## Computational Methods in Statistics and EconometricsReflecting current technological capacities and analytical trends, Computational Methods in Statistics and Econometrics showcases Monte Carlo and nonparametric statistical methods for models, simulations, analyses, and interpretations of statistical and econometric data. The author explores applications of Monte Carlo methods in Bayesian estimation, state space modeling, and bias correction of ordinary least squares in autoregressive models. The book offers straightforward explanations of mathematical concepts, hundreds of figures and tables, and a range of empirical examples. A CD-ROM packaged with the book contains all of the source codes used in the text. |

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### Contents

1 Elements of Statistics | 1 |

Part I Monte Carlo Statistical Methods | 78 |

2 Random Number Generation I | 79 |

3 Random Number Generation II | 170 |

Part II Selected Applications of Monte Carlo Methods | 247 |

4 Bayesian Estimation | 248 |

5 Bias Correction of OLSE in AR Models | 283 |

6 State Space Modeling | 318 |

Part III Nonparametric Statistical Methods | 394 |

7 Difference between TwoSample Means | 395 |

8 Independence between Two Samples | 446 |

489 | |

492 | |

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### Common terms and phrases

acceptance probability alpha beta binomial BMLE Cauchy distribution computational consider degrees of freedom denotes discussed in Section distributed with mean distribution function distribution with parameters error term example Figure filtering Fisher test follows Gibbs sampler given implies importance resampling Input joint density Kurtosis likelihood function Lines M2SE maximum likelihood estimator mean and variance Metropolis-Hastings algorithm moment-generating function Monte Carlo multivariate mutually independently distributed Nikkei stock average noncentral nonparametric tests normal random draws null hypothesis null hypothesis H0 obtained OLSE Output percent points permutation test probability density function procedure random number random variable regression coefficient rejection sampling represented RMSE sample powers sampling density score test shown Simulation Skewness smoothing source code space model standard error standard normal random subroutine Table Tanizaki target density test statistic theorem unbiased estimator uniform distribution uniform random draw urnd(ix,iy,rn utilized vector Wilcoxon test zero