dynardl: Estimate and simulate ARDL model

In dynamac: Dynamic Simulation and Testing for Single-Equation ARDL Models

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
)

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

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)

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

Author(s)

Soren Jordan and Andrew Q. Philips

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
## Not run: 
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)

## End(Not run)

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)

dynamac documentation built on Nov. 18, 2022, 1:07 a.m.