Econometric Models and Economic ForecastsThis updated edition of the text has been restructured into four parts: multiple regression model; single-equation regression models; revised exposition and a small macroeconomic model; and a revised treatment of time-series analysis. |
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Results 1-3 of 52
Page 39
... 1 . 15 μ . 55 if X = 3 2 . 1.5 surs 5.5 if X = 3.5 3 . .7 ≤ x ≤ 4.7 if X = 2.7 Some of these intervals can be expected to exclude the CHAPTER 2 : ELEMENTARY STATISTICS : A REVIEW 39 HYPOTHESIS TESTING AND CONFIDENCE INTERVALS.
... 1 . 15 μ . 55 if X = 3 2 . 1.5 surs 5.5 if X = 3.5 3 . .7 ≤ x ≤ 4.7 if X = 2.7 Some of these intervals can be expected to exclude the CHAPTER 2 : ELEMENTARY STATISTICS : A REVIEW 39 HYPOTHESIS TESTING AND CONFIDENCE INTERVALS.
Page 66
... CONFIDENCE INTERVALS Given the knowledge of the distributions of â and B , it is possible to construct confidence intervals and test hypotheses concerning the regression parameters . Confidence intervals provide a range of values which ...
... CONFIDENCE INTERVALS Given the knowledge of the distributions of â and B , it is possible to construct confidence intervals and test hypotheses concerning the regression parameters . Confidence intervals provide a range of values which ...
Page 209
... confidence intervals by using the t distribution . Writing the estimated forecast error variance s } = ܐܨ 1 ( Xz + ... confidence interval for Ŷ + 1 is thus given by ŶT + 1 - 1.05§ƒ ≤ YT + 1 ≤ ŶT + 1 + 1.05 $ ƒ ( 8.23 ) An example of ...
... confidence intervals by using the t distribution . Writing the estimated forecast error variance s } = ܐܨ 1 ( Xz + ... confidence interval for Ŷ + 1 is thus given by ŶT + 1 - 1.05§ƒ ≤ YT + 1 ≤ ŶT + 1 + 1.05 $ ƒ ( 8.23 ) An example of ...
Contents
Introduction to the Regression Model | 1 |
1 The Use of Summation Operators | 13 |
A Review | 19 |
Copyright | |
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Econometric Models and Economic Forecasts Robert S. Pindyck,Daniel L. Rubinfeld No preview available - 1998 |
Common terms and phrases
2SLS a₂ actual ARIMA model associated assume assumption autocorrelation function autoregressive B₁ B₂ C₁ calculate Chapter coefficients confidence intervals consider consistent estimator consumption covariance degrees of freedom demand dependent variable dynamic econometric endogenous variables equal error term error variance example exogenous explanatory variables F distribution FIGURE follows forecast error heteroscedasticity income independent intercept interest rate least-squares estimation linear regression matrix mean measure moving average nonlinear nonstationary normally distributed null hypothesis observations obtain ordinary least squares P₁ parameter estimates percent level period predict procedure random variable random walk reduced form regression equation regression model reject the null residuals sample autocorrelation function serial correlation significant single-equation slope specification standard deviation standard error stationary statistic stochastic sum of squares time-series model uncorrelated w₁ X₁ Y₁ ŶT+1 zero ΣΧ