Function that estimates the Unbalanced Competition Regime Change Diachronic model. Fitted values for cumulative and instantaneous data are displayed (if display = T).
UCRCD(series1, series2, display=T, alpha=0.05,
delta=0.01, gamma=0.01, par="double",
m1 = BM(series1,display = F)$Estimate[1,1],
m2 = BM(series2,display = F)$Estimate[1,1],
p1c = BM(series1,display = F)$Estimate[2,1],
q1c = BM(series1,display = F)$Estimate[3,1],
p2 = BM(series2,display = F)$Estimate[2,1],
q2 = BM(series2,display = F)$Estimate[3,1])UCRCD returns an object of class "Dimora".
The function summary is used to obtain and print a summary table of the results. The generic accessor functions coefficients, fitted and residuals extract various useful features of the value returned by UCRCD.
An object of class "Dimora" is a list containing at least the following components:
the model formula used.
the model frame used.
a summary table of estimates.
a named vector of coefficients.
the statistical measure R-squared (on the istantaneous data).
the residual sum of squares (on the istantaneous data).
the residuals (observed cumulative data - fitted cumulative data).
the cumulative fitted values.
the cumlative observed data.
the matched call.
The UCRCD model is estimated on the instantaneous data. For this reason, the UCRCD function also returns the following elements:
the instantaneous observed data.
the instantaneous fitted values.
the residuals (observed instantaneous data - fitted instantaneous data).
the istantaneous observed data of the first product.
the istantaneous observed data of the second product.
the significance level for confidence intervals.
default is 'double'. Instead 'unique', meaning the costraint delta equal to gamma has been selected.
if TRUE returns the predicted values for cumulative and instantaneous observed data.
preliminary estimate of delta.
preliminary estimate of gamma.
preliminary estimate of the first product's market potential under competition
preliminary estimate of the second product's market potential
preliminary estimate of the first product's innovation coefficient under competition
preliminary estimate of the second product's innovation coefficient
preliminary estimate of the first product's imitation coefficient under competition
preliminary estimate of the second product's imitation coefficient
Zanghi Federico: federico.zanghi.11@gmail.com
Savio Andrea: svandr97@gmail.com
Ziliotto Filippo: filippo.ziliotto1996@gmail.com
Bessi Alessandro: alessandrobessi92@gmail.com
The model estimates several parameters that refer both to the stand-alone phase and the competition phase. The description of the parameters is reported below: (wom = "Word Of Mouth")
ma : Market Potential 1 (stand-alone phase)
p1a : Innovation coefficient 1 (stand-alone phase)
q1a : Imitation coefficient 1 (stand-alone phase)
mc : Market Potential 1 (competition phase)
p1c : Innovation coefficient 1 (competition phase)
q1c+delta : Within-product wom 1 (competition phase)
q1c : Cross-product wom 1 (competition phase)
p2 : Innovation coefficient 2
q2 : Within-product wom 2
q2-gamma : Cross-product wom 2
Guidolin, M. (2023). Innovation Diffusion Models: Theory and Practice, First Edition. John Wiley & Sons Ltd.
Guseo, R., & Mortarino, C. (2014). Within-brand and cross-brand word-of-mouth for sequential multi-innovation diffusions. IMA Journal of Management Mathematics, 25(3), 287-311.
The Dimora models: BM, GBM, GGM.
summary.Dimora for summaries.
plot.Dimora for graphics and residuals analysis.
predict.Dimora for prediction.
make.instantaneous to create instantaneous series from the cumulative one.
data(DBdimora)
Gd <- DBdimora$Gdenmark[1:36]
Rd <- DBdimora$Rdenmark[13:36]
Ca <- DBdimora$Caustralia
Ra <- DBdimora$Raustralia[26:56]
## Example 1
M8 <- UCRCD(Gd,Rd)
summary(M8)
plot.Dimora(M8,type="fit")
## Example 2
M9 <- UCRCD(Ca,Ra,par = "unique",display=FALSE)
summary(M9)
plot.Dimora(M9)
Run the code above in your browser using DataLab