bimets - Time Series And Econometric Modeling In R

bimets is an R package developed with the aim of easing time series analysis and building up a framework that facilitates the definition, estimation and simulation of simultaneous equation models.

bimets does not depend on compilers or third-party software so it can be freely downloaded and installed on Linux, MS Windows(R) and Mac OSX(R), without any further requirements.

If you have general questions about using bimets, or for bug reports, please write to the mantainer: Andrea.Luciani@bancaditalia.it

Features

TIME SERIES:

Example:

#create ts
myTS=TIMESERIES((1:100),START=c(2000,1),FREQ='D');
 
myTS[1:3];                    #get first three obs.
myTS['2000-01-12'];           #get Jan 12, 2000 data
myTS['2000-02-03/2000-03-04'] #get Feb 3 up to Mar 4
myTS[[2000,14]];              #get year 2000 period 14
myTS[[2032,1]];               #get year 2032 period 1 (out of range)
    
myTS['2000-01-15']=NA;        #assign to Jan 15, 2000
myTS[[2000,3]]=NA;            #assign to Jan 3, 2000
myTS[[2000,42]] = NA          #assign to Feb 11, 2000
myTS[[2000,100]]= c(-1,-2,-3);#assign array starting from 2000                  
                              #period 100 (i.e. extend series)
                              
#aggregation/disaggregation
myMonthlyTS=TIMESERIES(1:100,START=c(2000,1),FREQ='M');
myAnnualTS=ANNUAL(myMonthlyTS,'AVE');
myDailyTS=DAILY(myMonthlyTS,'INTERP_CENTER');

#manipulation
myTS1=TIMESERIES(1:100,START=c(2000,1),FREQ='M');
myTS2=TIMESERIES(-(1:100),START=c(2005,1),FREQ='M');
myExtendedTS=TSEXTEND(myTS1,UPTO = c(2020,4),EXTMODE = 'QUADRATIC');
myMergedTS=TSMERGE(myExtendedTS,myTS2,fun = 'SUM');
myProjectedTS=TSPROJECT(myMergedTS,TSRANGE = c(2004,2,2006,4));
myLagTS=TSLAG(myProjectedTS,2);
myDeltaPTS=TSDELTAP(myLagTS,2);
myMovAveTS=MOVAVG(myDeltaPTS,5);
TABIT(myMovAveTS,myTS1);

#     DATE, PER, myMovAveTS     , myTS1          
# 
# Jan 2000, 1  ,                ,  1             
# Feb 2000, 2  ,                ,  2             
# Mar 2000, 3  ,                ,  3             
# ...
# Sep 2004, 9  ,                ,  57            
# Oct 2004, 10 ,  3.849002      ,  58            
# Nov 2004, 11 ,  3.776275      ,  59            
# Dec 2004, 12 ,  3.706247      ,  60            
# Jan 2005, 1  ,  3.638771      ,  61            
# Feb 2005, 2  ,  3.573709      ,  62            
# Mar 2005, 3  ,  3.171951      ,  63            
# Apr 2005, 4  ,  2.444678      ,  64            
# May 2005, 5  ,  1.730393      ,  65            
# Jun 2005, 6  ,  1.028638      ,  66            
# Jul 2005, 7  ,  0.3389831     ,  67            
# Aug 2005, 8  ,  0             ,  68            
# Sep 2005, 9  ,  0             ,  69            
# Oct 2005, 10 ,  0             ,  70            
# ...
# Mar 2008, 3  ,                ,  99            
# Apr 2008, 4  ,                ,  100 

MODELING:

bimets econometric modeling capabilities comprehend:

A Klein’s model example, having restrictions, error autocorrelation and conditional evaluations, follows:


#define the Klein model
klein1.txt="MODEL

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

COMMENT> Consumption with autocorrelation on errors
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 with restrictions
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 with PDL
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 2

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

END"

#load the model
kleinModel=LOAD_MODEL(modelText = klein1.txt);

# Loading model: "klein1.txt"...
# Analyzing behaviorals...
# Analyzing identities...
# Optimizing...
# Loaded model "klein1.txt":
#     3 behaviorals
#     3 identities
#    12 coefficients
# ...LOAD MODEL OK

kleinModel$behaviorals$cn
# $eq
# [1] "cn=a1+a2*p+a3*TSLAG(p,1)+a4*(w1+w2)"
# 
# $eqCoefficientsNames
# [1] "a1" "a2" "a3" "a4"
# 
# $eqComponentsNames
# [1] "cn" "p"  "w1" "w2"
# 
# $tsrange
# [1] 1925    1 1941    1
# 
# $eqRegressorsNames
# [1] "1"         "p"        "TSLAG(p,1)" "(w1+w2)" 
# 
# $eqSimExp
# expression(cn[2,]=cn_ADDFACTOR[2,]+cn_a1+cn_a2*p[2,]+cn_a3*...
# 
# ...and more

kleinModel$incidence_matrix

#    cn i w1 y p k
# cn  0 0  1 0 1 0
# i   0 0  0 0 1 0
# w1  0 0  0 1 0 0
# y   1 1  0 0 0 0
# p   0 0  1 1 0 0
# k   0 1  0 0 0 0

#define data
kleinModelData=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)
    );

kleinModel=LOAD_MODEL_DATA(kleinModel,kleinModelData);
# Load model data "kleinModelData" into model "klein1.txt"...
# ...LOAD MODEL DATA OK
 
 
kleinModel=ESTIMATE(kleinModel)
#.CHECK_MODEL_DATA(): warning, there are undefined values in time series "time".
#
#Estimate the Model klein1.txt:
#the number of behavioral equations to be estimated is 3.
#The total number of coefficients is 13.
#
#_________________________________________
#
#BEHAVIORAL EQUATION: cn
#Estimation Technique: OLS
#Autoregression of Order  2  (Cochrane-Orcutt procedure)
#
#Convergence was reached in  9  /  20  iterations.
#
#
#cn                  =   19.01352    
#                        T-stat. 13.1876     ***
#
#                    +   0.3442816   p
#                        T-stat. 3.841051    **
#
#                    +   0.03443117  TSLAG(p,1)
#                        T-stat. 0.4280928   
#
#                    +   0.6993905   (w1+w2)
#                        T-stat. 15.30744    ***
#
#ERROR:  AUTO(2) 
#
#AUTOREGRESSIVE PARAMETERS:
#Rho             Std. Error      T-stat.         
# 0.05743131      0.3324101       0.1727725       
# 0.007785936     0.2647013       0.02941404      
#
#
#STATs:
#R-Squared                      : 0.985263    
#Adjusted R-Squared             : 0.979595    
#Durbin-Watson Statistic        : 1.966609    
#Sum of squares of residuals    : 9.273455    
#Standard Error of Regression   : 0.8445961   
#Log of the Likelihood Function : -20.14564   
#F-statistic                    : 173.8271    
#F-probability                  : 1.977107e-11
#Akaike's IC                    : 54.29129    
#Schwarz's IC                   : 60.90236    
#Mean of Dependent Variable     : 55.71765    
#Number of Observations         : 19
#Number of Degrees of Freedom   : 13
#Current Sample (year-period)   : 1925-1 / 1941-1
#
#
#Signif. codes:   *** 0.001  ** 0.01  * 0.05  
#
# 
# ...similar output for the all the regressions.

#simulate GDP in 1925-1930
kleinModel=SIMULATE(kleinModel, 
                      TSRANGE=c(1925,1,1930,1), 
                      simIterLimit = 100)

# Simulation:    100.00%
# ...SIMULATE OK

#print simulated gdp
TABIT(kleinModel$simulation$y)
#
#DATE, PER, kleinModel$simulation$y
#
#      1925, 1  ,  62.74953      
#      1926, 1  ,  56.46665      
#      1927, 1  ,  48.3741       
#      1928, 1  ,  55.58927      
#      1929, 1  ,  73.35799      
#      1930, 1  ,  74.93561  

#get multiplier matrix in 1941
kleinModel=MULTMATRIX(kleinModel,
                        TSRANGE=c(1941,1,1941,1),
                        INSTRUMENT=c('w2','g'),
                        TARGET=c('cn','y'),
                        simIterLimit = 100)

# Multipliter Matrix:    100.00%
# ...MULTMATRIX OK

kleinModel$MultiplierMatrix
#           w2_1      g_1
#cn_1 -0.1576305 2.837179
#y_1  -0.7182719 5.693190

#we want an arbitrary value on Consumption of 66 in 1940 and 78 in 1941
#we want an arbitrary value on GNP of 77 in 1940 and 98 in 1941
kleinTargets = list(
                    cn = TIMESERIES(66,78,START=c(1940,1),FREQ=1),
                    y  = TIMESERIES(77,98,START=c(1940,1),FREQ=1)
                    )

#renormalize the model              
kleinModel=RENORM(kleinModel
                   ,INSTRUMENT = c('w2','g')
                   ,TARGET = kleinTargets
                   ,TSRANGE = c(1940,1,1941,1)
                   ,simIterLimit = 100
 );

# Convergence reached in 3 iterations.
# ...RENORM OK

#The calculated values of exogenous INSTRUMENT 
#that allow to achieve the desired endogenous TARGET values
#are stored into the model:

with(kleinModel,TABIT(modelData$w2,
                      renorm$INSTRUMENT$w2,
                      modelData$g,
                      renorm$INSTRUMENT$g))

# DATE, PER, modelData$w2, renorm$w2, modelData$g, renorm$g
# 
#       ...
# 
#       1938, 1  ,        7.7,           ,         13,           
#       1939, 1  ,        7.8,           ,       14.4,           
#       1940, 1  ,          8,    7.41333,       15.4,    16.1069
#       1941, 1  ,        8.5,     9.3436,       22.3,    22.6599

#So, if we want to achieve on "cn" (Consumption) an arbitrary simulated value of 66 in 1940
#and 78 in 1941, and if we want to achieve on "y" (GNP) an arbitrary simulated value of 77
#in 1940 and 98 in 1941, we need to change exogenous "w2" (Wage Bill of the Government
#Sector) from 8 to 7.41 in 1940 and from 8.5 to 9.34 in 1941, and we need to change exogenous
#"g"(Government non-Wage Spending) from 15.4 to 16.1 in 1940 and from 22.3 to 22.66 in 1941.

#Let's verify:

#create a new model
kleinRenorm=kleinModel

#update the required INSTRUMENT
kleinRenorm$modelData=kleinRenorm$renorm$modelData

#simulate the new model
kleinRenorm=SIMULATE(kleinRenorm
                  ,TSRANGE=c(1940,1,1941,1)
                  ,simConvergence=0.00001
                  ,simIterLimit=100
                  )
#Simulation:    100.00%
#...SIMULATE OK

#verify TARGETs are achieved
with(kleinRenorm$simulation,
    TABIT(cn,y)
    )
    
#    DATE, PER, cn             , y              
#
#    1940, 1  ,  66.02157      ,  77.03568      
#    1941, 1  ,  78.05216      ,  98.09119 

bimets estimation and simulation results have been compared to the output results of leading commercial econometric software, by using several large and complex models.

The models used in the comparison have more than:

In these models we can find equations with restricted coefficients, polynomial distributed lags, error autocorrelation and conditional evaluation of technical identities; all models have been simulated in static, dynamic, and forecast mode, with exogenization and constant adjustments of endogenous variables, through the use of bimets capabilities.

In the +800 endogenous simulated time series over the +20 simulated periods (i.e. more than 16.000 simulated observations), the average percentage difference between bimets and leading commercial software results has a magnitude of 10E-7 %. The difference between results calculated by using different commercial software has the same average magnitude.

Installation

The package can be installed and loaded in R with the following commands:

install.packages("bimets")
library(bimets)

Guidelines for contributing

We welcome contributions to the bimets package. In the case please write to the mantainer: Andrea.Luciani@bancaditalia.it.

License

The bimets package is licensed under the EUPL

Disclaimer: The views and opinions expressed in these pages are those of the authors and do not necessarily reflect the official policy or position of the Bank of Italy. Examples of analysis performed within these pages are only examples. They should not be utilized in real-world analytic products as they are based only on very limited and dated open source information. Assumptions made within the analysis are not reflective of the position of the Bank of Italy.