dynamac (version 0.1.12)

dynardl: Estimate and simulate ARDL model

Description

Estimate autoregressive distributed lag models and simulate interesting values (if desired)

Usage

dynardl(
  formula,
  data = list(),
  lags = list(),
  diffs = c(),
  lagdiffs = list(),
  levels = c(),
  ec = FALSE,
  trend = FALSE,
  constant = TRUE,
  modelout = FALSE,
  noLDV = FALSE,
  simulate = FALSE,
  shockvar = list(),
  shockval = sd(data[[shockvar]], na.rm = T),
  time = 10,
  qoi = "mean",
  forceset = NULL,
  range = 20,
  burnin = 20,
  sims = 1000,
  sig = 95,
  expectedval = FALSE,
  fullsims = FALSE
)

Value

dynardl should always return an estimated model. It may or may not be simulated, according to the user. But the relevant regression output, model residuals (which can be tested for autocorrelation), and simulated response (if created) are stored in a list if the model is assigned to an object

Arguments

formula

a symbolic description of the model to be estimated. ARDL models are estimated using linear regression

data

an optional data frame or list containing the the variables in the model

lags

a list of variables and their corresponding lags to be estimated

diffs

a vector of variables to be differenced. Only first differences are supported

lagdiffs

a list of variables to be included in lagged differences

levels

a vector of variables to be included in levels

ec

estimate model in error-correction form, (i.e., y appears in first-differences). By default, ec is set to FALSE, meaning y will appear in levels.

trend

include a linear time trend. The default is FALSE

constant

include a constant. The default is TRUE

modelout

print the regression estimates in the console

noLDV

do not add a lagged dependent variable (LDV) to ARDL models when omitted in formula (special thanks to Hannes Datta). This is not recommended

simulate

simulate the reponse. Otherwise, just the regression model will be estimated. If simulate = FALSE, options shockvar, shockval, time, qoi, forceset, range, burnin, sims, sig, expectedval, and fullsims are ignored. The default is FALSE so that users can build models without needing to simulate the results each time. When simulate = TRUE, users are highly encouraged to set a seed before simulation, as with any stochastic exercise

shockvar

the variable to be shocked in the counterfactual simulation. There is no default

shockval

the amount by which the shockvar should be shocked. The default is one standard deviation of the shocked variable

time

the time period in the simulation for the variable to be shocked

qoi

summarize the response of the dependent variable with the mean or the median. Although the default is mean, if there is underlying skew in the distribution, it might be better summarized by median

forceset

by default, in the simulations, variables in levels will be set to their means; variables in differences will be set to 0. Alternatively, users can set any variable in the model to a different value using a list in forceset. These values can be any user-defined value, including means, medians, percentiles, or other values of interest

range

the range of the simulation to be conducted

burnin

the number of time periods to disregard before recording the values. These do not include the range; in other words, they take place before the range specified above. Users can increase the number of burnin periods, but probably should not decrease them. The default is 20

sims

the number of simulations to use in creating the quantities of interest (the response of the dependent variable). The default is 1000

sig

the significance level (1 - p) that the user wants for the simulations. The default level is 95% significance (sig = 95)

expectedval

if this is TRUE, the simulation will record the expected values of across the sims by averaging errors. The default is FALSE, since expected values do not account for stochastic error present in the model itself

fullsims

whether all of the raw simulations should be stored in the model object. These are required for some of the more advanced plotting functions, especially those that use the simulations to derive confidence intervals about the size of the period-over-period differences. The default is FALSE

Author

Soren Jordan and Andrew Q. Philips

Details

Estimate an auto-regressive distributed lag model. Moreover, enable a graphical interpretation of the results (through dynardl.simulation.plot) by simulating the response of the dependent variable to shocks in one of the regressors, and enable the Pesaran, Shin, and Smith (2001) test for cointegration for error-correction models (through pssbounds)

Examples

# Using the inequality data from dynamac
ardl.model <- dynardl(concern ~ incshare10 + urate, data = ineq, 
       lags = list("concern" = 1, "incshare10" = 1),
       diffs = c("incshare10", "urate"), 
       ec = TRUE, simulate = FALSE)
summary(ardl.model)

# Adding a lagged difference of the dependent variable
ardl.model.2 <- dynardl(concern ~ incshare10 + urate, data = ineq, 
       lags = list("concern" = 1, "incshare10" = 1),
       diffs = c("incshare10", "urate"), 
       lagdiffs = list("concern" = 1),
       ec = TRUE, simulate = FALSE)
summary(ardl.model.2)

# Does not work: levels and diffs must appear as a vector
if (FALSE) {
ardl.model.3 <- dynardl(concern ~ incshare10 + urate, data = ineq, 
       lags = list("concern" = 1, "incshare10" = 1),
       levels = list("urate" = 1),
       diffs = list("incshare10" = 1, "urate" = 1), 
       lagdiffs = list("concern" = 1),
       ec = TRUE, simulate = FALSE)
}

ardl.model.3 <- dynardl(concern ~ incshare10 + urate, data = ineq, 
       lags = list("concern" = 1, "incshare10" = 1),
       levels = c("urate"),
       diffs = c("incshare10", "urate"), 
       lagdiffs = list("concern" = 1),
       ec = TRUE, simulate = FALSE)