Conditional Mean Models
Autoregressive (AR), moving average (MA), ARMA, ARIMA, ARIMAX, and seasonal models
应用
| 计量经济学建模者 | Analyze and model econometric time series |
职能
Examples and How To
Create Model
- 指定条件平均模型
Create conditional mean models using阿里玛or the Econometric Modeler app. - Modify Properties of Conditional Mean Model Objects
使用点表示法更改可修改的模型属性。 - Specify Conditional Mean Model Innovation Distribution
Specify Gaussian or t distributed innovations process, or a conditional variance model for the variance process. - 使用计量经济学建模应用程序指定创新分布
交互式指定atArima模型的创新分布。 - AR Model Specifications
使用使用阿里玛or the Econometric Modeler app. - MA Model Specifications
Create invertible moving average models using阿里玛or the Econometric Modeler app. - ARMA模型规格
使用使用的固定和反转回归自回旋运动平均模型阿里玛or the Econometric Modeler app. - ARIMA Model Specifications
Create autoregressive integrated moving average models using阿里玛or the Econometric Modeler app. - ARIMAX Model Specifications
Create ARIMAX models using阿里玛or the Econometric Modeler app. - Multiplicative ARIMA Model Specifications
使用使用阿里玛or the Econometric Modeler app. - Specify Multiplicative ARIMA Model
Create a seasonal ARIMA model. - 指定条件均值和方差模型
创建一个复合条件均值和方差模型。
适合数据
- Time Base Partitions for ARIMA Model Estimation
When you fit a time series model to data, lagged terms in the model require initialization, usually with observations at the beginning of the sample. - Implement Box-Jenkins Model Selection and Estimation Using Econometric Modeler App
交互式实现盒子 - 詹金斯方法，以选择单变量条件平均模型的适当数量的滞后。然后，将模型安装到数据并将估计模型导出到命令行以生成预测。 - Box-Jenkins差异与Arima估计
Compare Box-Jenkins and ARIMA estimation. - Choose ARMA Lags Using BIC
Select ARMA model using information criteria. - 使用计量器Modeler应用程序估算乘法ARIMA模型
互动估计乘法季节性Arima模型。 - 估计乘法Arima模型
估计乘法季节性Arima模型。 - Model Seasonal Lag Effects Using Indicator Variables
通过指定乘法模型或使用季节性假人来估算季节性Arima模型。 - Estimate ARIMAX Model Using Econometric Modeler App
交互式指定和估计Arimax模型。 - Estimate Conditional Mean and Variance Model
Estimate a composite conditional mean and variance model. - Perform ARIMA Model Residual Diagnostics Using Econometric Modeler App
Interactively evaluate model assumptions after fitting data to an ARIMA model by performing residual diagnostics. - Infer Residuals for Diagnostic Checking
从拟合的Arima模型中推断残差。 - 分享计量经济学建模者应用程序会话的结果
Export variables to the MATLAB®工作空间，生成纯文本和实时功能，这些功能返回在应用程序会话中估计的模型，或在计量经济学建模者应用程序中将活动记录到时间序列和估计模型的报告中。
生成模拟或冲动响应
- 模拟固定过程
Simulate stationary autoregressive models and moving average models. - 模拟趋势平台和差异平台过程
Illustrate the distinction between trend-stationary and difference-stationary processes by simulation. - Simulate Multiplicative ARIMA Models
Simulate sample paths from a multiplicative seasonal ARIMA model. - 模拟条件均值和方差模型
从复合条件均值和方差模型中模拟响应和条件差异。 - 绘制条件平均模型的脉冲响应函数
绘制单变量自回归运动平均模型的脉冲响应函数。
生成最小平方误差预测
- 在使用计量器建模器应用程序创建模型后比较预测性能
通过比较估计模型的AIC值，可以进行交互式选择ARIMA模型的滞后。然后，将几个模型导出到命令行，以比较其预测性能。 - 预测乘法Arima模型
Forecast a multiplicative seasonal ARIMA model. - Convergence of AR Forecasts
评估来自AR模型的预测的渐近收敛性，并比较有或不使用预样品数据的预测。 - Forecast Conditional Mean and Variance Model
Forecast responses and conditional variances from a composite conditional mean and variance model. - ARX模型的预测IgD速率
Forecast an ARIMAX model by computing MMSE forecasts or using Monte Carlo simulation. - 指定预先样本和预测期数据以预测Arimax模型
This example shows how to partition a timeline into presample, estimation, and forecast periods, and it shows how to supply the appropriate number of observations to initialize a dynamic model for estimation and forecasting.
概念
- 计量器建模应用程序概述
The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate or multivariate time series data.
- Specifying Univariate Lag Operator Polynomials Interactively
Specify univariate lag operator polynomial terms for time series model estimation using Econometric Modeler.
- Conditional Mean Models
了解条件平均模型的特征和形式。
- Autoregressive Model
Learn about autoregressive models.
- 移动平均模型
Learn about moving average models.
- 自回归运动平均模型
Learn about autoregressive, moving average models.
- ARIMA Model
了解自回归的集成运动平均模型。
- Multiplicative ARIMA Model
Learn about addressing seasonality and potential seasonal unit roots using multiplicative ARIMA models.
- Arima模型包括外源协变量
了解包含外源变量的线性项的Arima模型。
- Maximum Likelihood Estimation for Conditional Mean Models
Learn how maximum likelihood is carried out for conditional mean models.
- Conditional Mean Model Estimation with Equality Constraints
使用已知参数值在估计过程中约束模型。
- 条件平均模型估计的预先样本数据
指定预先样本数据以初始化模型。
- Initial Values for Conditional Mean Model Estimation
为估计指定初始参数值。
- Optimization Settings for Conditional Mean Model Estimation
通过指定替代优化选项来解决估计问题。
- 赖斯的蒙特卡罗模拟tional Mean Models
Learn about Monte Carlo simulation.
- 条件平均模型模拟的预先样本数据
了解模拟的预先样本要求。
- Transient Effects in Conditional Mean Model Simulations
Learn how to minimize transient effects.
- 蒙特卡洛预测条件平均模型
Learn about Monte Carlo forecasting.
- MMSE Forecasting of Conditional Mean Models
Learn about MMSE forecasting.
特色示例
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