data_trans: Transform data for estimation

In opendoor-labs/MarkovSwitchingDCF: Markov Switching Dynamic Common Factor Model using the Kim filter (Hamilton + Kalman filters)

Description

Transform data for estimation

Usage

data_trans(y, model = NULL, log.vars = NULL, diff.vars = NULL,
  freq = NULL, detect.diff = F, detect.growth = F, level = 0.01,
  diff.lag = 1, panelID = NULL, timeID = NULL)

Arguments

y	Multivariate time series of data values. May also be a data frame containing a date column
model	Estimated model from ms_dcf_estim
log.vars	Character vector of variables to be logged
diff.vars	Character vector of unit root variables to be differenced
freq	Seasonality of the data
detect.diff	Logical, detect which variables to difference
detect.growth	Logical, detect which variables to log
level	Significance level of statistical tests (0.01, 0.05, 0.1)
diff.lag	Integer, number of lags to use for differencing
panelID	Column name that identifies the cross section of the data
timeID	Column name that identifies the date
detect.formula	Logical, detect lag length of the dynamic common factor to include in each observation equation using the cross correlation function up to a max of 3

Value

list containing differenced data (yy_d) and standardized data (yy_s)

Author(s)

Alex Hubbard ([e-mail])

Examples

data_trans(y = y, panelID = "panel", timeID = "date")

opendoor-labs/MarkovSwitchingDCF documentation built on Jan. 8, 2020, 12:24 p.m.