(s), estimation of 67—69
Parameter(s), in simple regression analysis 135
Parameter(s), statistic and 71—72 (see also “Specific parameters”)
Partial autocorrelation function (PACF) 244—245 251—253
Partial-correlation coefficients 158—159 172—173 179
Pearson’s coefficient of skewness see “Skewness coefficient
Percentiles 23—24
Perfect linear relationship 172—173
Perfect multicollinearity 210
permutations 50
Personalistic (subjective) probability 42—43
Platykurtic curve 15
Point estimates 69 76
Poisson distribution 40 55—57 61 65
Polynomial function 181 186—187
Population 1—3
Population mean 19
Population mean, hypothesis testing 87—89 96—101 119—120
Population mean, in estimation 67—69 72—84
Population parameters, functional form and 186—187
Population parameters, in simple regression analysis 148
Population, defined 71
Population, grouped 11—14 19—29 51—52
Population, ungrouped 11—14 20—28
Positive linear correlation 132—133 144—145
Positive linear net relationship 172—173
Positively skewed distribution 15
Power curve 89 100—101 120
Predetermined variables 231—232
Prediction, and forecasting 197—198 (see also “Forecasting”)
Prediction, and forecasting, in multiple regression analysis 154 (see also “Multiple regression analysis”)
Prediction, and forecasting, simple regression analysis for 128 (see also “Simple regression analysis”)
Price elasticity 175—178 181—182 187
probability 1 36—66
Probability distribution (density function, continuous random variable) 41—42 57—58
Probability distribution (density function, continuous random variable), binomial distribution as discrete 39—40 54 64
Probability distribution (density function, continuous random variable), normal distribution as continuous 41—42 57—58 65
Probability distribution (density function, continuous random variable), Poisson distribution as 40 55—57 65
Probability theory 3
Probability, of multiple events 37—39 44—50 63—64
Probability, of single events 36—37 42—44 62—63
Probit model (cumulative normal function) 184 199
Qualitative dependent variable 184—185
Qualitative explanatory variable 182 189—193 203—204
Quartile deviation 13 24
Quartiles 23—24
Random disturbance see “Error term”
Random samplings 3
Random samplings, and sampling distribution of the mean 67—68
Random samplings, in estimation 67—39 72—81 84
Random samplings, in hypothesis testing 67 87—89 95—96
Random samplings, in simple regression analysis 147—148
Random samplings, simple, defined 72
Random variables, continuous 41—42 57—58
Random variables, discrete 39—40 54
Random variables, in binomial distribution 39 51
Random walk 246
Random walk, with drift 246
Random-number table 309
Randomized design, completely 111
RANGE 13 24
Range, coefficients in multiple regression analysis 172
Range, in simple regression analysis 144
Rank (Spearman’s) correlation coefficient 132—133 146
Rank condition 233
Reciprocal function 181 186—187
recursive models 232—233
Reduced-form coefficients 232—237
Reduced-form equations 228—230 231—237
Reduced-form parameters 233
Regression analysis 1 3—4 128—227
Regression analysis, autocorrelation as problem in 208—209 215—220 242
Regression analysis, distributed lag models in 182—183 193—196 204—205
Regression analysis, dummy variables in 182 189—193 203—204
Regression analysis, errors in variables as problems in 209—210 221—222 226—227
Regression analysis, forecasting 183—184 197—198 205
Regression analysis, functional form in 181—182 186—189 202
Regression analysis, heteroscedasticity as problem in 207—208 212—215 223—225
Regression analysis, multicollinearity as problem in 206—207 210—212 222—223
Regression analysis, multiple regression analysis in see “Multiple regression analysis”
Regression analysis, simple regression analysis in see “Simple regression analysis”
Regression sum of squares (RSS) 110—115 132 144 157
| Rejection region, in autocorrelation 208 217
Rejection region, in hypothesis testing 87—89 95—104
Rejection region, in multiple regression analysis 171—172
Rejection region, in simple regression analysis 143
Rejection region, type I and type II errors and 87 95—96 100 119
Relative dispersion 29
Relative frequency (empirical probability) distribution 9 42—44
Relative frequency (empirical probability) distribution, probability distribution distinguished from 51
Relative frequency (empirical probability) distribution, probability or theoretical 97
Representative sample 1—3 67 72
Residual variance 111—113 130
Residual variance, in multiple regression analysis 126 165 171
Residual variance, in simple regression analysis 130
Right-tail test 88—90 97—98 104 110—111
Row mean 111—115
RSS (regression sum of squares) 110—115 132 144 157
Sample (column) mean 92 109—114
Sample in estimation 67 72—76 84
Sample size, in estimation 78—81 85
Sample size, in hypothesis testing 87—88
Sample space 47
Sample variance 109—110
Sample(s) 1 3 72 92
Sample, representative 1—3 67 72
Sampling distribution of biased estimator 147
Sampling distribution of consistent estimator 149
Sampling distribution of the mean 67
Sampling distribution of the mean, empirical 74
Sampling distribution of the mean, in estimation 67—69 72—76 84
Sampling distribution of the mean, in hypothesis testing 87 96—97
Sampling distribution of the mean, theoretical 72—74 78 81 83
Sampling distribution of unbiased estimator 147
SAS 269—271 282—292 293
Scatter diagram 128 134
Semilog function 181—182 186—189
Sequential (tree) diagram 47—48
Serial correlation see “Autocorrelation”
Set theory 38 47
Significance level, heteroscedasticity and 214—215
Significance level, in autocorrelation 208—209 215—220
Significance level, in hypothesis testing 87 95
Significance level, in multiple regresssion analysis 158 171—172 179
Significance level, in simple regression analysis 130—132 143—144
Simple regression analysis 4 128—153
Simple regression analysis, ordinary least-squares method in see “Ordinary least-squares method”
Simple regression analysis, properties of ordinary least-squares estimators in 133—134 147—149 153
Simple regression analysis, test of goodness of fit and correlation in 132—133 144—147 153
Simple regression analysis, tests of significance of parameter estimates in 130—132 141—144 152—153
Simple regression analysis, two-variable linear model of 128 134—136 151
Simultaneous-equations bias 228 231—232
Simultaneous-equations methods (models, system) 1 3—4 228—241
Simultaneous-equations methods (models, system), identification and 229 233—235 239—240
Simultaneous-equations methods (models, system), indirect least squares and 229—230 235—237 240—241
Single events 36—37 42—44 62—63
Skewness, coefficient of (Pearson’s coefficient of skewness) 15—16 29—30
Skewness, coefficient of (Pearson’s coefficient of skewness), binomial distribution and 39 54 64
Skewness, coefficient of (Pearson’s coefficient of skewness), in shape of distribution 14—15
Spearman’s (rank) correlation coefficient 132—133 144—145
Specification of model 2
SSA (sum of suqares) 92—93 110—115
Standard deviation (error) 13—15 26—29
Standard deviation (error), autocorrelation and 208
Standard deviation (error), in binomial distribution 39 54—55
Standard deviation (error), in estimation 67—71 72—76 77—84
Standard deviation (error), in hypothesis testing 88—90 97—99 101—104
Standard deviation (error), in multipe regression analysis 165
Standard deviation (error), in Poisson distribution 56
Standard deviation (error), in simple regression analysis 141
Standard deviation (error), indirect least squares 229 236
Standard deviation (error), of continuous probability distribution 57—58
Standard deviation (error), of lagged values 197
Standard deviation (error), of the estimates 79 130—131 155
Standard deviation (error), probability 62
Standard deviation (error), sampling distribution of the mean 67—71
Statistic 67—69 71—72
Statistical criteria 6
Statistical inference 1 3 67 70—71 84 hypothesis
Statistics 1 2 84
Statistics examination 124—127
Statistics, and econometrics 1 3—5 7—8
Statistics, nature of 1—3 7
Stepwise multiple regression analysis 172—173
Stochastic disturbance see “Error term”
Stochastic equation 1 5 7—8
Stochastic explanatory variables see “Independent variables”
Stochastic term see “Error term”
Stratified sampling 72
Structural (behavioral) equations 228—233
Structural coefficients 223—235
Structural parameters 228—231 233—237
Student’s t distribution see “t distribution”
Subjective (personalistic) probability 42—43
sum of absolute deviations 136—137
Sum of deviations 136—137
Sum of squared deviations 136—137
Sum of squares (SSA) 92—93 110—115
Symmetry, of binomial distribution 39 54 64
Symmetry, of continuous probability distribution 57—58
Symmetry, of distribution 15
Symmetry, of normal distribution 41 57—58
Symmetry, of t distribution 70
systematic sampling 72
t (Student’s t) distribution, confidence intervals for the mean using 70—71 81—84 86
t (Student’s t) distribution, in estimation 81—82
t (Student’s t) distribution, in forecast 184 197—198
t (Student’s t) distribution, in hypothesis testing 88 98
t (Student’s t) distribution, in simple regression analysis 131 143—144
t (Student’s t) distribution, proportions of area for 310
Text formats 266
Theorem 1 (sampling distribution of the mean) 67
Theorem 2 (sampling distribution of the mean) 68 75
Theoretical sampling distribution of the mean 72—74 78 81 83
Third movement 15 30
Three-variable linear model 154—155 161—165 178
Time-series analysis 136 208 215 242—265
Time-series data 6
Total sum of squares (TSS) in hypothesis testing 92 110—114
Total sum of squares (TSS) in multiple regression analysis 157
Total sum of squares (TSS) in simple regression analysis 132 144
Tree (sequential) diagram 47—48
Trend stationary 246
TSS see “Total sum of squares”
Two-factor ANOVA table 113—115
Two-stage least squares (2SLS) 230 237—238 241
Two-tail test 87—8 96—97 101 103 143 167
Two-variable linear model 128 134—136 151
Two-way (two-factor) analysis 113
Two-way (two-factor) analysis, ANOVA table 113—115
Type I error 87 95—96 100 119
Type II error 87 95—96 100 119
Unbiased estimate(s), in forecast 184 197
Unbiased estimate(s), in functional form 181
Unbiased estimate(s), in hypothesis testing 103
Unbiased estimate(s), in multiple regression analysis 155 163
Unbiased estimate(s), in simple regression analysis 141 147
Unbiased estimate(s), of forecast-error variance 197
Unbiased estimators 147—148
Unbiased estimators, in estimation 76—77
Unbiased estimators, qualitative dependent variable and 184
Unbiased point estimate 69 85
Underidentified equation 229—230 233—235
Unexplained residual 111—115
Ungrouped data 118
Uniform distribution 245
Unit root 11—14 20—28
Variables see “Specific variables”
Variance 26—29
Variance, analysis of 92—93 109—115
Variance, ANOVA tables 109—115
Variance, as equal mean-square error plus, square of bias of estimator 148
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