esti: ARIMA Model Estimation

In wqmeeker/RTseries: Interactive Time Series Analysis Tools

ARIMA Model Estimation

Description

Performas estimation, diagnostic checking, and forecasting for a specified ARIMA model and time series data object.

Usage

esti(
  data.tsd,
  gamma = 1,
  m = 0,
  model,
  gof.lag = 10,
  lag.max = 38,
  number.forecasts = 24,
  pred.level = 0.95,
  xreg.in,
  y.range,
  x.range,
  d.trend = FALSE,
  print.table = FALSE,
  ps1 = NULL,
  ps2 = NULL,
  ...
)

Arguments

data.tsd	Input time series data object (output from the tsd function).
gamma	Optional; the Box-Cox transformation power parameter. The default value is gamma=1 (no transformation). If a log transformation is needed, use gamma=0.
m	Optional constant to added to the response variable before the data are transformed. The default value is m=0.
model	A list specifying an ARIMA model. See functions model.pdq for a simple method for specifying the model input.
gof.lag	Number of lags to be use to compute the Ljung-Box statistic (default=10).
lag.max	The number of lags at which to estimate residual autocorrelations (default is lag.max=38).
number.forecasts	How far into the future to forecast (default is number.forecast=24).
pred.level	Prediction confidence level (default is pred.level =0 .95).
xreg.in	An x matrix for dynamic regression (see lead.matrix).
y.range	Range of the y axis for the predictions
x.range	Range of the x axis for the predictions
d.trend	Indicates whether a model should have a deterministic trend term or not (default is d.trend = FALSE).
print.table	If print.table = TRUE, a table of the forecasts and prediction intervals will be provided (default is print.table = FALSE).
ps1	if provided, the postscript file name for the first page of graphical output
ps2	if provided, the postscript file name for the second page of graphical output
...	allows sending down extra arguments to the arima function (such as optimization options or fixed values of parameters).

Value

Invisibly returns the output from the R arima command used to do the estimation.

Examples

# AR(1) model for the sunspot data
esti(spot.tsd, model=model.pdq(p=3))

# Airline model for the international airline passengers data
Passengers.ts <- ts(Passengers, freq=12, start=1949)
Passengers.tsd <- tsd(Passengers.ts, data.title='International Airline Passengers',
                      time.units='Year', response.units='Thousands of Passengers')
airline.model <- model.pdq(period=12, d=1, D=1, q=1, Q=1)
esti(Passengers.tsd, gamma = 0, model=airline.model)
esti(Passengers.tsd, gamma = 0, m = 2, model=airline.model)

wqmeeker/RTseries documentation built on Dec. 31, 2022, 10 a.m.