LOAD_MODEL_DATA {bimets} | R Documentation |
This function verifies the input time series list and copies the data into a BIMETS model object. For each endogenous and exogenous variable of the model a related time series must be defined in the input list. Provided time series must be BIMETS compliant, as defined in is.bimets
LOAD_MODEL_DATA(model=NULL, modelData=NULL, showWarnings=FALSE, quietly=FALSE, ...)
model |
The BIMETS model object (see |
modelData |
The input time series list containing endogenous and exogenous data (see example). |
showWarnings |
If |
quietly |
If |
... |
Backward compatibility. |
This function add a new named element, i.e. modelData
, into the output model object.
The new modelData
element is a named R 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.
MDL
LOAD_MODEL
ESTIMATE
SIMULATE
MULTMATRIX
RENORM
TIMESERIES
BIMETS indexing
BIMETS configuration
#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