Package index • gets

All functions

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

arx(): Estimate an AR-X model with log-ARCH-X errors

as.arx(): Convert an object to class 'arx'

as.lm(): Convert to 'lm' object

biascorr(): Bias-correction of coefficients following general-to-specific model selection

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

coef(<arx>) fitted(<arx>) logLik(<arx>) model.matrix(<arx>) plot(<arx>) print(<arx>) residuals(<arx>) sigma(<arx>) summary(<arx>) vcov(<arx>): Extraction functions for 'arx' objects

coef(<gets>) fitted(<gets>) logLik(<gets>) plot(<gets>) predict(<gets>) print(<gets>) residuals(<gets>) sigma(<gets>) summary(<gets>) vcov(<gets>): Extraction functions for 'gets' objects

coef(<isat>) fitted(<isat>) logLik(<isat>) plot(<isat>) predict(<isat>) print(<isat>) residuals(<isat>) sigma(<isat>) summary(<isat>) vcov(<isat>): Extraction functions for 'isat' objects

coef(<logitx>) fitted(<logitx>) logLik(<logitx>) plot(<logitx>) print(<logitx>) summary(<logitx>) toLatex(<logitx>) vcov(<logitx>): Extraction functions for 'logitx' objects

diagnostics(): Diagnostics tests

distorttest(): Jiao-Pretis-Schwarz Outlier Distortion Test

distorttestboot() print(<distorttestboot>): Bootstrapped Jiao-Pretis-Schwarz Outlier Distortion Test

dropvar(): Drop variable

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

eviews() stata(): Exporting results to EViews and STATA

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

gets(): General-to-Specific (GETS) Modelling

gets(<isat>): General-to-Specific (GETS) Modelling 'isat' objects

gets(<lm>): General-to-Specific (GETS) Modelling 'lm' objects

gets(<logitx>): General-to-Specific (GETS) Modelling of objects of class 'logitx'

getsFun(): General-to-Specific (GETS) modelling function

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

gmm(): Generalised Method of Moment (GMM) estimation of linear models

hpdata: Hoover and Perez (1999) data

iim() sim() tim(): Make Indicator Matrices (Impulses, Steps, Trends)

infldata: Quarterly Norwegian year-on-year CPI inflation

infocrit() info.criterion(): Computes the Average Value of an Information Criterion

isat(): Indicator Saturation

isatdates(): Extracting Indicator Saturation Breakdates

isatloop(): Repeated Impulse Indicator Saturation

isattest(): Indicator Saturation Test

isatvar(): Variance of the coefficient path

isatvarcorrect(): Consistency and Efficiency Correction for Impulse Indicator Saturation

isvarcor(): IIS Consistency Correction

isvareffcor(): IIS Efficiency Correction

logit(): Estimation of a logit model

logitx() dlogitx(): Estimate an autoregressive logit model with covariates

logitxSim() dlogitxSim(): Simulate from a dynamic logit-x model

mvrnormsim(): Simulate from a Multivariate Normal Distribution

ols(): OLS estimation

outlierscaletest(): Sum and Sup Scaling Outlier Tests

outliertest(): Jiao and Pretis Outlier Proportion and Count Tests

paths() terminals() rsquared(): Extraction functions for 'arx', 'gets' and 'isat' objects

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

predict(<arx>): Forecasting with 'arx' models

printtex() toLatex(<arx>) toLatex(<gets>): Generate LaTeX code of an estimation result

recursive(): Recursive estimation

regressorsMean(): Create the regressors of the mean equation

regressorsVariance(): Create the regressors of the variance equation

so2data: UK SO2 Data

sp500data: Daily Standard and Poor's 500 index data

vargaugeiis(): Variance of the Impulse Indicator Saturation Gauge