Function reference • fitAutoReg

Simulation Functions

SimVAR(): Simulate Data from a Vector Autoregressive (VAR) Model

SimVARExo(): Simulate Data from a Vector Autoregressive (VAR) Model with Exogenous Variables

Model Fitting Functions

FitMLVARDynr(): Fit Vector Autoregressive (VAR) Model using dynr on Each of the Data Matrix in a List

FitMLVARMplus(): Fit Multilevel Vector Autoregressive Model using Mplus

FitVARDynr(): Fit Vector Autoregressive Model using dynr

FitVARLasso(): Fit Vector Autoregressive (VAR) Model Parameters using Lasso Regularization

FitVARLassoSearch(): Fit Vector Autoregressive (VAR) Model Parameters using Lasso Regularization with Lambda Search

FitVARMplus(): Fit Vector Autoregressive Model using Mplus

FitVAROLS(): Fit Vector Autoregressive (VAR) Model Parameters using Ordinary Least Squares (OLS)

LambdaSeq(): Function to generate the sequence of lambdas

ModelVARP1Dynr(): Specify Vector Autoregressive (VAR(p = 1)) Model using dynr

ModelVARP2Dynr(): Specify Vector Autoregressive (VAR(p = 2)) Model using dynr

SearchVARLasso(): Compute AIC, BIC, and EBIC for Lasso Regularization

SelectVARLasso(): Select the Lasso Estimates from the Grid Search

Parametric Bootstrap Functions

BootCI(): Bootstrap Percentile Confidence Intervals

BootSE(): Bootstrap Standard Errors

PBootVARExoLasso(): Parametric Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Lasso Regularization

PBootVARExoOLS(): Parametric Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Ordinary Least Squares

PBootVARLasso(): Parametric Bootstrap for the Vector Autoregressive Model Using Lasso Regularization

PBootVAROLS(): Parametric Bootstrap for the Vector Autoregressive Model Using Ordinary Least Squares

Residual Bootstrap Functions

BootCI(): Bootstrap Percentile Confidence Intervals

BootSE(): Bootstrap Standard Errors

RBootVARExoLasso(): Residual Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Lasso Regularization

RBootVARExoOLS(): Residual Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Ordinary Least Squares

RBootVARLasso(): Residual Bootstrap for the Vector Autoregressive Model Using Lasso Regularization

RBootVAROLS(): Residual Bootstrap for the Vector Autoregressive Model Using Ordinary Least Squares

Utility Functions

OrigScale(): Return Standardized Estimates to the Original Scale

StdMat(): Standardize Matrix

YX(): Create Y and X Matrices

YXExo(): Create Y and X Matrices with Exogenous Variables

Methods Functions

coef(<dynr_model>): Auto and Cross Regression Coeffcients

plot(<dynr_model>): Plot Method for an Object of Class dynr_model

print(<dynr_model>): Print Method for an Object of Class dynr_model

Data

SimVAR(): Simulate Data from a Vector Autoregressive (VAR) Model

SimVARExo(): Simulate Data from a Vector Autoregressive (VAR) Model with Exogenous Variables

dat_ml_p1: Data from the Multilevel Vector Autoregressive Model (p = 1)

dat_ml_p2: Data from the Multilevel Vector Autoregressive Model (p = 2)

dat_p1: Data from the Vector Autoregressive Model (p = 1)

dat_p1_yx: Data from the Vector Autoregressive Model (Y) and Lagged Predictors (X) (p = 1)

dat_p2: Data from the Vector Autoregressive Model (p = 2)

dat_p2_exo: Data from the Vector Autoregressive Model with Exogenous Variables (p = 2)

dat_p2_exo_yx: Data from the Vector Autoregressive Model (Y) and Lagged Predictors and Exogenous Variables (X) (p = 2)

dat_p2_yx: Data from the Vector Autoregressive Model (Y) and Lagged Predictors (X) (p = 2)

All

SimVAR(): Simulate Data from a Vector Autoregressive (VAR) Model

SimVARExo(): Simulate Data from a Vector Autoregressive (VAR) Model with Exogenous Variables

YX(): Create Y and X Matrices

YXExo(): Create Y and X Matrices with Exogenous Variables

BootCI(): Bootstrap Percentile Confidence Intervals

BootSE(): Bootstrap Standard Errors

FitMLVARDynr(): Fit Vector Autoregressive (VAR) Model using dynr on Each of the Data Matrix in a List

FitMLVARMplus(): Fit Multilevel Vector Autoregressive Model using Mplus

FitVARDynr(): Fit Vector Autoregressive Model using dynr

FitVARLasso(): Fit Vector Autoregressive (VAR) Model Parameters using Lasso Regularization

FitVARLassoSearch(): Fit Vector Autoregressive (VAR) Model Parameters using Lasso Regularization with Lambda Search

FitVARMplus(): Fit Vector Autoregressive Model using Mplus

FitVAROLS(): Fit Vector Autoregressive (VAR) Model Parameters using Ordinary Least Squares (OLS)

LambdaSeq(): Function to generate the sequence of lambdas

ModelVARP1Dynr(): Specify Vector Autoregressive (VAR(p = 1)) Model using dynr

ModelVARP2Dynr(): Specify Vector Autoregressive (VAR(p = 2)) Model using dynr

OrigScale(): Return Standardized Estimates to the Original Scale

PBootVARExoLasso(): Parametric Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Lasso Regularization

PBootVARExoOLS(): Parametric Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Ordinary Least Squares

PBootVARLasso(): Parametric Bootstrap for the Vector Autoregressive Model Using Lasso Regularization

PBootVAROLS(): Parametric Bootstrap for the Vector Autoregressive Model Using Ordinary Least Squares

RBootVARExoLasso(): Residual Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Lasso Regularization

RBootVARExoOLS(): Residual Bootstrap for the Vector Autoregressive Model with Exogenous Variables Using Ordinary Least Squares

RBootVARLasso(): Residual Bootstrap for the Vector Autoregressive Model Using Lasso Regularization

RBootVAROLS(): Residual Bootstrap for the Vector Autoregressive Model Using Ordinary Least Squares

SearchVARLasso(): Compute AIC, BIC, and EBIC for Lasso Regularization

SelectVARLasso(): Select the Lasso Estimates from the Grid Search

StdMat(): Standardize Matrix

dat_ml_p1: Data from the Multilevel Vector Autoregressive Model (p = 1)

dat_ml_p2: Data from the Multilevel Vector Autoregressive Model (p = 2)

dat_p1: Data from the Vector Autoregressive Model (p = 1)

dat_p1_yx: Data from the Vector Autoregressive Model (Y) and Lagged Predictors (X) (p = 1)

dat_p2: Data from the Vector Autoregressive Model (p = 2)

dat_p2_exo: Data from the Vector Autoregressive Model with Exogenous Variables (p = 2)

dat_p2_exo_yx: Data from the Vector Autoregressive Model (Y) and Lagged Predictors and Exogenous Variables (X) (p = 2)

dat_p2_yx: Data from the Vector Autoregressive Model (Y) and Lagged Predictors (X) (p = 2)