We consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. To overcome the innate endogeneity, we propose a generalized Yule–Walker estimation method which applies the least squares estimation to a Yule–Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations (or panels) tend to infinity under a general setting for stationary and αα-mixing processes, which includes spatial autoregressive panel data models driven by i.i.di.i.d. innovations as special cases. The proposed methods are illustrated using both simulated and real data

Over the last decade, the assessment of university teaching quality has assumed a prominent role in the university system with the main purpose of improving the quality of courses offered to students. As a result of this process, a host of studies on the evaluation of university teaching was devoted to the Italian system, covering different topics and considering case studies and methodological issues. Based upon this debate, the contribution aims to present an integrated strategy of analysis which combines both descriptive and model-based methods for the treatment of student evaluation of teaching data. More specifically, the joint use of item response theory and multilevel models allows, on the one hand, to compare courses’ ranking based on different indicators and, on the other hand, to define a model-based approach for building up indicators of overall students’ satisfaction, while adjusting for their characteristics and differences in the compositional variables across courses. The usefulness and the relative merits of the proposed procedure are discussed within a real data set

We propose an adaptive smoothing method for nonparamet-\ud ric regression. The central idea of the proposed method is to\ud “calibrate” the estimated function through an adaptive bandwidth function, which is a kind of intermediate solution between the global bandwidth (constant on\ud the support) and the local bandwidth (variable with x\ud ). This also allows to correct the bias of the local polynomial estimator, with some benefits for the inference based on such estimators. Our method, which uses the Neural Network technique in a preliminary (pilot) stage, is based on a rolling,\ud plug-in, bandwidth selection procedure. It automatically\ud reaches a trade-off between the efficiency of global smoothing and the adaptability of local smoothing. The consistency and the optimal convergence rate of the resulting bandwidth estimators are shown theoretically. A simulation study shows the performance of our method for finite sample size

The local polynomial estimator is particularly affected by the curse of dimensionality, which reduces the potential of this tool for large-dimensional applications. We propose an estimation procedure based on the local linear estimator and a sparseness condition that focuses on nonlinearities in the model. Our procedure, called BID (bias inflation--deflation), is automatic and easily applicable to models with many covariates without requiring any additivity assumption. It is an extension of the RODEO method, and introduces important new contributions: consistent estimation of the multivariate optimal bandwidth (the tuning parameter of the estimator); consistent estimation of the multivariate bias-corrected regression function and confidence bands; and automatic identification and separation of nonlinear and linear effects. Some theoretical properties of the method are discussed. In particular, we show the nonparametric oracle property. For linear models, BID automatically reaches the optimal rate $O_p(n^{-1/2})$, equivalent to the parametric case. A simulation study shows the performance of the procedure for finite samples

Clustering methods for time series have been widely studied and applied within a range of different fields. They are generally based on the choice of a relevant metric. The aim of this paper is to propose and discuss a clustering technique in the frequency domain for stationary time series. The idea of the new procedure consists in analyzing the discrete component of the spectrum, avoiding the introduction of any metric for the classification of the time series. The novel technique is suitable for time series that show strong periodic components and is based on an efficient algorithm requiring less computational and memory resources, making it appropriate for large and complex temporal databases. The problem of the selection of the optimal partition is also addressed along with a proposal that takes into account the stability of the clusters and the efficiency of the procedure in classifying the time series among the different groups. The results of a simulation study show the relative merits of the proposed procedure compared to other spectral-based approaches. An application to a large time-series database provided by a big electric company is also discussed. The application showed the good performance of the proposed technique, which was able to classify the time series in a few groups of customers with homogeneous electricity demand patterns