Model and Canvass Economic and Financial Systems Employing Statistical methods

Cardinal Prominent Attributes of Econometrics Toolbox

Econometrics Toolbox renders mathematical function for prototyping economic data. Developers can choose and fine-tune economic models for forecasting and simulation. Time series capacities constitute uni variate GARCH/ARMAX complex models with respective multivariate VARMAX models , GARCH versions and co integration analysis. The toolbox renders Monte Carlo methods for modeling systems of nonlinear and linear stochastic differential coefficient equations and a diversity of nosology for model selection, admitting  unit root, stationary tests and hypothesis.

Modeling Time Series

Econometrics Toolbox alleviates the multistage procedure of distinguishing and examining multivariate and uni variate models of the  time series  for econometric  and financial data. The toolbox affirms the work flow of the full model growth and analysis:

Identification of model

Analysis of data and preprocessing

Estimation of parameter

Simulation and Forecasting

Uni variate Time-Series Modeling

Time-series modeling capabilities in Econometrics Toolbox are projected to captivate prominent attribute ordinarily linked with financial and econometric data, comprising data with  leverage effects, volatility clustering and fat tails,

Endorsed counter factual mean models constitute:

Auto regressive moving average with exogenous inputs (ARMAX)

Auto regressive moving average (ARMA)

Endorsed counter factual  variance models constitute:

Exponential GARCH (EGARCH).

Glosten-Jagannathan-Runkle (GJR).

Generalized auto regressive conditional hetreroscedasticity (GARCH).

Cardinal Prominent Attributes of Econometrics Toolbox

Model Recognition and Analysis

Co integration Modeling

Time-Series Modeling

Forecasting

Monte Carlo Simulation

Parameter Estimation

Volatility Modelings

Econometrics Toolbox corroborates multivariate time-series analysis by extending capabilities for uni variate models. Supported models constitute:

Vector moving average (VMA)

Vector auto regressive (VAR)

Vector auto regressive moving average with exogenous inputs (VARMAX)

Vector auto regressive moving average (VARMA)

Vector error-correction (VEC)

Structural VARMAX (SVARMAX)

Model Identification and Analysis

With Econometrics Toolbox, developers can choose and test models by assigning a model structure, distinguishing the model order, approximating parameters and measuring residuals. A variety of pre- and post-approximation nosology and tests affirm these analysis, establishing:

Akaike and Bayesian data standards for model order choice.

Likelihood ratio, Wald, and Lagrange multiplier tests for model stipulation

Cross-correlation, partial auto correlation and sample auto correlation functions.

For the presence of ARCH/GARCH effects, Engle's test.

Phillips-Perron and Dickey-Fuller unit root tests.

Ljung-Box Q test for auto correlation

Variance ratio test for random walks

Johansen tests and Engle-Granger test for cointegration

Leybourne-McCabe and KPSS stationarity tests

Monte Carlo Simulation

Econometrics Toolbox permits developers execute Monte Carlo simulations to bring forth forecast distributions of both multiple  and single time-series models, comprising multivariate VARMAX models and  univariate ARMAX/GARCH composite models.

Monte Carlo Simulation of Stochastic Differential Equations

The toolbox in addition, permits developers simulate a variety of popular stochastic differential equations (SDEs), interfaces for custom-making the own SDE simulation methods and models.

Simulation of common stochastic differential equations with predetermined model classes.

Simulation of any nonlinear or linear stochastic differential equation with predefined interfaces.

Forecasting

Developers can forecast market trends to make investing, budgeting, policy decisions and planning. Financial Toolbox renders the cornerstone for figuring out with financial time-series data, executing regression and parameter approximation with or without neglecting data and simulation of various assumptions to approximate risk. Econometrics Toolbox extends this cornerstone with brought forward capabilities that account for non consistent variance throughout time.

Cointegration Modeling

Econometrics Toolbox renders Johansen  and Engle-Granger methods for cointegration modeling and testing. The Engel-Granger method tests for item-by-item cointegrating relationships and approximates their parameters. Johansen methods tests for multiple cointegrating relationships, and approximates parameters in representing vector error-correction (VEC) models. In addition, Johansen methods tests linear confinements on both error-correction accelerates and the space of cointegrating vectors and approximates confined model parameters.

Volatility Modeling

Econometrics Toolbox has a all over set of tools for constructing on time-varying volatility models. The toolbox corroborates respective variants of univariate GARCH models, comprising standard ARCH/GARCH models, as well as asymmetric EGARCH and GJR models projected to capture leverage effects in asset returns. The toolbox corroborates the simulation of stochastic volatility models, comprising the Heston model.

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