Asta-Advances in Statistical Analysis最新文献

This paper considers a partially linear model in which the covariates of parametric part are measured with normal distributed errors. A newly robust corrected empirical likelihood procedure based on the corrected score function is proposed to attenuate the effects of measurement errors as well as outliers. What’s more, profit from the QR decomposition technique, the parametric and nonparametric components of the models can be estimated separately. The asymptotic properties of the proposed robust corrected empirical likelihood approach are established under some regularity conditions. Simulation studies are demonstrated to show that our proposed method performs well in finite samples. Boston housing price data are applied to illustrate the proposed estimation procedure.

We consider random coefficient INAR(1) processes with a strongly dependent latent random coefficient process. It is shown that, in spite of its conditional Markovian structure, the unconditional process exhibits long-range dependence. Short-term prediction and estimation of parameters involved in the prediction are considered. Asymptotic rates of convergence are derived.

The paper discusses the problem of estimating group heterogeneous fixed-effect panel data models under the assumption of fuzzy clustering, that is each unit belongs to all the clusters with a membership degree. To enhance spatial clustering, a spatio-temporal approach is considered. An iterative procedure, alternating panel data estimation and spatio-temporal clustering of the residuals, is proposed. The proposed method can be of relevance to researchers interested in using fuzzy group fixed-effect methods, but want to leverage spatial dimension for clustering units. Two empirical examples, the first on cigarette consumption in the US states and the second on non-life insurance demand in Italy, are presented to illustrate the performance of the proposed approach. The spatial fuzzy GFE model reveals important regional differences in both the US cigarette consumption and non-life insurance determinants in Italy. In the case of the US, we found a distinction in two main clusters, East and West. For the Italy provinces data, we find a distinction in North and South clusters. Regarding the regression results, for cigarette consumption data, different from the previous studies, we find that the smuggling effect is significant only in east regions, thus suggesting localised impacts of bootlegging. In the context of Italian non-life insurance demand, we find that while population density explains insurance consumption in northern provinces, the trust issues in the south explain the lower insurance demand.

This study investigates the spatial clustering and spillover effects of COVID-19 vaccine uptake in Romania, focusing on the municipality-level distribution of vaccine acceptance and hesitancy while considering the factors that influence it. The research uses the Spatial Durbin Error Model (SDEM) and identifies spatial clusterization, as well as significant contagion and diffusion processes in the vaccination behaviour conditioned by socioeconomic factors, labour market characteristics, social and religious attitudes, urban, and health indicators. We find evidence in favour of spatial spillover effects of the poverty rate, opposition to same-sex marriage, COVID-19 infection rate, peri-urban towns, and denser cities. Our findings contribute to the literature of the spatial distribution and determinants of vaccine uptake and carry practical implications for policy makers offering evidence-based insights that can inform targeted strategies and interventions to enhance vaccine acceptance and address hesitancy in specific locations.

In this paper, we propose an integrated modified harmonic mean estimator (IHME) for nested and non-nested model selection problems in spatial panel data models with entity and time fixed effects. We formulate the IHME based on the integrated likelihood functions obtained by analytically integrating out the high-dimensional entity and time fixed effects from the complete likelihood functions. To investigate the finite sample properties of the IHME, we design a comprehensive simulation study that allows for both nested and non-nested model selection exercises in some popular spatial panel data models. Our simulation results show that the IHME has excellent finite sample performance and outperforms some competing estimators in terms of precision. We provide an empirical application on the US house price changes to show the usefulness of the proposed IHME in a model selection exercise.

The primary purpose of this paper is to assess households’ burden due to out-of-pocket healthcare expenditures. These payments are modeled on a representative sample of 25668 Italian households as the fraction of out-of-pocket healthcare expenditures over the households’ capacity to pay. For this purpose, we propose extending the analysis of the so-called catastrophic payments by looking at the entire distribution of this ratio. We introduce a novel finite mixture regression able to capture different levels of heterogeneity in the data. By using such a model specification, the fairness of the Italian National Health Service and its determinants are investigated.

In this work, the distributional properties of the goodness-of-fit term in likelihood-based information criteria are explored. These properties are then leveraged to construct a novel goodness-of-fit test for normal linear regression models that relies on a nonparametric bootstrap. Several simulation studies are performed to investigate the properties and efficacy of the developed procedure, with these studies demonstrating that the bootstrap test offers distinct advantages as compared to other methods of assessing the goodness-of-fit of a normal linear regression model. Our inferential technique can be employed using the DBModelSelect R package, available freely via the Comprehensive R Archive Network.

This paper compares the ability of several econometric and machine learning methods to nowcast GDP in (pseudo) real-time. The analysis takes the example of Dutch GDP over the period 1992Q1–2018Q4 using a broad data set of monthly indicators. It discusses the forecast accuracy but also analyzes the use of information from the large data set of macroeconomic and financial predictors. We find that, on average, the random forest provides the most accurate forecast and nowcasts, whilst the dynamic factor model provides the most accurate backcasts.

This research proposes a method to test and estimate change points in the covariance structure of high-dimensional multivariate series data. Our method uses the trace of the beta matrix, known as Pillai’s statistics, to test the change in covariance matrix at each time point. We study the asymptotic normality of Pillai’s statistics for testing the equality of two covariance matrices when both sample size and dimension increase at the same rate. We test the existence of a single change point in a given time period using Cauchy combination test, the test using an weighted sum of Cauchy transformed p-values, and estimate the change point as the point whose statistic is the greatest. To test and estimate multiple change points, we use the idea of the wild binary segmentation and repeatedly apply the procedure for a single change point to each segmented period until no significant change point exists. We numerically provide the size and power of our method. We finally apply our procedure to finding abnormal behavior in the investment of a private equity fund.

Questionnaires are useful tool for exploring respondents’ perceptions through ratings, assumed to result from a latent decision process (DP). The DP varies when respondents rate on Likert or Semantic Differential scales. A possible paradigm to formalize the DP is based on the presence of a feeling and an uncertainty latent component, originally proposed as the foundations of the CUB (Combination of Uniform and shifted Binomial) class. It can be assumed that with Likert scales, respondents begin reasoning from the bottom, progressing upwards based on their sensations. Conversely, Semantic Differential scale users are assumed to start from the middle and move either upward or downward. The CUM (Combination of Uniform and Multinomial), a new model in the CUB class, derived from this DP, analyzes rating data on a Semantic Differential scale. This paper defines the concept of local and global unidirectional equivalence and studies, from an analytical point of view, the conditions under which CUB and CUM models generate identical theoretical probabilities, in order to enhance the interpretative understanding of the models.