## Econometrics and Data Analysis for Developing CountriesGetting accurate data on less developed countries has created great problems for studying these areas. Yet until recently students of development economics have relied on standard econometrics texts, which assume a Western context. Econometrics and Data Analysis for Developing Countries solves this problem. It will be essential reading for all advanced students of development economics. |

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### Contents

Introduction | 1 |

Model specification and applied research | 23 |

Modelling an average | 44 |

Outliers skewness and data transformations | 75 |

Data analysis and simple regression | 111 |

interpreting multiple regression coefficients | 163 |

infant mortality05 HDI2 and urban | 191 |

Model selection and misspecification in multiple regression | 208 |

Trends spurious regressions and transformations | 335 |

Misspecification and autocorrelation | 366 |

regression | 376 |

Cointegration and the error correction model | 393 |

S Scatter plot for Costa Rican consumption against income | 404 |

Misspecification bias from single equation estimation | 415 |

Estimating simultaneous equation models | 437 |

The data sets used in this book | 455 |

Dealing with heteroscedasticity | 251 |

S Residual versus predicted plot | 274 |

Categories counts and measurements | 279 |

worker data | 320 |

Statistical tables | 463 |

481 | |

### Other editions - View all

Econometrics and Data Analysis for Developing Countries Chandan Mukherjee,Howard White,Marc Wuyts Limited preview - 2013 |

Econometrics for Developing Countries Chandan Mukherjee,Howard White,Mark Wuyts No preview available - 1997 |

### Common terms and phrases

applied autocorrelation average bias box plot calculated categorical variables Chapter cointegration confidence interval consumption function correlation critical value data analysis data file data point data set degrees of freedom demand dependent variable dummy variables econometric equal equation error term example Exercise exogenous explanatory variables F-test Figure follows gender given GNP per capita Hence heteroscedasticity homoscedastic hypothesis testing income variable infant mortality inferences intercept least squares estimators linear regression log income logarithm logit regression median model specification multiple regression normal distribution normality assumption null hypothesis observations obtained omitted variable omitted variable bias outliers parameters partial regression plot population mean probability problem random walk reason regres regression analysis regression coefficients regression line regression model regressors residuals restricted sample mean scatter plot shows simple regression sion skewness slope coefficient spurious standard deviation standard errors stationary statistic sum of squares Table tails tion variance variation workers zero