## Computational Economics and Finance: Modeling and Analysis with MathematicaThis book/software package divulges the combined knowledge of a whole international community of Mathematica users - from the fields of economics, finance, investments, quantitative business and operations research. The 23 contributors - all experts in their fields - take full advantage of the latest updates of Mathematica in their presentations and equip both current and prospective users with tools for professional, research and educational projects. The real-world and self-contained models provided are applicable to an extensive range of contemporary problems. The DOS disk contains Notebooks and packages which are also available online from the TELOS site. |

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State of the art in its time. Things have moved on since it's release, so now a bit dated ... but still lots of interesting ideas from a diverse and well-selected set of authors.

### Contents

Linear Programming with Mathematical The Simplex Algorithm | 3 |

Linear Programming with Mathematica Sensitivity Analysis | 31 |

Optimization with Mathematica | 65 |

Optimizing with Piecewise Smooth Functions | 104 |

Data Screening and Data Envelopment Analysis | 120 |

Efficiency in Production and Consumption | 131 |

Cost Allocation | 143 |

Simulating the Effects of Mergers Among Noncooperative Oligopolists | 177 |

Yield Management | 235 |

Implementing Numerical Option Pricing Models | 251 |

YieldCurve | 269 |

Log Spectral Analysis Variance Components of Asset Prices | 305 |

Data Analysis Using Mathematica | 330 |

Doing Monte Carlo Studies with Mathematica | 362 |

Random Title Manipulating Probability Density Functions | 416 |

Auctions | 199 |

### Other editions - View all

Computational Economics and Finance: Modeling and Analysis with Mathematica® Hal R. Varian No preview available - 2011 |

### Common terms and phrases

additional algorithm allows analysis apply assumed auction average basic calculate capacity chapter choice coefficient compute consider constraints cost defined demand derive desks determine distribution domain economic effects efficiency equal estimate example expected final firm function given Graphics illustrate increase Infinity interest linear programming marginal Mathematica mean measure mergers methods Monte Carlo normal Note objective observations optimal optimal solution option output package parameters plot positive possible problem production profit programming provides quantity random regression Research residuals resource returns rules sample serial correlation shadow prices simple smooth solution solve specified spline standard statistic structure tableau term transform True unit University variables variance vector yield zero zero-padded