搜索资源列表
gdfm_toolbox_1.3
- 基于虑子方法拟合平滑转换向量自回归模型，包含若干分解算法(Fitting smooth transformation vector autoregressive model)
matlab-spatial analysis
- 本文给出了如何使用matlab进行空间计量分析，包括空间自回归检验、空间滞后模型、空间误差模型和空间杜宾模型的选择及检验结果，选择模型通过计算LM值、Wald检验和LR检验，同时可以通过LR检验选择模型类别（时间固定、空间固定及时空双固定）(This paper presents how to use matlab to carry out spatial econometric analysis, including spatial autoregressive test, spatial l
Statistics Code by Matlab
- 包含一元线性回归分析、多元线性回归分析、逐步回归分析、非线性回归分析、主成分分析、因子分析、层次聚类分析、判别分析、自相关分析和自回归分析的全套Matlab源代码(MATLAB source code including linear regression analysis, multiple linear regression analysis, stepwise regression analysis, non-linear regression analysis, principal co
tvpvar
- 时变参数向量自回归模型的估计代码以及模型应用方法(Estimation code and application of Time-Varying parameter vector autoregressive model)
garchsk
- Jondeau 、Leon 等提出自回归条件方差—偏度—峰度模型(GARCHSK)，用于同时描述收益率二阶矩、三阶矩和四阶矩的时变特征。此文件为该模型代码。(Jondeau, Leon et al. Proposed an autoregressive conditional variance-skewness-kurtosis model (GARCHSK), which was used to describe the time-varying characteristics of the
65143424ARIMA
- 自回归移动平均模型(Autoregressive Integrated Moving Average Model)的Matlab实现,时间序列分析代码((Autoregressive moving average model (Autoregressive Integrated Moving Average Model) to achieve the Matlab))
TVP-VAR
- 包含了目前主流的时变参数向量自回归模型代码以及文献(Including the current mainstream time-varying parameter vector autoregressive model code and Literature)
MI-TVP-SV-VAR
- Koop大神写出来的时变向量自回归模型的进化版，希望对大神写作有帮助。(The evolutionary version of the time-varying vector autoregressive model written by the Koop hopes, to be helpful to your writing.)
ARIMA预测
- ARIMA整合移动平均自回归模型，时间序列预测分析方法之一，可用于股价预测。(ARIMA integrates moving average autoregressive model and time series forecasting analysis method, which can be used for stock price forecasting.)
贝叶斯向量自回归MATLAB代码
- 使用matlab实现贝叶斯向量自回归模型，可用于经济学中的预测(It can realize Bayesian vector autoregressive model, and it can be used to predict in economics.)
模拟验证一阶自回归模型中自回归系数
- 运用Python的数组和矩阵操作模拟验证一阶自回归模型中，自回归系数OLS估计量的有限样本偏差问题。(Python array and matrix operations are used to simulate and verify the finite sample bias of OLS estimator of autoregressive coefficient in the first-order autoregressive model.)