FP6Индивидуална стипендия2004–2005

SPATIAL-WC ECONOMET · SPATIAL AND WORST-CASE ECONOMETRIC TECHNIQUES, WITH APPLICATION TO MANAGEMENT MODELLING

6РП — Действия „Мария Кюри“

Период
2004-03-15 → 2005-03-14
Финансиране от ЕС
40 000 €
Участници
1
Схема
EIF

Линиите свързват координатора с партньорите.

Накратко на български

Економетричните техники тук анализират данни с пространствени характеристики и модели с невидими променливи, например при управлението на бизнес процеси. Тези методи помагат за по-точното предсказване на резултати и по-добрата оценка на рисковете при работа с непълна информация.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Final Activity Report Summary - SPATIAL-WC ECONOMET (spatial and worst-case econometric techniques, with application to management modelling)

The work carried out during this project was concerned with several lines of research. The first focused on worst-case econometrics, the second was devoted to statistics and econometrics for spatial data and the last one considered management modelling applications. Several research papers were submitted for publication in international peer-refereed journals and some were in progress by the time of the project completion. All research results gave rise to the following manuscripts: 1. ‘Worst-case estimation and asymptotic theory for models with unobservables’, with Mercedes Esteban-Bravo. This paper proposed a worst-case approach for estimating econometric models containing unobservable variables. Limiting theory was obtained and a Monte Carlo study of finite-sample properties was conducted. An economic application was also included. The manuscript keywords were unobservable variables, robust estimation, minimax optimisation, M-estimators and GMM estimators. The manuscript was submitted by the time of this report. 2. ‘Automatic nonparametric spectral density estimation for multilateral spatial processes’. This paper considered the nonparametric estimation of spectral densities for second order stationary spatio-temporal processes. We prove uniform consistency and asymptotic normality when the smoothing number was estimated from the sampled data. The paper keywords were spatial data, multilateral samples, edge effect, nonparametrics, spectral density and smoothing number. The paper was submitted by the time of the project completion. 3. ‘Nonparametric prediction for spatial data in the frequency domain: extrapolation and interpolation’. This paper considered the nonparametric prediction of second order stationary random fields using linear filters based on spectral analysis. We discussed extrapolation and interpolation problems and derived the asymptotic properties of the proposed estimators. The considered approach allowed data to spread in some or all space directions. The relevant keywords were random fields, spectral analysis, nonparametrics, prediction and interpolation. The paper was submitted by the time of this report. 4. ‘Spatial density in retailing and its impact on business performance’, with Mercedes Esteban-Bravo and J. M. Mugica. In this paper we offered an efficient and strong competition measurement which captured the interdependent nature of spatial relationships. We considered a spatial density metrics based on spatial point processes and nonparametric statistics. Our results confirmed that retail density had an impact on economic performance. Furthermore, this methodology outperformed the results inferred from data aggregated by a conventional postcode partitioning. The submitted paper keywords were retailing, spatial competition, spatial point process models and nonparametric estimation. 5. ‘Optimal duration of magazine promotions’, with Mercedes Esteban-Bravo and J. M. Mugica. On the basis of the expected economic return associated with dynamic response to stimuli, we determined the ideal length of marketing events using dynamic programming optimisation and applied the model to a complex promotion event. Relevant keywords were optimal duration of promotion events, Markovian process and dynamic programming. This paper was forthcoming in Marketing Letters by the time of the project completion. 6. ‘The value of a ‘free’ customer’, with Sunil Gupta and Carl Mela. The purpose of this paper was to develop an analytical model that would help us assess customer value in the presence of direct and indirect network effects. The paper keywords were indirect network effect, customer relations management and customer valuation. The theoretical results were completely established and an empirical application was considered by the time of the project completion. We expected to write up the results and submit the paper for its publication in the near future.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

This proposal is concerned with two research lines: spatial econometrics and worst-case estimation. Spatial Statistics is a set of techniques for the analysis of spatially located data.The main characteristic of spatial data is that the nearer observations tend to be more dependent. When these techniques are mainly concerned with economic problems, they are know as Spatial Econometrics. A large proportion of the Spatial Statistics work is appearing outside statistical journal (see e.g. Cresses, 1993 Preface). In fact, some inference techniques seem to be heuristic and could be improved. One of the aims of these research projects is to develop a wide range of statistical techniques for the analysis of spatial data, providing a formal mathematical basis for their use. This research project extends previous work of the applicant, supported by a Marie Curie Fellowship. The previous research focused on spatial data regularly sampled on a network. In this project we stress the estimation of random fields aggregated in small areas. An additional aim is the development of estimation techniques for econometric models with absence of empirical information about some of the involved variables. We propose a worst-case approach that is robust against the worst possible outcome of the unobserved variables. This methodology could be especially useful in management modelling, where firms often do not have available all the information required to estimate a decision model.

Оригинален текст от CORDIS (на английски).

Участници

  • UNIVERSIDAD CARLOS III DE MADRID · GETAFEКоординаторИспания

Връзки

Данни: CORDIS, © Европейски съюз