| 本书坚持循序渐进、理论联系实际的原则，以各种丰富易懂的例证生动地介绍了一元回归分析法及时间序列等多种计算模型，非常适合课堂教学和读者自学使用。 |
| 罗伯特S. 平狄克(Robert S. Pindyck) 麻省理工学院管理分院应用经济学教授，美国经济研究学会会员。所著的《微观经济学》广为流传 。 丹尼尔L. 鲁宾费尔德(Daniel L. Rubinfeld) 加州大学伯克利分校法律及经济学教授，美国经济研究学会会员，美国行为科学研究中心成员，《微观经济学》、《法律与经济学的国际研究》一书的作者之一。 .. << 查看详细 |
| part 1 the basics of regression analysis 1 introduction to the regression model 1.1 curve fitting 1.2 derivation of least squares appendix 1.1 the use of summation operators appendix 1.2 derivation of least-squares parameter estimates 2 elementary statistics: a review 2.1 random variables 2.2 estimation 2.3 desirable properties of estimators 2.4 probability distributions 2.5 hypothesis testing and confidence intervals 2.6 descriptive statistics appendix 2.1 the properties of the expectations operator appendix 2.2 maximum-likelihood estimation 3 the two-variable regression model 3.1 the model 3.2 best linear unbiased estimation 3.3 hypothesis testing and confidence intervals 3.4 analysis of variance and correlation .appendix 3.1 variance of the least-squares slope estimator appendix 3.2 some properties of the least-squares residuals 4 the multiple regression model 4.1 the model 4.2 regression statistics 4.3 f tests,r,and corrected r 4.4 multicollinearity 4.5 standardized coefficients and elasticities 4.6 partial correlation and stepwise regression appendix 4.1 least-squares parameter estimation appendix 4.2 regression coefficients appendix 4.3 the multiple regression model in matrix form part 2 single-equation regression models 5 using the multiple regression model 5.1 the general linear model 5.2 use of dummy variables 5.3 the use of t and f tests for hypotheses involving more than one parameter 5.4 piecewise linear regression 5.5 the multiple regression model with stochastic explanatory variables appendix 5.1 tests involving dummy-variable coefficients 6 serial correlation and heteroscedasticity 6.1 heteroscedasticity 6.2 serial correlation appendix 6.1 generalized least-squares estimation 7 instrumental variables and model specification 7.1 correlation between an independent variable and the error term 7.2 errors in variables 7.3 specification error 7.4 regression diagnostics 7.5 specification tests appendix 7.1 instrumental-variables estimation in matrix form 8 forecasting with a single-equation regression model 8.1 unconditional forecasting 8.2 forecasting with serially correlated errors 8.3 conditional forecasting appendix 8.1 forecasting with the multiple regression model 9 single-equation estimation: advanced topics 9.1 distributed lag models 9.2 tests for causality 9.3 missing observations 9.4 the use of panel data appendix 9.1 estimating confidence intervals for long-run elasticities 10 nonlinear and maximum-likelihood estimation 10.1 nonlinear estimation 10.2 maximum-likelihood estimation 10.3 arch and garch models appendix 10.1 generalized method of moments estimation 11 models of qualitative choice 11.1 binary-cholce models 11.2 multiple-choice models 11.3 censored regression models appendix 11.1 maximum-likelihood estimation of the logit and probit models part 3 multi-equation models 12 simultaneous-equation estimation 12.1 introduction to simultaneous-equation models 12.2 the identification problem 12.3 consistent parameter estimation 12.4 two-stageleast squares 12.5 simultaneous-equation estimation with serial correlation and lagged dependent variables 12.6 more advanced estimation methods appendix 12.1 the identification problem in matrix form appendix 12.2 two-stage least squares in matrix form appendix 12.3 seemingly unrelated regression estimation in matrix form 13 introduction to simutation models 13.1 the simulation process 13.2 evaluating simulation models 13.3 a simulation example 13.4 model estimation 13.5 nonstructural models: vector autoregressions 13.6 modeling with limited data 14 dynamic behavior of simulation models 14.1 model behavior: stability and oscillations 14.2 model behavior: multipliers and dynamic response 14.3 the impulse response function and vector autoregressions 14.4 adjusting simulation models 14.5 stochastic simulation appendix 14.1 s small macroeconomic model part 4 time-series models 15 smoothing and extrapolation of time series 15.1 simple extrapolation models 15.2 smoothing and seasonal adjustment 16 properties of stochastic time series 16.1 introduction to stochastic time-series models 16.2 characterizing time series: the autocorrelation function 16.3 testing for random walks 16.4 co-integrated time series appendix 16.1 the autocorrelation function for a stationary process 17 linear time-series models 17.1 moving average models 17.2 artoregressive models 17.3 mixed autoregressive-moving average models 17.4 homogeneous nonstationary processes: arima models 17.5 specification of arima models appendix 17.1 stationarity, invertibility, and homogeneity 18 estimating and forecasting with time-series models 18.1 model estimation 18.2 diagnostic checking 18.3 minimum mean-square-error forecasts 18.4 computing a forecast 18.5 the forecast error 18.6 forecat confidence intervals 18.7 properties of arima forecasts 18.8 two examples 19 estimating and forecasting with times-series models 18.1 model estimation 18.2 diagnostic checki ng 18.3 minimum mean-square-error forecasts 18.4 computing a forecast 18.5 the forecast error 18.6 forecast confidence intervals 18.7 properties of arima forecasts 18.8 two examples 19 applications of time-series models 19.1 review of the modeling process 19.2 models of economic variables: inventory investment 19.3 forecasting seasonal telephone data 19.4 combining regression analysis with a time-series model: transfer function models 19.5 a combined regression-term savings deposit flows 19.6 a combined regression-time -series model to forecast interest rates statistical tables solutions to selected problems indexes author index subject index |
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