Nonparametric forecasting in time series: a comparative study

The problem of predicting a future value of a time series is considered in this paper. If the series follows a stationary Markov process, this can be done by nonparametric estimation of the autoregression function. Two forecasting algorithms are introduced. They only differ in the nonparametric kernel-type estimator used: the Nadaraya-Watson estimator and the local linear estimator. There are three major issues in the implementation of these algorithms: selection of the autoregressor variables; smoothing parameter selection and computing prediction intervals. These have been tackled using recent techniques borrowed from the nonparametric regression estimation literature under dependence. The performance of these nonparametric algorithms has been studied by applying them to a collection of 43 well-known time series. Their results have been compared to those obtained using classical Box-Jenkins methods. Finally, the practical behaviour of the methods is also illustrated by a detailed analysis of two data sets.Galicia. Consellería de Innovación, Industria e Comercio; PGIDT01PXI10505PRGalicia. Consellería de Innovación, Industria e Comercio; PGIDT03PXIC10505PNMinisterio de Ciencia y Tecnología; BMF2002-0026

A Comparative Analysis of the Efficiency of Romanian Banks

In this paper, we analyze the efficiency of the main banks in Romania, the Czech Republic and Hungary for the period 2000-2006, by using the frontier analysis. For the estimation of efficiency of banking we used a nonparametric method – the DEA Method (Data Envelopment Analysis) and a parametric method - the SFA Method (Stochastic Frontier Analysis). The results of the analyses show that the banks in the three East-European countries reach low levels of technical efficiency and cost efficiency, especially the ones in Romania, and that the main factors influencing the level of banks efficiency in these countries are: quality of assets; bank size, annual inflation rate; banking reform and interest rate liberalisation level and form of ownership.efficiency, banking, DEA method, SFA method

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Does the Box-Cox transformation help in forecasting macroeconomic time series?

The paper investigates whether transforming a time series leads to an improvement in forecasting accuracy. The class of transformations that is considered is the Box-Cox power transformation, which applies to series measured on a ratio scale. We propose a nonparametric approach for estimating the optimal transformation parameter based on the frequency domain estimation of the prediction error variance, and also conduct an extensive recursive forecast experiment on a large set of seasonal monthly macroeconomic time series related to industrial production and retail turnover. In about one fifth of the series considered the Box-Cox transformation produces forecasts significantly better than the untransformed data at one-step-ahead horizon; in most of the cases the logarithmic transformation is the relevant one. As the forecast horizon increases, the evidence in favour of a transformation becomes less strong. Typically, the na¨ıve predictor that just reverses the transformation leads to a lower mean square error than the optimal predictor at short forecast leads. We also discuss whether the preliminary in-sample frequency domain assessment conducted provides a reliable guidance which series should be transformed for improving significantly the predictive performance.Forecasts comparisons; Multi-step forecasting; Rolling forecasts; Nonparametric estimation of prediction error variance.

ILR Research in Progress 2011-12

The production of scholarly research continues to be one of the primary missions of the ILR School. During a typical academic year, ILR faculty members published or had accepted for publication over 25 books, edited volumes, and monographs, 170 articles and chapters in edited volumes, numerous book reviews. In addition, a large number of manuscripts were submitted for publication, presented at professional association meetings, or circulated in working paper form. Our faculty's research continues to find its way into the very best industrial relations, social science and statistics journals.Research_in_Progress_2011_12.pdf: 46 downloads, before Oct. 1, 2020

The mortality of the Italian population: Smoothing techniques on the Lee--Carter model

Several approaches have been developed for forecasting mortality using the stochastic model. In particular, the Lee-Carter model has become widely used and there have been various extensions and modifications proposed to attain a broader interpretation and to capture the main features of the dynamics of the mortality intensity. Hyndman-Ullah show a particular version of the Lee-Carter methodology, the so-called Functional Demographic Model, which is one of the most accurate approaches as regards some mortality data, particularly for longer forecast horizons where the benefit of a damped trend forecast is greater. The paper objective is properly to single out the most suitable model between the basic Lee-Carter and the Functional Demographic Model to the Italian mortality data. A comparative assessment is made and the empirical results are presented using a range of graphical analyses.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS394 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

On the prediction of stationary functional time series

This paper addresses the prediction of stationary functional time series. Existing contributions to this problem have largely focused on the special case of first-order functional autoregressive processes because of their technical tractability and the current lack of advanced functional time series methodology. It is shown here how standard multivariate prediction techniques can be utilized in this context. The connection between functional and multivariate predictions is made precise for the important case of vector and functional autoregressions. The proposed method is easy to implement, making use of existing statistical software packages, and may therefore be attractive to a broader, possibly non-academic, audience. Its practical applicability is enhanced through the introduction of a novel functional final prediction error model selection criterion that allows for an automatic determination of the lag structure and the dimensionality of the model. The usefulness of the proposed methodology is demonstrated in a simulation study and an application to environmental data, namely the prediction of daily pollution curves describing the concentration of particulate matter in ambient air. It is found that the proposed prediction method often significantly outperforms existing methods

Performance of short-term trend predictors for current economic analysis

We study the performance of several short-term trend estimators for current economic analysis. These estimators are available in X11-ARIMA, X12-ARIMA, TRAMO-SEATS and STAMP. We also include two other trend-cycle estimators obtained by post-processing seasonally adjusted data with X11ARIMA, namely, a modified Henderson nonlinear filter by Dagum (1996) DMH, and a new modified version of it, DMH-D. The estimators are applied to a number of simulated non-seasonal data of various levels of variability.