## Econometric Models and Economic ForecastsSingle-equation regression models (introduccion to the regression model; elementary statistics: a review; two-variable regression model; multiple regression model; using the multiple regression model; serial correlation and heteroscedasticity; instrumental variables and model specification; forecasting with a single-equation regression model; single-equation estimation: advanced topics; models of quantitative choice); Multi-equation simulation models (simultaneous-equation estimation; introduction to simulation models; dinamic behavior of simulation models); The-series models (smoothing and extrapolation of timer series; properties of stochastic time series; linear time-series models; estimating and checking time-series models; forecasting with time-series models; applications of time-series models). |

### From inside the book

85 pages matching **ordinary least squares** in this book

Where's the rest of this book?

Results 1-3 of 85

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

SINGLEEQUATION REGRESSION MODELS | 2 |

A Review | 19 |

The TwoVariable Regression Model | 46 |

Copyright | |

19 other sections not shown

### Other editions - View all

### Common terms and phrases

2SLS analysis ARIMA model associated assume assumption autocorrelation function autoregressive autoregressive process behavior calculate Chapter coefficients confidence intervals consider consistent estimator consumption covariance degrees of freedom demand dependent variable differenced discussion dynamic econometric economic endogenous variables equal error term error variance estimated parameters ex post forecast example exogenous expected value explanatory variables F distribution FIGURE follows forecast error given heteroscedasticity income independent individual intercept interest rate least-squares estimation linear regression matrix mean moving average nonlinear nonstationary normally distributed null hypothesis observations obtain ordinary least squares parameter estimates percent level period predict probit problem procedure random variable random walk reduced-form regression equation reject the null relationship residuals sample autocorrelation function seasonal serial correlation shown in Fig significant simulation model single-equation slope solution specification standard error stationary statistic stochastic structural sum of squares techniques time-series model tion zero