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Time Series MT
GAUSS TSMT应用模块为最大似然估计和状态空间估计、模型诊断以及单变量、多变量和非线性时间序列模型的预测提供了一套全面的工具。
Time Series MT 3.0
新的Times Series MT 3.0提供了时间序列模型的全面处理，包括模型诊断，最大似然估计和状态空间估计，以及预测。Time Series MT还包括用于管理面板系列数据以及估计和诊断面板系列模型的工具，包括随机效应和固定效应。
平台: Windows, Mac 和Linux
需求: GAUSS/GAUSS Engine 18 or 更高
单变量时间序列模型:
条件均值模型:
- Autoregressive moving average (ARMA)
- Seasonal autoregressive moving average (SARMA)
- Autoregressive moving average with exogenous variables (ARMAX)
- Autoregressive integrated moving average (ARIMA)
- Seasonal autoregressive integrated moving average (SARIMA)
条件方差模型:
- Generalized autoregressive conditional heteroscedasticity (GARCH)
- GARCH with a unit root (IGARCH)
- GARCH with asymmetrical effects (GJRGARCH)
- GARCH-in-mean (GARCHM)
多元时间序列模型:
条件均值模型:
- Vector autoregressive moving average (VARMA)
- Vector autoregressive moving average with exogenous variables (VARMAX)
- Seasonal vector autoregressive moving average (SVARMA)
- Seasonal vector autoregressive moving average with exogenous variables (SVARMAX)
- Vector error correction models (VECM)
面板数据和其他模型:
- Fixed effects and random effects models (TSCS)
- Least squares dummy variable (LSDV)
- Kalman Filter for state-space modeling.
非线性时间序列模型:
- Switching regression
- Structural break models
- Threshold autoregressive models (TAR)
参数不稳定性测试:
- Chow forecast
- CUSUM Test of Coefficient Equality
- Hansen-Nymblom test
- Rolling Regressions
单位根和协整检验
- Augmented Dickey-Fuller
- Breitung and Das
- Im, Pesaran, and Shin (IPS)
- Johansen’s trace and maximum eigenvalue statistic
- Levin-Lin-Chu (LLC)
- Phillips-Perron
- Zivot and Andrews
模型选择和评估
- Akaike information criterion (AIC)
- Adjusted R-Squared
- Schwartz Bayesian information criterion (BIC)
- Kwiatkowski–Phillips–Schmidt–Shin (KPSS)
- Likelihood ratio statistic (LRS)
- Multivariate Portmanteau statistic
- Wald statistic
- Friedman, Frees and Pesaran tests for cross-sectional independence in panel data models.
在线留言
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400-621-1085
