This paper considers a semiparametric panel data model with heterogeneous coefficients and individual-specific trending functions, where the random errors are assumed to be serially correlated and cross-sectionally dependent. We propose mean group estimators for the coefficients and trending functions involved in the model. It can be shown that the proposed estimators can achieve an asymptotic consistency with rates of root−N T and root−N T h, respectively as (N, T ) → (∞, ∞), where N is allowed to increase faster than T . Furthermore, a statistic for testing homogeneous coefficients is constructed based on the difference between the mean group estimator and a pooled estimator. Its asymptotic distributions are established under both the null and a sequence of local alternatives, even if the difference between these estimators vanishes considerably fast (can achieve root-N T 2 rate at most under the null) and consistent estimator available for the covariance matrix is not required explicitly. The finite sample performance of the proposed estimators together with the size and local power properties of the test are demonstrated by simulated data examples, and an empirical application with the OECD health care expenditure dataset is also provided.
This paper considers a semiparametric panel data model with heterogeneous coefficients and individual-specific trending functions, where the random errors are assumed to be serially correlated and cross-sectionally dependent. We propose mean group estimators for the coefficients and trending functions involved in the model. It can be shown that the proposed estimators can achieve an asymptotic consistency with rates of root−N T and root−N T h, respectively as (N, T) → (∞, ∞), where N is allowed to increase faster than T. Furthermore, a statistic for testing homogeneous coefficients is constructed based on the difference between the mean group estimator and a pooled estimator. Its asymptotic distributions are established under both the null and a sequence of local alternatives, even if the difference between these estimators vanishes considerably fast (can achieve root-N T 2 rate at most under the null) and consistent estimator available for the covariance matrix is not required explicitly. The finite sample performance of the proposed estimators together with the size and local power properties of the test are demonstrated by simulated data examples, and an empirical application with the OECD health care expenditure dataset is also provided.
This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement; more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing GMM methodology and develops Distance-type and LM-type tests for detecting a structural break, both when the breakpoint is known as well as when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, both in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002-2014.
How to achieve stable co-delivery of multiple phytochemicals is a common problem. This study focuses on the development, optimization and characterization of Huanglian-HouPo extract nanoemulsion (HLHPEN), with multiple components co-delivery, to enhance the anti-ulcerative colitis (UC) effects. The formulation of HLHPEN was optimized by pseudo-ternary phase diagram combined with Box-Behnken design. The physicochemical properties of HLHPEN were characterized, and its anti-UC activity was evaluated in DSS-induced UC mice model. Based on preparation process optimization, the herbal nanoemulsion HLHPEN was obtained, with the droplet size, PDI value, encapsulation efficiency (EE) for 6 phytochemicals (berberine, epiberberine, coptisine, bamatine, magnolol and honokiol) of 65.21 ± 0.82 nm, 0.182 ± 0.016, and 90.71 ± 0.21%, respectively. The TEM morphology of HLHPEN shows the nearly spheroidal shape of particles. The optimized HLHPEN showed a brownish yellow milky single-phase and optimal physical stability at 25 °C for 90 days. HLHPEN exhibited the good particle stability and gradual release of phytochemicals in SGF and SIF, to resist the destruction of simulated stomach and small intestine environment. Importantly, the oral administration of HLHPEN significantly restored the shrunk colon tissue length and reduced body weight, ameliorated DAI value and colon histological pathology, decreased the levels of inflammatory factors in DSS-induced UC mice model. These results demonstrated that HLHPEN had a significant therapeutic effect on DSS-induced UC mice, as a potential alternative UC therapeutic agent.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.