**bimets** is an R package developed with the aim to
ease time series analysis and to build 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.

Please consider reading the package vignette, wherein there are figures and the mathematical expressions are better formatted than in html.

If you have general questions about using **bimets**, or
for bug reports, please use the git issue
tracker or write to the
maintainer.

**TIME SERIES**

- supports daily, weekly, monthly, quarterly, semiannual, yearly time series, and frequency of 24 and 36 periods per year.
- indexing
*by date*- users can select and modify a single observation by using the syntax`ts['Date']`

, or multiple observations by using`ts['StartDate/EndDate']`

. - indexing
*by year-period*- users can select and modify observations by providing a two-dimensional numerical array composed by the year and the period, e.g.`ts[[Year,Period]]`

. - indexing
*by observation index*- users can select and modify observations by providing the array of requested indices (core R), e.g.`ts[indices]`

. *Aggregation/Disaggregation*- the package provides advanced (dis)aggregation capabilities, having linear interpolation capabilities in disaggregation, and aggregation functions (e.g.`STOCK`

,`SUM`

,`AVE`

, etc.) while reducing the time series frequency.*Manipulation*- the package provides, among others, the following time series manipulation capabilities: time series extension`TSEXTEND()`

, time series merging`TSMERGE()`

, time series projection`TSPROJECT()`

, lag`TSLAG()`

, lag differences absolute and percentage`TSDELTA()`

`TSDELTAP()`

, cumulative product`CUMPROD()`

, cumulative sum`CUMSUM()`

, moving average`MOVAVG()`

, moving sum`MOVSUM()`

, time series data presentation`TABIT()`

.

Example:

```
#create ts
=TIMESERIES((1:100),START=c(2000,1),FREQ='D')
myTS
1:3] #get first three obs.
myTS['2000-01-12'] #get Jan 12, 2000
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'] <- 42 #assign to Jan 15, 2000
myTS[2000,3]] <- pi #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 period 100 (i.e. extend series)
myTS[[
#aggregation/disaggregation
<- TIMESERIES(1:100,START=c(2000,1),FREQ='M')
myMonthlyTS <- YEARLY(myMonthlyTS,'AVE')
myYearlyTS <- DAILY(myMonthlyTS,'INTERP_CENTER')
myDailyTS
#create and manipulate time series
<- TIMESERIES(1:100,START=c(2000,1),FREQ='M')
myTS1 <- TIMESERIES(-(1:100),START=c(2005,1),FREQ='M')
myTS2
#extend time series
<- TSEXTEND(myTS1,UPTO = c(2020,4),EXTMODE = 'QUADRATIC')
myExtendedTS
#merge two time series
<-TSMERGE(myExtendedTS,myTS2,fun = 'SUM')
myMergedTS
#project time series
<- TSPROJECT(myMergedTS,TSRANGE = c(2004,2,2006,4))
myProjectedTS
#lag time series
<- TSLAG(myProjectedTS,2)
myLagTS
#percentage delta of time series
<- TSDELTAP(myLagTS,2)
myDeltaPTS
#moving average of time series
<- MOVAVG(myDeltaPTS,5)
myMovAveTS
#print data
TABIT(myMovAveTS,myTS1)
# Date, Prd., 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
```

More details are available in the reference manual.

**MODELING:**

**bimets** econometric modeling capabilities
comprehend:

*Model Definition Language*- the specification of an econometric model is translated and identified by keyword statements which are grouped in a model file, i.e. a plain text file or a`character`

R variable with a specific syntax. Collectively, these keyword statements constitute a kind of a**bimets**Model Description Language (i.e.`MDL`

). The MDL syntax allows the definition of behavioral equations, technical equations, conditional evaluations during the simulation, and other model properties.*Estimation*- the estimation function`ESTIMATE()`

supports Ordinary Least Squares, Instrumental Variables, deterministic linear restrictions on the coefficients, Almon Polynomial Distributed Lags (i.e.`PDL`

), autocorrelation of the errors, structural stability analysis (Chow tests).*Simulation*- the simulation function`SIMULATE()`

supports static, dynamic and forecast simulations, residuals check, partial or total exogenization of endogenous variables, constant adjustment of endogenous variables (i.e. add-factors).*Stochastic Simulation*- in the stochastic simulation function`STOCHSIMULATE()`

the structural disturbances are given values that have specified stochastic properties. The error terms of the estimated behavioral equation of the model are appropriately perturbed. Identity equations and exogenous variables can be as well perturbed by disturbances that have specified stochastic properties. The model is then solved for each data set with different values of the disturbances. Finally, mean and standard deviation are computed for each simulated endogenous variable.*Multipliers Evaluation*- the multipliers evaluation function`MULTMATRIX()`

computes the matrix of both impact and interim multipliers for a selected set of endogenous variables, i.e. the`TARGET`

, with respect to a selected set of exogenous variables, i.e. the`INSTRUMENT`

.*Endogenous Targeting*- the “renormalization” function`RENORM()`

performs the endogenous targeting of econometric models, which consists of solving the model while interchanging the role of one or more endogenous variables with an equal number of exogenous variables. The procedure determines the values for the`INSTRUMENT`

exogenous variables that allow achieving the desired values for the`TARGET`

endogenous variables, subject to the constraints given by the equations of the model. This is an approach to economic and monetary policy analysis.*Optimal Control*- The optimization consists of maximizing a social welfare function, i.e. the objective-function, depending on exogenous and (simulated) endogenous variables, subject to user constraints plus the constraints imposed by the econometric model equations. Users are allowed to define constraints and objective-functions of any degree, and are allowed to provide different constraints and objective-functions in different optimization time periods.

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

```
# MODEL DEFINITION AND LOADING #################################################
#define the Klein model
<- "MODEL
klein1.txt
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
<- LOAD_MODEL(modelText = klein1.txt)
kleinModel
# Loading model: "klein1.txt"...
# Analyzing behaviorals...
# Analyzing identities...
# Optimizing...
# Loaded model "klein1.txt":
# 3 behaviorals
# 3 identities
# 12 coefficients
# ...LOAD MODEL OK
$behaviorals$cn
kleinModel# $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
$incidence_matrix
kleinModel
# 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
<- list(
kleinModelData 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)
);
<- LOAD_MODEL_DATA(kleinModel,kleinModelData)
kleinModel # Load model data "kleinModelData" into model "klein1.txt"...
# ...LOAD MODEL DATA OK
# MODEL ESTIMATION #############################################################
<- ESTIMATE(kleinModel)
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. 12.13083 ***
#
# + 0.3442816 p
# T-stat. 3.533253 **
#
# + 0.03443117 TSLAG(p,1)
# T-stat. 0.3937881
#
# + 0.6993905 (w1+w2)
## T-stat. 14.0808 ***
#
#ERROR STRUCTURE: 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 : 0.9785644
#Durbin-Watson Statistic : 1.966609
#Sum of squares of residuals : 9.273455
#Standard Error of Regression : 0.9181728
#Log of the Likelihood Function : -18.97047
#F-statistic : 147.0844
#F-probability : 1.090551e-09
#Akaike's IC : 51.94093
#Schwarz's IC : 57.77343
#Mean of Dependent Variable : 55.71765
#Number of Observations : 17
#Number of Degrees of Freedom : 11
#Current Sample (year-period) : 1925-1 / 1941-1
#
#
#Signif. codes: *** 0.001 ** 0.01 * 0.05
#
#
# ...similar output for all the regressions.
# MODEL SIMULATION #############################################################
#simulate GNP in 1925-1930
<- SIMULATE(kleinModel,
kleinModel TSRANGE=c(1925,1,1930,1),
simIterLimit = 100)
# Simulation: 100.00%
# ...SIMULATE OK
#print simulated GNP
TABIT(kleinModel$simulation$y)
#
# Date, Prd., kleinModel$simulation$y
#
# 1925, 1 , 62.74953
# 1926, 1 , 56.46665
# 1927, 1 , 48.3741
# 1928, 1 , 55.58927
# 1929, 1 , 73.35799
# 1930, 1 , 74.93561
# MODEL STOCHASTIC FORECAST ####################################################
#we want to perform a stochastic forecast of the GNP up to 1944
#we will add normal disturbances to endogenous Consumption 'cn'
#in 1942 by using its regression standard error
#we will add uniform disturbances to exogenous Government Expenditure 'g'
#in whole TSRANGE
<- list(
myStochStructure cn=list(
TSRANGE=c(1942,1,1942,1),
TYPE='NORM',
PARS=c(0,kleinModel$behaviorals$cn$statistics$StandardErrorRegression)
),g=list(
TSRANGE=TRUE,
TYPE='UNIF',
PARS=c(-1,1)
)
)
#we need to extend exogenous variables up to 1944
$modelData <- within(kleinModel$modelData,{
kleinModel= TSEXTEND(w2, UPTO=c(1944,1),EXTMODE='CONSTANT')
w2 = TSEXTEND(t, UPTO=c(1944,1),EXTMODE='LINEAR')
t = TSEXTEND(g, UPTO=c(1944,1),EXTMODE='CONSTANT')
g = TSEXTEND(k, UPTO=c(1944,1),EXTMODE='LINEAR')
k = TSEXTEND(time,UPTO=c(1944,1),EXTMODE='LINEAR')
time
})
#stochastic model forecast
<- STOCHSIMULATE(kleinModel
kleinModel simType='FORECAST'
,TSRANGE=c(1941,1,1944,1)
,StochStructure=myStochStructure
,StochSeed=123
,
)
#print mean and standard deviation of forecasted GNP
with(kleinModel$stochastic_simulation,TABIT(y$mean, y$sd))
# Date, Prd., y$mean , y$sd
#
# 1941, 1 , 104.3109 , 3.267681
# 1942, 1 , 115.4303 , 7.014553
# 1943, 1 , 91.64526 , 7.685761
# 1944, 1 , 33.41637 , 6.199828
# MODEL MULTIPLIERS ###########################################################
#get multiplier matrix in 1941
<- MULTMATRIX(kleinModel,
kleinModel TSRANGE=c(1941,1,1941,1),
INSTRUMENT=c('w2','g'),
TARGET=c('cn','y'),
simIterLimit = 100)
# Multiplier Matrix: 100.00%
# ...MULTMATRIX OK
$MultiplierMatrix
kleinModel# w2_1 g_1
#cn_1 -0.1596758 2.853391
#y_1 -0.7216553 5.720007
# MODEL ENDOGENOUS TARGETING ###################################################
#we want an arbitrary value on Consumption of 66 in 1940 and 78 in 1941
#we want an arbitrary value on GNP of 77 in 1940 and 98 in 1941
<- list(
kleinTargets cn = TIMESERIES(66,78,START=c(1940,1),FREQ=1),
y = TIMESERIES(77,98,START=c(1940,1),FREQ=1)
)#Then, we can perform the model endogenous targeting
#by using Government Wage Bill 'w2'
#and Government Expenditure 'g' as
#INSTRUMENT in the years 1940 and 1941:
<- RENORM(kleinModel
kleinModel INSTRUMENT = c('w2','g')
,TARGET = kleinTargets
,TSRANGE = c(1940,1,1941,1)
,simIterLimit = 100
,
)
# Convergence reached in 3 iterations.
# ...RENORM OK
#The calculated values of exogenous INSTRUMENT
#that allow achieving the desired endogenous TARGET values
#are stored into the model:
with(kleinModel,TABIT(modelData$w2,
$INSTRUMENT$w2,
renorm$g,
modelData$INSTRUMENT$g))
renorm
# Date, Prd., modelData$w2, renorm$w2, modelData$g, renorm$g
#
# ...
#
# 1938, 1 , 7.7, , 13,
# 1939, 1 , 7.8, , 14.4,
# 1940, 1 , 8, 8.857669, 15.4, 15.81276
# 1941, 1 , 8.5, 12.18823, 22.3, 21.83899
#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 8.86 in 1940 and from 8.5 to 12.19 in 1941, and we need to change exogenous
#"g"(Government Expenditure) from 15.4 to 15.81 in 1940 and from 22.3 to 21.84 in 1941.
#Let's verify:
#create a new model
<- kleinModel
kleinRenorm
#update the required INSTRUMENT
$modelData <- kleinRenorm$renorm$modelData
kleinRenorm
#simulate the new model
<- SIMULATE(kleinRenorm
kleinRenorm TSRANGE=c(1940,1,1941,1)
,simConvergence=0.00001
,simIterLimit=100
,
)#Simulation: 100.00%
#...SIMULATE OK
#verify TARGETs are achieved
with(kleinRenorm$simulation,
TABIT(cn,y)
)
# Date, Prd., cn , y
#
# 1940, 1 , 66.02157 , 77.03568
# 1941, 1 , 78.05216 , 98.09119
# MODEL OPTIMAL CONTROL ########################################################
#reset time series data in model
<- LOAD_MODEL_DATA(kleinModel
kleinModel
,kleinModelDataquietly = TRUE)
,
#we want to maximize the non-linear objective function:
#f()=(y-110)+(cn-90)*ABS(cn-90)-(g-20)^0.5
#in 1942 by using INSTRUMENT cn in range (-5,5)
#(cn is endogenous so we use the add-factor)
#and g in range (15,25)
#we will also impose the following non-linear restriction:
#g+(cn^2)/2<27 & g+cn>17
#we need to extend exogenous variables up to 1942
$modelData <- within(kleinModel$modelData,{
kleinModel= TSEXTEND(w2, UPTO = c(1942,1), EXTMODE = 'CONSTANT')
w2 = TSEXTEND(t, UPTO = c(1942,1), EXTMODE = 'LINEAR')
t = TSEXTEND(g, UPTO = c(1942,1), EXTMODE = 'CONSTANT')
g = TSEXTEND(k, UPTO = c(1942,1), EXTMODE = 'LINEAR')
k = TSEXTEND(time, UPTO = c(1942,1), EXTMODE = 'LINEAR')
time
})
#define INSTRUMENT and boundaries
<- list(
myOptimizeBounds cn = list( TSRANGE = TRUE
BOUNDS = c(-5,5)),
,g = list( TSRANGE = TRUE
BOUNDS = c(15,25))
,
)
#define restrictions
<- list(
myOptimizeRestrictions myRes1=list(
TSRANGE = TRUE
INEQUALITY = 'g+(cn^2)/2<27 & g+cn>17')
,
)
#define objective function
<- list(
myOptimizeFunctions myFun1 = list(
TSRANGE = TRUE
FUNCTION = '(y-110)+(cn-90)*ABS(cn-90)-(g-20)^0.5')
,
)
#Monte-Carlo optimization by using 50.000 stochastic realizations
#and 1E-7 convergence criterion
<- OPTIMIZE(kleinModel
kleinModel simType = 'FORECAST'
,TSRANGE=c(1942,1,1942,1)
,simConvergence= 1E-7
,simIterLimit = 1000
,StochReplica = 50000
,StochSeed = 123
,OptimizeBounds = myOptimizeBounds
,OptimizeRestrictions = myOptimizeRestrictions
,OptimizeFunctions = myOptimizeFunctions
,quietly = TRUE)
,
#print local maximum
$optimize$optFunMax
kleinModel#[1] 6.92624
#print INSTRUMENT that allow local maximum to be achieved
$optimize$INSTRUMENT
kleinModel#$cn
#Time Series:
#Start = 1942
#End = 1942
#Frequency = 1
#[1] 1.996275
#
#$g
#Time Series:
#Start = 1942
#End = 1942
#Frequency = 1
#[1] 24.9766
```

Transformations of the dependent variable are allowed in
`EQ>`

definition, e.g. `TSDELTA(cn)=...`

,
`EXP(i)=...`

, `TSDELTALOG(y)=...`

, etc.

More details are available in the reference manual.

**COMPUTATIONAL DETAILS**

The iterative simulation procedure is the most time-consuming
operation of the **bimets** package. For small models, this
operation is quite immediate; on the other hand, the simulation of
models that count hundreds of equations could last for minutes,
especially if the requested operation involves a parallel simulation
having hundreds of realizations per equation. This could be the case for
the endogenous targeting, the stochastic simulation and the optimal
control.

The `SIMULATE`

code has been optimized in order to
minimize the execution time in these cases. In terms of computational
efficiency, the procedure takes advantage of the fact that multiple
datasets are bind together in matrices, therefore in order to achieve a
global convergence, the iterative simulation algorithm is executed once
for all perturbed datasets. This solution can be viewed as a sort of a
SIMD (i.e. Single Instruction Multiple Data) parallel simulation: the
`SIMULATE`

algorithm transforms time series into matrices and
consequently can easily bind multiple datasets by column. At the same
time, the single run ensures a fast code execution, while each column in
the output matrices represents a stochastic or perturbed
realization.

The above approach is even faster if R has been compiled and linked to optimized multi-threaded numerical libraries, e.g. Intel(R) MKL, OpenBlas, Microsoft(R) R Open, etc.

Finally, model equations are pre-fetched into sorted R expressions,
and an optimized R environment is defined and reserved to the
`SIMULATE`

algorithm; this approach removes the overhead
usually caused by expression parsing and by the `R`

looking
for variables inside nested environments.

**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:

- +100 behavioral equations;
- +700 technical identities;
- +500 coefficients;
- +1000 time series of endogenous and exogenous variables;

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.

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

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

We welcome contributions to the **bimets** package. In
the case, please use the git issue
tracker or write to the
maintainer.

The **bimets** package is licensed under the GPL-3.

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.*