Econometrics and Statistics最新文献

Binary classification is an important issue in many applications but mostly studied for independent data in the literature. A binary time series classification is investigated by proposing a semiparametric procedure named “Model Averaging nonlinear MArginal LOgistic Regressions” (MAMaLoR) for binary time series data based on the time series information of predictor variables. The procedure involves approximating the logistic multivariate conditional regression function by combining low-dimensional non-parametric nonlinear marginal logistic regressions, in the sense of Kullback-Leibler distance. A time series conditional likelihood method is suggested for estimating the optimal averaging weights together with local maximum likelihood estimations of the nonparametric marginal time series logistic (auto)regressions. The asymptotic properties of the procedure are established under mild conditions on the time series observations that are of $\beta$-mixing property. The procedure is less computationally demanding and can avoid the “curse of dimensionality” for, and be easily applied to, high dimensional lagged information based nonlinear time series classification forecasting. The performances of the procedure are further confirmed both by Monte-Carlo simulation and an empirical study for market moving direction forecasting of the financial FTSE 100 index data.

The identification and estimation of conditional quantile functions for count responses using longitudinal data are considered. The approach is based on a continuous approximation to distribution functions for count responses within a class of parametric models that are commonly employed. It is first shown that conditional quantile functions for count responses are identified in zero-inflated models with subject heterogeneity. Then, a simple three-step approach is developed to estimate the effects of covariates on the quantiles of the response variable. A simulation study is presented to show the small sample performance of the estimator. Finally, the advantages of the proposed estimator in relation to some existing methods is illustrated by estimating a model of annual visits to physicians using data from a health insurance experiment.

Autoregressive models are reviewed for the analysis of multivariate count time series. A particular topic of interest which is discussed in detail is that of the choice of a suitable distribution for a vectors of count random variables. The focus is on three main approaches taken for multivariate count time series analysis: (a) integer autoregressive processes, (b) parameter-driven models and (c) observation-driven models. The aim is to highlight some recent methodological developments and propose some potentially useful research topics.

The low-rank approximation problem, that is the problem of approximating a given matrix with a matrix of lower rank, appears in many scientific fields. In some applications the given matrix is structured and the approximation is required to have the same structure. Examples of linear structures are Hankel, Toeplitz, and Sylvester. Currently, there are only a few results for nonlinearly structured low-rank approximation problems. The problem of Vandermonde structured low-rank approximation is considered. The high condition number of the Vandermonde matrix, in combination with the noise in the data, makes the problem challenging. A numerical method based on a bias correction procedure is proposed and its properties are demonstrated by simulation. The performance of the method is illustrated on numerical results.

In an extensive pseudo out-of-sample horserace, classical estimators (dynamic factor models, RIDGE and partial least squares regression) and the novel to forecasting, Regularized Sliced Inverse Regression, exhibit almost near-equivalent forecasting accuracy in a large panel of macroeconomic variables across targets, horizons and subsamples. This finding motivates the theoretical contributions in this paper. Most widely used linear dimension reduction methods are shown to solve closely related maximization problems with solutions that can be decomposed in signal and scaling components. They are organized under a common scheme that sheds light on their commonalities and differences as well as on their functionality. Regularized Sliced Inverse Regression delivers the most parsimonious forecast model and obtains the greatest reduction of the complexity of the forecasting problem. Nevertheless, the study’s findings are that (a) the intrinsic relationship between forecast target and the other macroseries in the panel is linear and (b) targeting contributes in reducing the complexity of modeling yet does not induce significant gains in macroeconomic forecasting accuracy.

The sum of a random number of independent and identically distributed random vectors has a distribution which is not analytically tractable, in the general case. The problem has been addressed by means of asymptotic approximations embedding the number of summands in a stochastically increasing sequence. Another approach relies on fitting flexible and tractable parametric, multivariate distributions, as for example finite mixtures. Both approaches are investigated within the framework of Edgeworth expansions. A general formula for the fourth-order cumulants of the random sum of independent and identically distributed random vectors is derived and it is shown that the above mentioned asymptotic approach does not necessarily lead to valid asymptotic normal approximations. The problem is addressed by means of Edgeworth expansions. Both theoretical and empirical results suggest that mixtures of two multivariate normal distributions with proportional covariance matrices satisfactorily fit data generated from random sums where the counting random variable and the random summands are Poisson and multivariate skew-normal, respectively.

Functional magnetic resonance imaging (fMRI) is one of the most popular neuroimaging technologies used in human brain studies. However, fMRI data analysis faces several challenges, including intensive computation due to the massive data size and large estimation errors due to a low signal-to-noise ratio of the data. A new statistical model and a computational algorithm are proposed to address these challenges. Specifically, a new multi-subject general linear model is built for stimulus-evoked fMRI data. The new model assumes that brain responses to stimuli at different brain regions of various subjects fall into a low-rank structure and can be represented by a few principal functions. Therefore, the new model enables combining data information across subjects and regions to evaluate subject-specific and region-specific brain activity. Two optimization functions and a new fast-to-compute algorithm are developed to analyze multi-subject stimulus-evoked fMRI data and address two research questions of a broad interest in psychology: evaluating every subject’s brain responses to different stimuli and identifying brain regions responsive to the stimuli. Both simulation and real data analysis are conducted to show that the new method can outperform existing methods by providing more efficient estimates of brain activity.