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gets (version 0.38)

General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Description

Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) .

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Version

Install

install.packages('gets')

Monthly Downloads

1,015

Version

0.38

License

GPL (>= 2)

Homepage

https://CRAN.R-project.org/package=gets

Maintainer

Genaro Sucarrat

Last Published

July 14th, 2024

Functions in gets (0.38)

Convert an object to class 'arx'

Conditional Value-at-Risk (VaR) and Expected Shortfall (ES)

Convert to 'lm' object

Extraction functions for 'logitx' objects

Diagnostics tests

Extraction functions for 'isat' objects

Indicator Saturation

Bias-correction of coefficients following general-to-specific model selection

Estimate an AR-X model with log-ARCH-X errors

Exporting results to EViews and STATA

Extracting Indicator Saturation Breakdates

Methods and extraction functions for 'larch' objects

General-to-Specific (GETS) Modelling of a heterogeneous log-ARCH-X model

General-to-Specific (GETS) Modelling 'lm' objects

Variance of the coefficient path

Consistency and Efficiency Correction for Impulse Indicator Saturation

Recursive estimation

Block-based General-to-Specific (GETS) modelling

General-to-Specific (GETS) Modelling

Create the regressors of the mean equation

General-to-Specific (GETS) and Indicator Saturation (ISAT) Modelling

outlierscaletest

Sum and Sup Scaling Outlier Tests

Daily Standard and Poor's 500 index data

Variance of the Impulse Indicator Saturation Gauge

General-to-Specific (GETS) Modelling 'isat' objects

Quarterly Norwegian year-on-year CPI inflation

Extraction functions for 'arx' objects

General-to-Specific (GETS) Modelling of an AR-X model (the mean specification) with log-ARCH-X errors (the log-variance specification).

Generalised Method of Moment (GMM) estimation of linear models

IIS Consistency Correction

Jiao-Pretis-Schwarz Outlier Distortion Test

IIS Efficiency Correction

Extraction functions for 'gets' objects

Computes the Average Value of an Information Criterion

Simulate from a dynamic logit-x model

Repeated Impulse Indicator Saturation

Simulate from a Multivariate Normal Distribution

Indicator Saturation Test

Variance forecasting with 'larch' models

distorttestboot

Bootstrapped Jiao-Pretis-Schwarz Outlier Distortion Test

Generate LaTeX code of an estimation result

General-to-Specific (GETS) Modelling of objects of class 'logitx'

Jiao and Pretis Outlier Proportion and Count Tests

General-to-Specific (GETS) modelling function

Extraction functions for 'arx', 'gets' and 'isat' objects

regressorsVariance

Create regressors for a log-variance model

Equally Weighted Moving Average (EqWMA) of the pth. exponentiated values

Estimate a heterogeneous log-ARCH-X model

Estimation of a log-variance model

Hoover and Perez (1999) data

Make Indicator Matrices (Impulses, Steps, Trends)

Estimation of a logit model

periodicdummies

Make matrix of periodicity (e.g. seasonal) dummies

Estimate an autoregressive logit model with covariates

Forecasting with 'arx' models