A B C D F G I K L M N O P R S T U V Z
| tsDyn-package | Getting started with the tsDyn package |
| AAR | Additive nonlinear autoregressive model |
| aar | Additive nonlinear autoregressive model |
| accuracy_stat | Forecasting accuracy measures. |
| accuracy_stat.default | Forecasting accuracy measures. |
| accuracy_stat.pred_roll | Forecasting accuracy measures. |
| addRegime | addRegime test |
| AIC.nlar | NLAR methods |
| ar_mean | Long-term mean of an AR(p) process |
| ar_mean.linear | Long-term mean of an AR(p) process |
| ar_mean.lstar | Long-term mean of an AR(p) process |
| ar_mean.setar | Long-term mean of an AR(p) process |
| as.data.frame.llar | Locally linear model |
| as.data.frame.rank.select | Selection of the cointegrating rank with Information criterion. |
| autopairs | Bivariate time series plots |
| autotriples | Trivariate time series plots |
| autotriples.rgl | Interactive trivariate time series plots |
| availableModels | Available models |
| barry | Time series of PPI used as example in Bierens and Martins (2010) |
| BBCTest | Test of unit root against SETAR alternative |
| BIC.nlar | NLAR methods |
| charac_root | Characteristic roots of the AR coefficients |
| charac_root.nlar | Characteristic roots of the AR coefficients |
| coef.nlar | NLAR methods |
| coefA | Extract cointegration parameters A, B and PI |
| coefA.ca.jo | Extract cointegration parameters A, B and PI |
| coefA.VECM | Extract cointegration parameters A, B and PI |
| coefB | Extract cointegration parameters A, B and PI |
| coefB.ca.jo | Extract cointegration parameters A, B and PI |
| coefB.VECM | Extract cointegration parameters A, B and PI |
| coefPI | Extract cointegration parameters A, B and PI |
| d2sigmoid | sigmoid functions |
| delta | delta test of conditional independence |
| delta.lin | delta test of linearity |
| delta.lin.test | delta test of linearity |
| delta.test | delta test of conditional independence |
| deviance.nlar | NLAR methods |
| dsigmoid | sigmoid functions |
| fevd.nlVar | Forecast Error Variance Decomposition |
| fitted | fitted method for objects of class nlVar, i.e. VAR and VECM models. |
| fitted.nlar | NLAR methods |
| fitted.nlVar | fitted method for objects of class nlVar, i.e. VAR and VECM models. |
| getTh | Extract threshold(s) coefficient |
| getTh.default | Extract threshold(s) coefficient |
| GIRF | Generalized Impulse response Function (GIRF) |
| GIRF.linear | Generalized Impulse response Function (GIRF) |
| GIRF.nlVar | Generalized Impulse response Function (GIRF) |
| GIRF.setar | Generalized Impulse response Function (GIRF) |
| IIPUs | US monthly industrial production from Hansen (1999) |
| irf.ar | Impulse response function |
| irf.linear | Impulse response function |
| irf.nlVar | Impulse response function |
| irf.setar | Impulse response function |
| irf.TVAR | Impulse response function |
| irf.TVECM | Impulse response function |
| irf.VAR | Impulse response function |
| irf.VECM | Impulse response function |
| isLinear | isLinear |
| KapShinTest | Test of unit root against SETAR alternative with |
| lags.select | Selection of the lag with Information criterion. |
| LINEAR | Linear AutoRegressive models |
| linear | Linear AutoRegressive models |
| linear.boot | Simulation and bootstrap of Threshold Autoregressive model (SETAR) |
| linear.sim | Simulation and bootstrap of Threshold Autoregressive model (SETAR) |
| lineVar | Multivariate linear models: VAR and VECM |
| llar | Locally linear model |
| llar.fitted | Locally linear model |
| llar.predict | Locally linear model |
| logLik.nlVar | Extract Log-Likelihood |
| logLik.VAR | Extract Log-Likelihood |
| logLik.VECM | Extract Log-Likelihood |
| LSTAR | Logistic Smooth Transition AutoRegressive model |
| lstar | Logistic Smooth Transition AutoRegressive model |
| m.unrate | Monthly US unemployment |
| MakeThSpec | Specification of the threshold search |
| makeThSpec | Specification of the threshold search |
| MAPE | Mean Absolute Percent Error |
| MAPE.default | Mean Absolute Percent Error |
| MAPE.nlar | NLAR methods |
| mse | Mean Square Error |
| mse.default | Mean Square Error |
| mse.nlar | NLAR methods |
| nlar-methods | NLAR methods |
| NNET | Neural Network nonlinear autoregressive model |
| nnetTs | Neural Network nonlinear autoregressive model |
| OlsTVAR | Multivariate Threshold Vector Autoregressive model |
| plot-methods | Plotting methods for SETAR and LSTAR subclasses |
| plot.aar | Additive nonlinear autoregressive model |
| plot.GIRF_df | Generalized Impulse response Function (GIRF) |
| plot.llar | Locally linear model |
| plot.lstar | Plotting methods for SETAR and LSTAR subclasses |
| plot.nlar | NLAR methods |
| plot.setar | Plotting methods for SETAR and LSTAR subclasses |
| plot_ECT | Plot the Error Correct Term (ECT) response |
| predict | Predict method for objects of class "nlar". |
| predict.nlar | Predict method for objects of class "nlar". |
| predict.TVAR | Predict method for objects of class "VAR", "VECM" or "TVAR" |
| predict.VAR | Predict method for objects of class "VAR", "VECM" or "TVAR" |
| predict.VECM | Predict method for objects of class "VAR", "VECM" or "TVAR" |
| predict_rolling | Rolling forecasts |
| predict_rolling.nlVar | Rolling forecasts |
| print.aar | Additive nonlinear autoregressive model |
| print.linear | Linear AutoRegressive models |
| print.llar | Locally linear model |
| print.rank.select | Selection of the cointegrating rank with Information criterion. |
| print.rank.test | Test of the cointegrating rank |
| print.summary.linear | Linear AutoRegressive models |
| rank.select | Selection of the cointegrating rank with Information criterion. |
| rank.test | Test of the cointegrating rank |
| regime | Extract a variable showing the regime |
| regime.default | Extract a variable showing the regime |
| regime.lstar | Extract a variable showing the regime |
| resample_vec | Resampling schemes |
| residuals.nlar | NLAR methods |
| resVar | Residual variance |
| selectLSTAR | Automatic selection of model hyper-parameters |
| selectNNET | Automatic selection of model hyper-parameters |
| selectSETAR | Automatic selection of SETAR hyper-parameters |
| selectSetar | Automatic selection of SETAR hyper-parameters |
| selectsetar | Automatic selection of SETAR hyper-parameters |
| SETAR | Self Threshold Autoregressive model |
| setar | Self Threshold Autoregressive model |
| setar.boot | Simulation and bootstrap of Threshold Autoregressive model (SETAR) |
| setar.sim | Simulation and bootstrap of Threshold Autoregressive model (SETAR) |
| setarTest | Test of linearity against threshold (SETAR) |
| setartest | Test of linearity against threshold (SETAR) |
| setarTest_IIPUs_results | Results from the setarTest, applied on Hansen (1999) data |
| sigmoid | sigmoid functions |
| STAR | STAR model |
| star | STAR model |
| summary.aar | Additive nonlinear autoregressive model |
| summary.linear | Linear AutoRegressive models |
| summary.nlar | NLAR methods |
| summary.rank.select | Selection of the cointegrating rank with Information criterion. |
| summary.rank.test | Test of the cointegrating rank |
| summary.setar | Self Threshold Autoregressive model |
| toLatex.nlar | NLAR methods |
| toLatex.setar | Latex representation of fitted setar models |
| tsDyn | Getting started with the tsDyn package |
| TVAR | Multivariate Threshold Vector Autoregressive model |
| TVAR.boot | Simulation of a multivariate Threshold Autoregressive model (TVAR) |
| TVAR.LRtest | Test of linearity |
| TVAR.sim | Simulation of a multivariate Threshold Autoregressive model (TVAR) |
| TVECM | Threshold Vector Error Correction model (VECM) |
| TVECM.boot | Simulation and bootstrap a VECM or bivariate TVECM |
| TVECM.HStest | Test of linear cointegration vs threshold cointegration |
| TVECM.SeoTest | No cointegration vs threshold cointegration test |
| TVECM.sim | Simulation and bootstrap a VECM or bivariate TVECM |
| UsUnemp | US unemployment series used in Caner and Hansen (2001) |
| VAR.boot | Simulate or bootstrap a VAR model |
| VAR.sim | Simulate or bootstrap a VAR model |
| VARrep | VAR representation |
| VARrep.VAR | VAR representation |
| VARrep.VECM | VAR representation |
| VECM | Estimation of Vector error correction model (VECM) |
| VECM.boot | Simulation and bootstrap a VECM or bivariate TVECM |
| VECM.sim | Simulation and bootstrap a VECM or bivariate TVECM |
| VECM_symbolic | Virtual VECM model |
| zeroyld | zeroyld time series |
| zeroyldMeta | zeroyld time series |