Название: MATLAB Econometrics Toolbox User’s Guide (R2022b)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2022
Страниц: 4162
Язык: английский
Формат: pdf (true)
Размер: 27.3 MB

Model and analyze financial and economic systems using statistical methods. Econometrics Toolbox provides functions for analyzing and modeling time series data. It offers a wide range of visualizations and diagnostics for model selection, including tests for autocorrelation and heteroscedasticity, unit roots and stationarity, cointegration, causality, and structural change. You can estimate, simulate, and forecast economic systems using a variety of modeling frameworks. These frameworks include regression, ARIMA, state-space, GARCH, multivariate VAR and VEC, and switching models. The toolbox also provides Bayesian tools for developing time-varying models that learn from new data.

A probabilistic time series model is necessary for a wide variety of analysis goals, including regression inference, forecasting, and Monte Carlo simulation. When selecting a model, aim to find the most parsimonious model that adequately describes your data. A simple model is easier to estimate, forecast, and interpret.

Model selection is an iterative process. When goodness-of-fit checks suggest model assumptions are not satisfied—or the predictive performance of the model is not satisfactory—consider making model adjustments. Additional specification tests, model comparisons, and goodness-of-fit checks help guide this process.

Econometrics Toolbox includes a number of model objects used to represent a variety of discrete-time, time series models. The supported models are univariate or multivariate, linear or nonlinear, and standard or Bayesian. Model specification tests (see “Specification Testing”), economic theory, or your analysis goals can suggest a model, or set of models, for your data. After preprocessing your data, running specification tests, and selecting a set of candidate models, create the objects that best represent the models in MATLAB to proceed with your analysis.

Contents:

Getting Started
Data Preprocessing
Model Selection
Econometric Modeler
Time Series Regression Models
Bayesian Linear Regression
Conditional Mean Models
Conditional Variance Models
Multivariate Time Series Models
Structural Change Models
State-Space Models
Functions
Appendices