The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Case Study: Bookshop Orders
Vladimír Holý & Petra Tomanová
2024-02-01
Introduction
We loosely follow Tomanová and Holý (2021) and analyze the timing of
orders from a Czech antiquarian bookshop. Besides seasonality and
diurnal patterns, one would expect the times of orders to be independent
of each other. However, this is not the case and we use a GAS model to
capture dependence between the times of orders.
A strand of financial econometrics is devoted to analyzing the timing
of transactions by the so-called autoregressive conditional duration
(ACD) model introduced by Engle and Russell (1998). For a textbook
treatment of such financial point processes, see e.g. Hautsch
(2012).
Data Preparation
Let us prepare the analyzed data. We use the
bookshop_orders dataset containing times of orders from
June 8, 2018 to December 20, 2018. The differences of subsequent times,
i.e. durations, are already included in the dataset. Additionally, the
dataset includes durations that have been adjusted for diurnal patterns
using smoothing splines. This is the time series we are interested
in.
library("gasmodel")
data("bookshop_orders")
y <- bookshop_orders$duration_adj[-1]
Model Estimation
The following distributions are available for our data type. We
utilize the generalized gamma family.
distr(filter_type = "duration", filter_dim = "uni")
#> distr_title param_title distr param type dim orthog default
#> 6 Birnbaum-Saunders Scale bisa scale duration uni TRUE TRUE
#> 7 Burr Scale burr scale duration uni FALSE TRUE
#> 11 Exponential Rate exp rate duration uni TRUE FALSE
#> 12 Exponential Scale exp scale duration uni TRUE TRUE
#> 13 Exponential-Logarithmic Rate explog rate duration uni FALSE TRUE
#> 14 Fisk Scale fisk scale duration uni TRUE TRUE
#> 15 Gamma Rate gamma rate duration uni FALSE FALSE
#> 16 Gamma Scale gamma scale duration uni FALSE TRUE
#> 17 Generalized Gamma Rate gengamma rate duration uni FALSE FALSE
#> 18 Generalized Gamma Scale gengamma scale duration uni FALSE TRUE
#> 25 Log-Normal Log-Mean-Variance lognorm logmeanvar duration uni TRUE TRUE
#> 26 Lomax Scale lomax scale duration uni FALSE TRUE
#> 34 Rayleigh Scale rayleigh scale duration uni TRUE TRUE
#> 40 Weibull Rate weibull rate duration uni FALSE FALSE
#> 41 Weibull Scale weibull scale duration uni FALSE TRUE
First, we estimate the model based on the exponential distribution.
By default, the logarithmic link for the time-varying scale parameter is
adopted. In this particular case, the Fisher information is constant and
the three scalings are therefore equivalent.
est_exp <- gas(y = y, distr = "exp")
est_exp
#> GAS Model: Exponential Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega -0.00089754 0.00117598 -0.7632 0.4453
#> log(scale)_alpha1 0.04992815 0.00657547 7.5931 3.123e-14 ***
#> log(scale)_phi1 0.96278385 0.00918996 104.7647 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5571.078, AIC: 11148.16, BIC: 11168.11
Second, we estimate the model based on the Weibull distribution.
Compared to the exponential distribution, it has an additional shape
parameter. By default, the first parameter is assumed time-varying while
the remaining are assumed static. In our case, the model features the
time-varying scale parameter with the constant shape parameter. However,
it is possible to modify this behavior using the par_static
argument.
est_weibull <- gas(y = y, distr = "weibull")
est_weibull
#> GAS Model: Weibull Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega -0.0019173 0.0013710 -1.3985 0.162
#> log(scale)_alpha1 0.0569780 0.0081800 6.9655 3.272e-12 ***
#> log(scale)_phi1 0.9617316 0.0102214 94.0896 < 2.2e-16 ***
#> shape 0.9472091 0.0094738 99.9819 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5555.903, AIC: 11119.81, BIC: 11146.41
Third, we estimate the model based on the gamma distribution. This is
another generalization of the exponential distribution with an
additional shape parameter.
est_gamma <- gas(y = y, distr = "gamma")
est_gamma
#> GAS Model: Gamma Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega 0.0010440 0.0013489 0.7740 0.4389
#> log(scale)_alpha1 0.0526020 0.0071647 7.3418 2.107e-13 ***
#> log(scale)_phi1 0.9627838 0.0094368 102.0247 < 2.2e-16 ***
#> shape 0.9491683 0.0155575 61.0102 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5565.939, AIC: 11139.88, BIC: 11166.48
Fourth, we estimate the model based on the generalized gamma
distribution. The generalized gamma distribution encompasses all three
aforementioned distributions as special cases.
est_gengamma <- gas(y = y, distr = "gengamma")
est_gengamma
#> GAS Model: Generalized Gamma Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega -0.057636 0.021624 -2.6653 0.007691 **
#> log(scale)_alpha1 0.071908 0.011810 6.0889 1.137e-09 ***
#> log(scale)_phi1 0.950375 0.015152 62.7220 < 2.2e-16 ***
#> shape1 1.886317 0.168357 11.2043 < 2.2e-16 ***
#> shape2 0.660542 0.033779 19.5546 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5521.126, AIC: 11052.25, BIC: 11085.51
By comparing the Akaike information criterion (AIC), we find that the
most general model, i.e. the one based on the generalized gamma
distribution, is the most suitable. For this purpose, we use generic
function AIC(). Alternatively, the AIC of an estimated
model is stored in est_gengamma$fit$aic.
AIC(est_exp, est_weibull, est_gamma, est_gengamma)
#> df AIC
#> est_exp 3 11148.16
#> est_weibull 4 11119.81
#> est_gamma 4 11139.88
#> est_gengamma 5 11052.25
Let us take a look on the time-varying parameters of the generalized
gamma model.
Trend
We can see a slight negative trend in time-varying parameters. We can
try including a trend as an exogenous variable for all four considered
distributions.
x <- as.integer(as.Date(bookshop_orders$datetime[-1])) - 17690
est_exp_tr <- gas(y = y, x = x, distr = "exp", reg = "sep")
est_exp_tr
#> GAS Model: Exponential Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega 0.29796923 0.04555104 6.5414 6.093e-11 ***
#> log(scale)_beta1 -0.00306464 0.00037483 -8.1760 2.934e-16 ***
#> log(scale)_alpha1 0.05458719 0.00793868 6.8761 6.151e-12 ***
#> log(scale)_phi1 0.91427487 0.02071189 44.1425 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5546.41, AIC: 11100.82, BIC: 11127.43
est_weibull_tr <- gas(y = y, x = x, distr = "weibull", reg = "sep")
est_weibull_tr
#> GAS Model: Weibull Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega 0.27259433 0.04799805 5.6793 1.353e-08 ***
#> log(scale)_beta1 -0.00304756 0.00039284 -7.7578 8.640e-15 ***
#> log(scale)_alpha1 0.06213485 0.00967455 6.4225 1.341e-10 ***
#> log(scale)_phi1 0.91083171 0.02285770 39.8479 < 2.2e-16 ***
#> shape 0.95160961 0.00954366 99.7112 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5533.828, AIC: 11077.66, BIC: 11110.91
est_gamma_tr <- gas(y = y, x = x, distr = "gamma", reg = "sep")
est_gamma_tr
#> GAS Model: Gamma Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega 0.34382456 0.04940370 6.9595 3.415e-12 ***
#> log(scale)_beta1 -0.00306464 0.00038353 -7.9907 1.342e-15 ***
#> log(scale)_alpha1 0.05714860 0.00855559 6.6797 2.395e-11 ***
#> log(scale)_phi1 0.91427486 0.02119257 43.1413 < 2.2e-16 ***
#> shape 0.95518003 0.01566489 60.9759 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5542.452, AIC: 11094.9, BIC: 11128.16
est_gengamma_tr <- gas(y = y, x = x, distr = "gengamma", reg = "sep")
est_gengamma_tr
#> GAS Model: Generalized Gamma Distribution / Scale Parametrization / Unit Scaling
#>
#> Coefficients:
#> Estimate Std. Error Z-Test Pr(>|Z|)
#> log(scale)_omega -0.84823616 0.21103504 -4.0194 5.834e-05 ***
#> log(scale)_beta1 -0.00295781 0.00039514 -7.4855 7.129e-14 ***
#> log(scale)_alpha1 0.08248222 0.01361272 6.0592 1.368e-09 ***
#> log(scale)_phi1 0.87825862 0.03321251 26.4436 < 2.2e-16 ***
#> shape1 1.88969866 0.17087187 11.0592 < 2.2e-16 ***
#> shape2 0.66228593 0.03432663 19.2936 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Log-Likelihood: -5500.065, AIC: 11012.13, BIC: 11052.04
The trend variable is significant in all cases. The AIC also confirms
improvement of the fit.
AIC(est_exp_tr, est_weibull_tr, est_gamma_tr, est_gengamma_tr)
#> df AIC
#> est_exp_tr 4 11100.82
#> est_weibull_tr 5 11077.66
#> est_gamma_tr 5 11094.90
#> est_gengamma_tr 6 11012.13
Note that the time-varying parameters returned by the
gas() function include the effect of exogenous variables.
By using the plot() function, the now modeled trend can be
clearly seen.
Bootstrapping
To assess the suitability of standard deviations based on asymptotics
for our finite sample, we employ the gas_bootstrap()
function. This function conducts a parametric bootstrap, allowing us to
calculate standard errors and quantiles. It’s important to note that
this could be computationally very intensive, depending on the number of
repetitions, the quantity of observations, the complexity of the model
structure, and the optimizer used. Note that the function supports
parallelization through arguments parallel_function and
parallel_arguments. For example, for the snow
parallelization functionality with 4 cores, you can call
gas_bootstrap(est_gengamma_tr, parallel_function = wrapper_parallel_snow, parallel_arguments = list(spec = 4)).
set.seed(42)
boot_gengamma_tr <- gas_bootstrap(est_gengamma_tr, method = "parametric", rep_boot = 100)
boot_gengamma_tr
#> GAS Model: Generalized Gamma Distribution / Scale Parametrization / Unit Scaling
#>
#> Method: Parametric Bootstrap
#>
#> Number of Bootstrap Samples: 100
#>
#> Bootstrapped Coefficients:
#> Original Mean Std. Error P-Value 2.5% 97.5%
#> log(scale)_omega -0.848236158 -0.842209482 0.2167730720 0 -1.363494729 -0.48985072
#> log(scale)_beta1 -0.002957812 -0.002958998 0.0003882626 0 -0.003716998 -0.00218889
#> log(scale)_alpha1 0.082482219 0.080713448 0.0112301969 0 0.059672742 0.10357046
#> log(scale)_phi1 0.878258619 0.874389999 0.0311604443 0 0.813825846 0.92564283
#> shape1 1.889698658 1.887747102 0.1815016954 0 1.588541151 2.30737901
#> shape2 0.662285932 0.665782378 0.0353508015 0 0.593998755 0.72810791
The results can also be viewed in a boxplot.
Given that the number of observations in our model is 5752
(accessible through est_gengamma_tr$model$t), it is
reasonable to anticipate that standard deviations based on asymptotics
would yield precise results. Fortunately, this holds true in our
scenario. Note that standard deviations can also be obtained using the
vcov() generic function for both
est_gengamma_tr and boot_gengamma_tr.
est_gengamma_tr$fit$coef_sd - boot_gengamma_tr$bootstrap$coef_sd
#> log(scale)_omega log(scale)_beta1 log(scale)_alpha1 log(scale)_phi1 shape1
#> -5.738027e-03 6.877385e-06 2.382526e-03 2.052065e-03 -1.062983e-02
#> shape2
#> -1.024169e-03
Simulation
Lastly, we highlight the utilization of simulation techniques.
Simulation is executed using the gas_simulate() function,
which can be supplied with either an estimated model or a custom model
structure.
t_sim <- 20
x_sim <- rep(max(x) + 1, t_sim)
set.seed(42)
sim_gengamma_tr <- gas_simulate(est_gengamma_tr, t_sim = t_sim, x_sim = x_sim)
sim_gengamma_tr
#> GAS Model: Generalized Gamma Distribution / Scale Parametrization / Unit Scaling
#>
#> Simulations:
#> t1 t2 t3 t4 t5 t6 t7 t8
#> 1.012906817 0.709825947 1.145436580 0.118002151 0.257303884 2.289065205 2.292494996 0.749569859
#> t9 t10 t11 t12 t13 t14 t15 t16
#> 0.683965696 0.265536370 0.006713397 0.081422285 0.611021641 0.802745017 1.131981476 0.162341162
#> t17 t18 t19 t20
#> 0.128581898 0.104261067 0.650304016 0.223734674
The simulated time series can be plotted using the generic
plot() function.
The simulated time series can be employed, for example, to assess the
impact of order arrivals on queuing systems, as demonstrated by Tomanová
and Holý (2021).
References
Engle, R. F. and Russell, J. R. (1998). Autoregressive Conditional
Duration: A New Model for Irregularly Spaced Transaction Data.
Econometrica, 66(5), 1127–1162. doi: 10.2307/2999632.
Hautsch, N. (2012). Econometrics of Financial High-Frequency
Data. Springer. doi: 10.1007/978-3-642-21925-2.
Tomanová, P. and Holý, V. (2021). Clustering of Arrivals in Queueing
Systems: Autoregressive Conditional Duration Approach. Central
European Journal of Operations Research, 29(3),
859–874. doi: 10.1007/s10100-021-00744-7.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.
