Estimation methods comparison of SVAR model with the mixture of two normal distributions - Monte Carlo analysis

This paper addresses the issue of obtaining maximum likelihood estimates of parameters for structural VAR models with a mixture of distributions. Hence the problem does not have a closed form solution, numerical optimization procedures need to be used. A Monte Carlo experiment is design to compare the performance of four maximization algorithms and two estimation strategies. It is shown that the EM algorithm outperforms the general maximization algorithms such as BFGS, NEWTON and BHHH. Moreover simplification of the probelm introduced in the two steps quasi ML method does not worsen small sample properties of the estimators and therefore may be recommended in the empirical analysis.Structural vetcor autoregression , Error correction models, Mixed normal, Monte Carlo

Identification and Estimation of Sources of Common Fluctuations: New methodologies and applications.

This thesis addresses the problem of how to identify and model sources of common fluctuations of economic variables. It is an interesting question not only for researchers but also for policy makers and other authorities. The literature presents two approaches. The first one is based on an assumption that the important structural shocks can be captured by a small set of macroeconomic variables. The most popular models used in this context are structural vector autoregression models (SVAR). The second approach follows from a belief that there exists a small number of factors that affect many economic processes. Therefore, it involves analysis of large data sets, with both time and cross- sectional dimensions large enough to describe the factor structure. We dedicate the first part of the thesis to the problem of identification and estimation of structural shocks in small SVAR models. We follow the ideas of Rigobon (2003) and Lanne and Lütkepohl (2008), which show that the statistical property of the data may provide enough information to identify the structure of the model. The papers argue that a shift in the error covariance matrix allows for the estimation of the structural parameters of interest. The literature concentrates on models in which the shift is a result of a structural brake or a mixed distribution of errors.Business cycles; Instrumental variables (Statistics); Economics -- Statistical models;

Structural Vector Autoregressions with Markov Switching

It is argued that in structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. The model setup is formulated and discussed and it is shown how it can be used to test restrictions which are just-identifying in a standard structural vector autoregressive analysis. The approach is illustrated by two SVAR examples which have been reported in the literature and which have features which can be accommodated by the MS structure.Cointegration, Markov regime switching model, vector error correction model, structural vector autoregression, mixed normal distribution

Common factors in nonstationary panel data with a deterministic trend - estimation and distribution theory

The paper studies large-dimention factor models with nonstationary factors and allows for deterministic trends and factors integrated of order higher then one.We follow the model speci.cation of Bai (2004) and derive the convergence rates and the limiting distributions of estimated factors, factors loadings and common components. We discuss in detail a model with a linear time trend. We ilustrate the theory with an empirical exmple that studies the fluctuations of the real activity of U.S.economy. We show that these .uctuationas can be explained by two nonstationary factors and a small number of stationary factors. We test the economic interpretation of nonstationary factors.Common-stochastic trends; Dynamic factors; Generalized dynamic factor models; Principal components; Nonstationary panel data

Assessing the number of components in a normal mixture: an alternative approach

In this article, a new approach for model specification is proposed. The method allows to choose the correct order of a mixture model by testing, if a particular mixture component is significant. The hypotheses are set in a new way, in order to avoid identification problems, which are typical for mixture models. If some of the parameters are known, the distribution of the LR statistic is Chi2, with the degrees of freedom depending on the number of components and the number of parameters in each component. The advantage of the new approach is its simplicity and computational feasibility

Assessing the number of components in a normal mixture: an alternative approach

In this article, a new approach for model specification is proposed. The method allows to choose the correct order of a mixture model by testing, if a particular mixture component is significant. The hypotheses are set in a new way, in order to avoid identification problems, which are typical for mixture models. If some of the parameters are known, the distribution of the LR statistic is Chi2, with the degrees of freedom depending on the number of components and the number of parameters in each component. The advantage of the new approach is its simplicity and computational feasibility

Long-term cenobamate retention, efficacy, and safety: outcomes from Expanded Access Programme

Aim of the study. To evaluate the long-term retention rate, efficacy, and tolerability of adjunctive cenobamate (CNB) in patients with drug-resistant epilepsy within the Polish Expanded Access Programme (EAP). Clinical rationale for the study. Long-term retention rate is a useful measure of effectiveness including efficacy, safety, and tolerability of antiseizure medications. Material and methods. We conducted a multicentre retrospective analysis of consecutive patients with focal epilepsy treated with CNB in the EAP between January 2020 and May 2023. All patients who completed the open-label extension phases of the YKP3089C013 and YKP3089C017 trials were offered the opportunity to continue CNB treatment within the EAP. We analysed cenobamate retention, seizure outcomes, and adverse events. Results. 38 patients (18 females; 47.3%) continued CNB treatment within the Expanded Access Programme for 41 months. The mean baseline age of patients was 39.3 years (range: 18–57). All patients were on polytherapy, with the most commonly used antiseizure medications being valproate, levetiracetam, and carbamazepine. Adjunctive CNB treatment resulted in a reduced mean seizure frequency from 8.1 seizures (range: 4-20) per month to 3 seizures (range: 0–8) per month. At the final follow-up, the median CNB dose was 200 mg/day (range: 50–350). Among the patients, 24 (63.1%) achieved ≥ 50% seizure reduction, and eight (21%) remained seizure-free for at least 12 months. One in three patients experienced adverse events, which resolved in half of the subjects. The most frequent adverse events were dizziness, somnolence, and headache. The retention rate after completing the open-label extension phase was 100%. Conclusions and clinical implications. Long-term effectiveness, including ≥ 50% seizure reduction and a 100% retention rate, was sustained over 41 months of CNB treatment within the Expanded Access Programme. No new safety issues were identified. These results provide support for the potential long-term clinical benefits of cenobamate