LOAD_MODEL_DATA function

Load time series data into a BIMETS model

This function verifies the input time series list and copies the data into a BIMETS model object. Provided time series must be BIMETS compliant, as defined in is.bimets

LOAD_MODEL_DATA(model=NULL, modelData=NULL, quietly=FALSE, ...)

Arguments

• model: The BIMETS model object (see LOAD_MODEL).
• modelData: The input time series list containing endogenous and exogenous data (see example).
• quietly: If TRUE, information messages will be suppressed.
• ...: Backward compatibility.

Returns

This function add two new named element, i.e. modelData and frequency, into the output model object.

The new modelData element is a named list that contains all the input time series. Each element name of this list is set equal to the name of the endogenous or exogenous variable the time series data refer to.

The new frequency element is an integer that represent the frequency of the time series model data.

See Also

MDL

LOAD_MODEL

ESTIMATE

SIMULATE

STOCHSIMULATE

MULTMATRIX

RENORM

TIMESERIES

BIMETS indexing

BIMETS configuration

Examples

#define model data
myModelData<-list(
  cn
  =TIMESERIES(39.8,41.9,45,49.2,50.6,52.6,55.1,56.2,57.3,57.8,55,50.9,
              45.6,46.5,48.7,51.3,57.7,58.7,57.5,61.6,65,69.7,
              START=c(1920,1),FREQ=1),
  g
  =TIMESERIES(4.6,6.6,6.1,5.7,6.6,6.5,6.6,7.6,7.9,8.1,9.4,10.7,10.2,9.3,10,
              10.5,10.3,11,13,14.4,15.4,22.3,
              START=c(1920,1),FREQ=1),
  i
  =TIMESERIES(2.7,-.2,1.9,5.2,3,5.1,5.6,4.2,3,5.1,1,-3.4,-6.2,-5.1,-3,-1.3,
              2.1,2,-1.9,1.3,3.3,4.9,
              START=c(1920,1),FREQ=1),
  k
  =TIMESERIES(182.8,182.6,184.5,189.7,192.7,197.8,203.4,207.6,210.6,215.7,
              216.7,213.3,207.1,202,199,197.7,199.8,201.8,199.9,
              201.2,204.5,209.4,
              START=c(1920,1),FREQ=1),
  p
  =TIMESERIES(12.7,12.4,16.9,18.4,19.4,20.1,19.6,19.8,21.1,21.7,15.6,11.4,
              7,11.2,12.3,14,17.6,17.3,15.3,19,21.1,23.5,
              START=c(1920,1),FREQ=1),
  w1
  =TIMESERIES(28.8,25.5,29.3,34.1,33.9,35.4,37.4,37.9,39.2,41.3,37.9,34.5,
              29,28.5,30.6,33.2,36.8,41,38.2,41.6,45,53.3,
              START=c(1920,1),FREQ=1),
  y
  =TIMESERIES(43.7,40.6,49.1,55.4,56.4,58.7,60.3,61.3,64,67,57.7,50.7,41.3,
              45.3,48.9,53.3,61.8,65,61.2,68.4,74.1,85.3,
              START=c(1920,1),FREQ=1),
  t
  =TIMESERIES(3.4,7.7,3.9,4.7,3.8,5.5,7,6.7,4.2,4,7.7,7.5,8.3,5.4,6.8,7.2,
              8.3,6.7,7.4,8.9,9.6,11.6,
              START=c(1920,1),FREQ=1),
  time 
  =TIMESERIES(NA,-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,
              START=c(1920,1),FREQ=1),
  w2
  =TIMESERIES(2.2,2.7,2.9,2.9,3.1,3.2,3.3,3.6,3.7,4,4.2,4.8,5.3,5.6,6,6.1,
              7.4,6.7,7.7,7.8,8,8.5,
              START=c(1920,1),FREQ=1)
)

#define model
myModelDefinition<-
"MODEL

COMMENT> Modified Klein Model 1 of the U.S. Economy with PDL,
COMMENT> autocorrelation on errors, restrictions and conditional evaluations

COMMENT> Consumption
BEHAVIORAL> cn
TSRANGE 1925 1 1941 1
EQ> cn = a1 + a2*p + a3*TSLAG(p,1) + a4*(w1+w2)
COEFF> a1 a2 a3 a4
ERROR> AUTO(2)

COMMENT> Investment
BEHAVIORAL> i
TSRANGE 1923 1 1941 1
EQ> i = b1 + b2*p + b3*TSLAG(p,1) + b4*TSLAG(k,1)
COEFF> b1 b2 b3 b4
RESTRICT> b2 + b3 = 1

COMMENT> Demand for Labor
BEHAVIORAL> w1
TSRANGE 1925 1 1941 1
EQ> w1 = c1 + c2*(y+t-w2) + c3*TSLAG(y+t-w2,1)+c4*time
COEFF> c1 c2 c3 c4
PDL> c3 1 3

COMMENT> Gross National Product
IDENTITY> y
EQ> y = cn + i + g - t
COMMENT> Profits
IDENTITY> p
EQ> p = y - (w1+w2)

COMMENT> Capital Stock with switches
IDENTITY> k
EQ> k = TSLAG(k,1) + i
IF> i > 0
IDENTITY> k
EQ> k = TSLAG(k,1)
IF> i <= 0

END"

#load model 
myModel<-LOAD_MODEL(modelText=myModelDefinition)

#load data into the model
myModel<-LOAD_MODEL_DATA(myModel,myModelData,showWarnings = TRUE)
#Load model data "myModelData" into model "myModelDefinition"...
#CHECK_MODEL_DATA(): warning, there are missing values in series "time".
#...LOAD MODEL DATA OK

#retrieve data from model object

myModel$modelData$cn
#Time Series:
#Start = 1920 
#End = 1941 
#Frequency = 1 
# [1] 39.8 41.9 45.0 49.2 50.6 52.6 55.1 56.2 57.3 
#57.8 55.0 50.9 45.6 46.5 48.7 51.3 57.7 58.7 57.5 61.6
#[21] 65.0 69.7

myModel$modelData$w1
#Time Series:
#Start = 1920 
#End = 1941 
#Frequency = 1 
# [1] 28.8 25.5 29.3 34.1 33.9 35.4 37.4 37.9 39.2 
#41.3 37.9 34.5 29.0 28.5 30.6 33.2 36.8 41.0 38.2 41.6
#[21] 45.0 53.3

myModel$modelData$i
#Time Series:
#Start = 1920 
#End = 1941 
#Frequency = 1 
# [1]  2.7 -0.2  1.9  5.2  3.0  5.1  5.6  4.2  3.0  5.1  
#1.0 -3.4 -6.2 -5.1 -3.0 -1.3  2.1  2.0 -1.9  1.3
#[21]  3.3  4.9

myModel$modelData$time
#Time Series:
#Start = 1920 
#End = 1941 
#Frequency = 1 
# [1]  NA -10  -9  -8  -7  -6  -5  -4  -3  -2  -1   
#0   1   2   3   4   5   6   7   8   9  10