在Gauss版转了很久，发现新手上路最大的问题就在于Gauss没有一本好的入门教材。

这是我在国外的大学网络课程中找到的一门用Gauss来进行数量分析的课程，用Gauss8.0，整门课程从Gauss的入门开始讲起，包含OLS、ML、GMM等方法，讲述了线性模型、ARMA等模型及检验，涉及蒙特卡罗实验。

我作为一个新手，认为这是我找到的最实用全面的资料。由于搜索和下载都比较费时，希望大家给点辛苦钱，课程的所有课件及Gauss code都发给大家。但是不会要大家很多钱，所以，免费送给大家课程配套使用的Gauss 8.0的软件。希望我们都能尽快入门！

Syllabus：

Session 1: Introduction to GAUSS 8.0

• Installation of GAUSS (Light)
• Installation of GAUSS Libraries
• Explanation of the Command Windows
• Basic Commands (Definition of matrices, logical operators, loops and if statements, random number generators, etc.)
• Use of Libraries
• Data Handling
• Procedures
• Global and Local Variables
  

Session 2: Descriptive Data Analysis

• Introduction to Descriptive Statistics
• Development of Procedures for Descriptive Data Analysis (e.g. skewness, kurtosis, normality tests, autocovariance and autocorrelation functions, kernel density estimation, etc.)
• Application to Financial Data: Stylized Facts at Moderate and High Frequencies
• Use of the Graphic Library: pgraph
  • Line graphs, Scatter plots, Histograms
  • (Partial) Autocorrelograms
  • window command
  • generation of eps files for LaTeX (epsfig command)
    

Session 3 & 4: Linear Regression Model

• Introduction to the Multivariate Linear Regression Model
• OLS Regression (by hand, ols command)
• Programming of a Standard Regression Output
  • T- Test
  • P-Value
  • White and Newey West Variance-Covariance Matrices
  • Wald F-Test
• Application to Wage Equations and Time Series Analysis (AR(p) models)
  
  

Session 5 & 6: Maximum Likelihood Estimation of ARMA(p,q) models

• Introduction to Maximum Likelihood (ML) Estimation
• Introduction to Linear Time Series Models
• Numerical Optimization: optmum and maxlik libraries
• Programming of the conditional likelihood for an ARMA(p,q) model
• Application to the Dynamic Properties of High Frequent Financial Data
  • Estimation of Different ARMA(p,q) models for NYSE stocks series
  • Model Evaluation and Diagnostics making use of Session 2
  • Information Criteria
    

Session 7: Maximum Likelihood Estimation (Ordered) Probit & Logit Models

• Introduction to Ordered Response Models
• Programming of the Likelihood Function for an Ordered Probit/Logit Model
• Application to the ZEW Financial Markets Surveys
  • Evaluation of the Forecasting Performance of Financial Experts
  • Forecasting Criteria

Session 8: Monte Carlo Study and Bootstrapping Techniques

• Introduction to Asymptotics
• Monte Carlo and Bootstrapping Techniques
• Application to the Asymptotic Properties of OLS and ML Estimators
  
  

Session 9: Volatility Estimation 1 & Risk Measurement

• Introduction to ARMA-GARCH Models
• Introduction to Value-at-Risk (VaR) and Expected Shortfall
• ML Estimation of ARMA-GARCH Models
  • Programming of the Conditional ARMA-GARCH Likelihood Function
  • Model Evaluation
  • Extension of the Program of Session 5 & 6
• Application to Value-at-Risk and Expected Shortfall using Conditional Volatility Estimates
  • Computation of the conditional VaR and Expected Shortfalls for Short and Long positions
  • Back Testing

Session 10: Volatility Estimation 2 & Option Pricing

• Introduction to Realized Volatility Estimation
• Introduction to Option Pricing (Black-Scholes Formula)
• Estimation of Volatility using High Frequency Financial Data
  • Realized Volatility
  • Volatility Signature Plots
• Application to Option Pricing using Black-Scholes Formula
  
  • Computation of Realized Volatility Estimates for Stocks and Exchange Rates
  • Programming of Black-Scholes Formula

Session 11 & 12: Asset Pricing and GMM Estimation

• Introduction to Asset Pricing and GMM Estimation a la Cochrane
• Programming of the GMM Objective Function
• Application to Asset Pricing
  • Stochastic Discount Factor Models
    (Consumption Based Model, CAPM Model, Fama-French Factor Models)
  • Model Evaluation