A package for estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal et al. (2013) and Harvey (2013), also known as dynamic conditional score (DCS) models or score-driven (SD) models.

Model specification allows for various conditional distributions, different parametrizations, exogenous variables, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method and the Hessian matrix.

The package offers the following functions for working with GAS models:

The package handles probability distributions by the following functions:

In addition, the package provides the following datasets used in examples:


To install the gasmodel package from CRAN, you can use:


To install the development version of the gasmodel package from GitHub, you can use:



As a simple example, let us model volatility of daily S&P 500 prices in 2021 in the fashion of GARCH models. We estimate the GAS model based on the Student’s t-distribution with time-varying volatility and plot the filtered time-varying parameters:


data <- sp500_daily %>%
  mutate(return = log(close) - log(lag(close))) %>%
  filter(format(sp500_daily$date, "%Y") == "2021") %>%
  select(date, return)

#>       date                return         
#>  Min.   :2021-01-04   Min.   :-0.023512  
#>  1st Qu.:2021-04-05   1st Qu.:-0.006242  
#>  Median :2021-07-04   Median :-0.001423  
#>  Mean   :2021-07-03   Mean   :-0.001029  
#>  3rd Qu.:2021-10-01   3rd Qu.: 0.003219  
#>  Max.   :2021-12-31   Max.   : 0.026013

model_gas <- gas(y = data$return, distr = "t", par_static = c(TRUE, FALSE, TRUE))

#> GAS Model: Student‘s t Distribution / Mean-Variance Parametrization / Unit Scaling 
#> Coefficients: 
#>                    Estimate  Std. Error  Z-Test  Pr(>|Z|)    
#> mean            -0.00145631  0.00042388 -3.4357 0.0005911 ***
#> log(var)_omega  -2.16158419  0.76650952 -2.8200 0.0048018 ** 
#> log(var)_alpha1  0.54442475  0.15216805  3.5778 0.0003465 ***
#> log(var)_phi1    0.78322463  0.07644654 10.2454 < 2.2e-16 ***
#> df              10.11802479  6.59431541  1.5344 0.1249422    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> Log-Likelihood: 870.9712, AIC: -1731.942, BIC: -1714.295

ggplot(mapping = aes(x = data$date, y = model_gas$fit$par_tv[, 2])) +
  labs(title = "Filtered Time-Varying Volatility", x = "Date", y = "log(var)") +
  geom_line(color = "#771468") +

Case Studies

To further illustrate the usability of GAS models, the package includes the following case studies in the form of vignettes:

Supported Distributions

Currently, there are 24 distributions available.

The list of supported distribution can be obtained by the distr() function:

distr() %>%
  arrange(!default, param) %>%
  select(distr, distr_title, dim, type, param) %>%
  group_by(distr, distr_title, dim, type) %>%
  summarize(param = paste(param, collapse = ", ")) %>%
  ungroup() %>%
  arrange(distr) %>%
  print(right = FALSE, row.names = FALSE)
#> # A tibble: 24 × 5
#>    distr     distr_title                     dim   type     param                  
#>    <chr>     <chr>                           <fct> <fct>    <chr>                  
#>  1 bernoulli Bernoulli                       uni   binary   prob                   
#>  2 beta      Beta                            uni   interval conc, meansize, meanvar
#>  3 cat       Categorical                     multi cat      worth                  
#>  4 dirichlet Dirichlet                       multi comp     conc                   
#>  5 dpois     Double Poisson                  uni   count    mean                   
#>  6 exp       Exponential                     uni   duration scale, rate            
#>  7 gamma     Gamma                           uni   duration scale, rate            
#>  8 gengamma  Generalized Gamma               uni   duration scale, rate            
#>  9 geom      Geometric                       uni   count    mean, prob             
#> 10 laplace   Laplace                         uni   real     meanscale              
#> 11 mvnorm    Multivariate Normal             multi real     meanvar                
#> 12 mvt       Multivariate Student‘s t        multi real     meanvar                
#> 13 negbin    Negative Binomial               uni   count    nb2, prob              
#> 14 norm      Normal                          uni   real     meanvar                
#> 15 pluce     Plackett-Luce                   multi ranking  worth                  
#> 16 pois      Poisson                         uni   count    mean                   
#> 17 skellam   Skellam                         uni   integer  meanvar, diff, meandisp
#> 18 t         Student‘s t                     uni   real     meanvar                
#> 19 vonmises  von Mises                       uni   circular meanconc               
#> 20 weibull   Weibull                         uni   duration scale, rate            
#> 21 zigeom    Zero-Inflated Geometric         uni   count    mean                   
#> 22 zinegbin  Zero-Inflated Negative Binomial uni   count    nb2                    
#> 23 zipois    Zero-Inflated Poisson           uni   count    mean                   
#> 24 ziskellam Zero-Inflated Skellam           uni   integer  meanvar, diff, meandisp

Details of each distribution, including its density function, expected value, variance, score, and Fisher information, can be found in vignette distributions.

Generalized Autoregressive Score Models

The generalized autoregressive score (GAS) models of Creal et al. (2013) and Harvey (2013), also known as dynamic conditional score (DCS) models or score-driven (SD) models, have established themselves as a useful modern framework for time series modeling.

The GAS models are observation-driven models allowing for any underlying probability distribution \(p(y_t|f_t)\) with any time-varying parameters \(f_t\) for time series \(y_t\). They capture the dynamics of time-varying parameters using the autoregressive term and the lagged score, i.e. the gradient of the log-likelihood function. Exogenous variables can also be included. Specifically, time-varying parameters \(f_{t}\) follow the recursion \[f_{t} = \omega + \sum_{i=1}^M \beta_i x_{ti} + \sum_{j=1}^P \alpha_j S(f_{t - j}) \nabla(y_{t - j}, f_{t - j}) + \sum_{k=1}^Q \varphi_k f_{t-k},\] where \(\omega\) is a vector of constants, \(\beta_i\) are regression parameters, \(\alpha_j\) are score parameters, \(\varphi_k\) are autoregressive parameters, \(x_{ti}\) are exogenous variables, \(S(f_t)\) is a scaling function for the score, and \(\nabla(y_t, f_t)\) is the score given by \[\nabla(y_t, f_t) = \frac{\partial \ln p(y_t | f_t)}{\partial f_t}.\] Alternatively, a different model can be obtained by defining the recursion in the fashion of regression models with dynamic errors as \[f_{t} = \omega + \sum_{i=1}^M \beta_i x_{ti} + e_{t}, \quad e_t = \sum_{j=1}^P \alpha_j S(f_{t - j}) \nabla(y_{t - j}, f_{t - j}) + \sum_{k=1}^Q \varphi_k e_{t-k}.\]

The GAS models can be straightforwardly estimated by the maximum likelihood method. For the asymptotic theory regarding the GAS models and maximum likelihood estimation, see Blasques et al. (2014), Blasques et al. (2018), and Blasques et al. (2022).

The use of the score for updating time-varying parameters is optimal in an information theoretic sense. For an investigation of the optimality properties of GAS models, see Blasques et al. (2015) and Blasques et al. (2021).

Generally, the GAS models perform quite well when compared to alternatives, including parameter-driven models. For a comparison of the GAS models to alternative models, see Koopman et al. (2016) and Blazsek and Licht (2020).

The GAS class includes many well-known econometric models, such as the generalized autoregressive conditional heteroskedasticity (GARCH) model of Bollerslev (1986), the autoregressive conditional duration (ACD) model of Engle and Russel (1998), and the Poisson count model of Davis et al. (2003). More recently, a variety of novel score-driven models has been proposed, such as the Beta-t-(E)GARCH model of Harvey and Chakravarty (2008), the discrete price changes model of Koopman et al. (2018), the directional model of Harvey (2019), the bivariate Poisson model of Koopman and Lit (2019), and the ranking model of Holý and Zouhar (2022). For an overview of various GAS models, see Harvey (2022).

The extensive GAS literature is listed on


Blasques, F., Gorgi, P., Koopman, S. J., and Wintenberger, O. (2018). Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models. Electronic Journal of Statistics, 12(1), 1019–1052. doi: 10.1214/18-ejs1416.

Blasques, F., Koopman, S. J., and Lucas, A. (2014). Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes. Electronic Journal of Statistics, 8(1), 1088–1112. doi: 10.1214/14-ejs924.

Blasques, F., Koopman, S. J., and Lucas, A. (2015). Information-Theoretic Optimality of Observation-Driven Time Series Models for Continuous Responses. Biometrika, 102(2), 325–343. doi: 10.1093/biomet/asu076.

Blasques, F., Lucas, A., and van Vlodrop, A. C. (2021). Finite Sample Optimality of Score-Driven Volatility Models: Some Monte Carlo Evidence. Econometrics and Statistics, 19, 47–57. doi: 10.1016/j.ecosta.2020.03.010.

Blasques, F., van Brummelen, J., Koopman, S. J., and Lucas, A. (2022). Maximum Likelihood Estimation for Score-Driven Models. Journal of Econometrics, 227(2), 325–346. doi: 10.1016/j.jeconom.2021.06.003.

Blazsek, S. and Licht, A. (2020). Dynamic Conditional Score Models: A Review of Their Applications. Applied Economics, 52(11), 1181–1199. doi: 10.1080/00036846.2019.1659498.

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. doi: 10.1016/0304-4076(86)90063-1.

Creal, D., Koopman, S. J., and Lucas, A. (2013). Generalized Autoregressive Score Models with Applications. Journal of Applied Econometrics, 28(5), 777–795. doi: 10.1002/jae.1279.

Davis, R. A., Dunsmuir, W. T. M., and Street, S. B. (2003). Observation-Driven Models for Poisson Counts. Biometrika, 90(4), 777–790. doi: 10.1093/biomet/90.4.777.

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.

Harvey, A. C. (2013). Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge University Press. doi: 10.1017/cbo9781139540933.

Harvey, A. C. (2022). Score-Driven Time Series Models. Annual Review of Statistics and Its Application, 9(1), 321–342. doi: 10.1146/annurev-statistics-040120-021023.

Harvey, A. C. and Chakravarty, T. (2008). Beta-t-(E)GARCH. Cambridge Working Papers in Economics, CWPE 0840. doi: 10.17863/cam.5286.

Harvey, A., Hurn, S., and Thiele, S. (2019). Modeling Directional (Circular) Time Series. Cambridge Working Papers in Economics, CWPE 1971. doi: 10.17863/cam.43915.

Holý, V. and Zouhar, J. (2022). Modelling Time-Varying Rankings with Autoregressive and Score-Driven Dynamics. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71(5). doi: 10.1111/rssc.12584.

Koopman, S. J. and Lit, R. (2019). Forecasting Football Match Results in National League Competitions Using Score-Driven Time Series Models. International Journal of Forecasting, 35(2), 797–809. doi: 10.1016/j.ijforecast.2018.10.011.

Koopman, S. J., Lit, R., Lucas, A., and Opschoor, A. (2018). Dynamic Discrete Copula Models for High-Frequency Stock Price Changes. Journal of Applied Econometrics, 33(7), 966–985. doi: 10.1002/jae.2645.

Koopman, S. J., Lucas, A., and Scharth, M. (2016). Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models. Review of Economics and Statistics, 98(1), 97–110. doi: 10.1162/rest_a_00533.