Asta-Advances in Statistical Analysis最新文献

This study proposes a comparison of hedonic pricing models that use attributes obtained by featurizing text. We collected prices of items sold on the websites of five famous fashion producers in order to estimate hedonic pricing models that leverage the information contained in product descriptions. We mapped product descriptions to a high-dimensional feature space and compared predictive accuracy and variable selection properties of some statistical estimators that leverage sparse modelling, topic modelling and aggregated predictors, to test whether better predictive accuracy comes with an empirically consistent selection of attributes. We call this approach Hedonic Text-Regression modelling. Its novelty is that by using attributes obtained by text-mining of product descriptions, we obtain an estimate of the implicit price of the words contained therein. Empirically, all the proposed models outperformed the traditional hedonic pricing model in terms of predictive accuracy, while also providing consistent variable selection.

The aim of this paper is to estimate the effects of seeking medical care on missing work. Specifically, our case study explores the question: Does visiting a medical provider cause an employee to miss work? To address this, we employ a model that can consistently estimate the impacts of two endogenous binary regressors. The model is based on three equations connected via a multivariate Gaussian distribution, which makes it possible to model the correlations among the equations, hence accounting for unobserved heterogeneity. Parameter estimation is reliably carried out via a trust region algorithm with analytical derivative information. We find that, observationally, having a curative visit associates with a nearly 80% increase in the probability of missing work, while having a preventive visit correlates with a smaller 13% increase in the likelihood of missing work. However, after addressing potential endogeneity, neither type of visit appears to significantly relate to missing work. That finding also applies to visits that occur during the previous year. Therefore, we conclude that the observed links between medical usage and absenteeism derive from unobserved heterogeneity, rather than direct causal channels. The modeling framework is available through the R package GJRM.

We consider a linear measurement error model (MEM) with AR(1) process in the state equation which is widely used in applied research. This MEM could be equivalently re-written as ARMA(1,1) process, where the MA(1) parameter is related to the variance of measurement errors. As the MA(1) parameter is of essential importance for these linear MEMs, it is of much relevance to provide instruments for online monitoring in order to detect its possible changes. In this paper we develop control charts for online detection of such changes, i.e., from AR(1) to ARMA(1,1) and vice versa, as soon as they occur. For this purpose, we elaborate on both cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts and investigate their performance in a Monte Carlo simulation study. The empirical illustration of our approach is conducted based on time series of daily realized volatilities.

We consider a sieve bootstrap procedure to quantify the estimation uncertainty of long-memory parameters in stationary functional time series. We use a semiparametric local Whittle estimator to estimate the long-memory parameter. In the local Whittle estimator, discrete Fourier transform and periodogram are constructed from the first set of principal component scores via a functional principal component analysis. The sieve bootstrap procedure uses a general vector autoregressive representation of the estimated principal component scores. It generates bootstrap replicates that adequately mimic the dependence structure of the underlying stationary process. We first compute the estimated first set of principal component scores for each bootstrap replicate and then apply the semiparametric local Whittle estimator to estimate the memory parameter. By taking quantiles of the estimated memory parameters from these bootstrap replicates, we can nonparametrically construct confidence intervals of the long-memory parameter. As measured by coverage probability differences between the empirical and nominal coverage probabilities at three levels of significance, we demonstrate the advantage of using the sieve bootstrap compared to the asymptotic confidence intervals based on normality.

In this paper, we compute closed-form expressions of moments and comoments for the CIR process which allows us to provide a new construction of the transition probability density based on a moment argument that differs from the historic approach. For Bates’ model with stochastic volatility and jumps, we show that finite difference approximations of higher moments such as the skewness and the kurtosis are unstable and, as a remedy, provide exact analytic formulas for log-returns. Our approach does not assume a constant mean for log-price differentials but correctly incorporates volatility resulting from Ito’s lemma. We also provide R, MATLAB, and Mathematica modules with exact implementations of the theoretical conditional and unconditional moments. These modules should prove useful for empirical research.

Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest possible part of the total variance of the observed variables. Several methodologies have been proposed either to improve the interpretation of PCA results (e.g., by means of orthogonal, oblique rotations, shrinkage methods), or to model oblique components or factors with a hierarchical structure, such as in Bi-factor and High-Order Factor analyses. In this paper, we propose a new methodology, called Hierarchical Disjoint Principal Component Analysis (HierDPCA), that aims at building a hierarchy of disjoint principal components of maximum variance associated with disjoint groups of observed variables, from Q up to a unique, general one. HierDPCA also allows choosing the type of the relationship among disjoint principal components of two sequential levels, from the lowest upwards, by testing the component correlation per level and changing from a reflective to a formative approach when this correlation turns out to be not statistically significant. The methodology is formulated in a semi-parametric least-squares framework and a coordinate descent algorithm is proposed to estimate the model parameters. A simulation study and two real applications are illustrated to highlight the empirical properties of the proposed methodology.

We introduce a selection model-based imputation approach to be used within the Fully Conditional Specification (FCS) framework for the Multiple Imputation (MI) of incomplete ordinal variables that are supposed to be Missing Not at Random (MNAR). Thereby, we generalise previous work on this topic which involved binary single-level and multilevel data to ordinal variables. We apply an ordered probit model with sample selection as base of our imputation algorithm. The applied model involves two equations that are modelled jointly where the first one describes the missing-data mechanism and the second one specifies the variable to be imputed. In addition, we develop a version for hierarchical data by incorporating random intercept terms in both equations. To fit this multilevel imputation model we use quadrature techniques. Two simulation studies validate the overall good performance of our single-level and multilevel imputation methods. In addition, we show its applicability to empirical data by applying it to a common research topic in educational science using data of the National Educational Panel Study (NEPS) and conducting a short sensitivity analysis. Our approach is designed to be used within the R software package mice which makes it easy to access and apply.

The evaluation of the possible effects of a treatment on an outcome plays a central role in both theoretical and applied statistical and econometrical literature. This paper focuses on nonparametric tests for possible difference in the distribution of potential outcomes due to receiving or not receiving a treatment. The approach is based on weighting observed data on the basis on the estimated propensity score. Kolmogorov–Smirnov type and Wilcoxon–Mann–Whitney type tests are constructed, and their limiting distributions are studied. Rejection regions are obtained by inverting confidence intervals. This involves the study of appropriate estimators of the limiting variance of test statistics. Approximations of quantiles via subsampling are also considered. The merits of the different tests are studied by Monte Carlo simulation. An application to the construction of tests for stochastic dominance is provided.

Within the stochastic approach, this paper establishes a closed-form solution to the price index problem for an arbitrary number of periods or countries. The index’s reference basket merges the intersections of all couples of baskets in all periods/countries and provides an effective commodity coverage. Under spherical regression errors, the index satisfies the Geary–Khamis equation system and, as such, offers a general and compact representation of the latter as well as the inferential framework as a dowry. Furthermore, by relaxing sphericalness in favor of a more realistic assumption of commodity-dependent variances, a broader result is achieved. The solution to the price index problem thus obtained encompasses the Geary–Khamis formulation and sows the seeds to further advances.