Introduction to Econometrics Toolbox
26:17In this webinar, we’ll demonstrate lected features of Econometrics Toolbox.Econometrics Toolbox lets you perform Monte Carlo simulation and forecasting with
linear and nonlinear stochastic differential equations (SDEs) and build univariate
ARMAX/G
Econometrics Toolbox
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Model and analyze financial and economic systems using statistical methods
Econometrics Toolbox™ provides functions for modeling economic data. You can lect and calibrate economic models for simulation and forecasting. For time ries modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with veral GARCH variants, multivariate VARMAX models, and
cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can u a variety of diagnostic functions for model lection, including hypothesis, unit root, and stationarity tests.
Key Features
▪Univariate ARMAX/GARCH composite models, including EGARCH, GJR, and other variants
▪Multivariate simulation and forecasting of VAR, VEC, and cointegrated models
▪State-space models and Kalman filters for parameter estimation
▪Tests for unit root (Dickey-Fuller, Phillips-Perron) and stationarity (Leybourne-McCabe, KPSS)
fsl▪Statistical tests, including likelihood ratio, LM, Wald, Engle’s ARCH, and Ljung-Box Q
▪Cointegration tests, including Engle-Granger and Johann
▪Diagnostics and utilities, including AIC/BIC model lection and partial-, auto-, and cross-correlations ▪Hodrick-Prescott filter for business-cycle analysis
Time-Series Modeling
Econometrics Toolbox facilitates the multistep process of identifying and testing univariate and multivariate time-ries models for financial and econometric data. The toolbox supports the full model development and analysis workflow:
▪Data analysis and preprocessing
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▪Parameter estimation
▪Simulation
▪Forecasting
Business Cycle Analysis Using Hodrick-Prescott Filter
U the Hodrick-Prescott filter to analyze GNP cyclicality.
去你妈逼Introduction to Econometrics Toolbox6:26
Create a predictive time-ries model of a stock index.
Univariate Time-Series Modeling
managingdirectorTime-ries modeling capabilities in Econometrics Toolbox are designed to capture characteristics commonly associated with financial and econometric data, including data with fat tails, volatility clustering, and leverage effects.
Supported conditional mean models include:
▪Autoregressive moving average (ARMA)
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▪Autoregressive moving average with exogenous inputs (ARMAX)
▪Autoregressive integrated moving average (ARIMA) with exogenous inputs (ARIMAX)
▪Regression with ARIMA error terms
Supported conditional variance models include:
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▪Generalized autoregressive conditional hetreroscedasticity (GARCH)
▪Glosten-Jagannathan-Runkle (GJR)
▪Exponential GARCH (EGARCH)
Multiple Time-Series Modeling
Econometrics Toolbox supports multivariate time-ries analysis by extending capabilities for univariate models. Supported models include:
▪Vector autoregressive (VAR)
▪Vector moving average (VMA)
▪Vector autoregressive moving average (VARMA)
▪Vector autoregressive moving average with exogenous inputs (VARMAX)
▪Vector error-correction (VEC)
Modeling the United States Economy
Develop a small macroeconomic model in the style of Smets and Wouters.
Model Identification and Analysis
With Econometrics Toolbox, you can lect and test models by specifying a model structure, identifying the model order, estimating parameters, and evaluating residuals. A variety of pre- and post-estimation diagnostics and tests support the analys, including:
▪Likelihood ratio, Wald, and Lagrange multiplier tests for model specification
▪Akaike and Bayesian information criteria for model order lection
▪Engle’s test for the prence of ARCH/GARCH effects
▪Sample autocorrelation, cross-correlation, and partial autocorrelation functions
▪Ljung-Box Q (portmanteau) test for autocorrelation
pullin▪Dickey-Fuller and Phillips-Perron unit root tests
▪KPSS and Leybourne-McCabe stationarity tests
▪Engle-Granger and Johann tests for cointegration
▪Variance ratio test for random walks
Testing of NASDAQ Composite Index price ries and returns (left) for autocorrelation and partial autocorrelation. The raw return ries does not have any correlation (top right), and correlation is prent in the squared return (bottom right).
State-Space Modeling and Parameter Estimation
Econometrics Toolbox includes functions for creating state-space models and tools for estimating parameters bad on the and other model types.
State-Space Modeling
Econometrics Toolbox provides functions for modeling time-invariant or time-varying, linear, Gaussian
state-space models. You can create state-space models with known parameter values, perform Monte-Carlo simulations, and generate forecasts from the model. For models with unknown parameter values, you can perform parameter estimation from full data ts or data ts with missing data using the Kalman filter.
Implementing the Diebold Li model, including estimating the parameters of the model with a Kalman filter using the ssm model.
Parameter Estimation
学历证书编号With Econometrics Toolbox, you can perform parameter estimation (also known as model calibration) of univariate ARMAX/GARCH composite models, multivariate VAR/VARX models, multivariate VEC models, and
state-space models.
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Estimating state-space model parameters using a Kalman filter.
