In Monte Carlo simulations, we illustrate the superiority of the proposed penalized estimation approach and argue that a combination of penalized and unpenalized estimation approaches results in overall best INAR model fits. For the data-driven selection of the penalization parameter, we propose two algorithms and evaluate their performance. Rinaldo: 2011, Autoregressive process modeling via the Lasso procedure. In addition, the TS-LASSO algorithm can also achieve the estimation and. Panel vector autoregressive (PVAR) models account for interdependencies and het- erogeneities across economies by jointly modeling multiple variables and. Keywords: ARDL, GARCH, sparse models, shrinkage, LASSO, adaLASSO, time series. This is the case, for example, in the frequently used INAR models with Poisson, negative binomially or geometrically distributed innovations. The large sample properties of the results for the variable-point estimation. Therefore, to improve the estimation accuracy, we propose a penalized version of the semiparametric estimation approach, which exploits the fact that the innovation distribution is often considered to be smooth, i.e. two consecutive entries of the PMF differ only slightly from each other. Al-Osh and Alzaid (1987) proposed the first-order integer-valued autoregressive (INAR) process Al-Osh and Alzaid (1988) proposed the integer-valued moving average (INMA) process and studied its various properties Zheng et al. Subset selection for vector autoregressive processes via adaptive lasso. However, for small sample sizes, the estimation performance of this semiparametric estimation approach may be inferior. In this regard, a semiparametric estimation approach is a remarkable exception which allows for estimation of the INAR models without any parametric assumption on the innovation distribution. Popular models for time series of count data are integer-valued autoregressive (INAR) models, for which the literature mainly deals with parametric estimation.
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