Forecasting with large datasets: A time varying covariance matrix

Información del proyecto

FORECASTING

Identificador del acuerdo de subvención: 708501

DOI

10.3030/708501

Proyecto cerrado

Fecha de la firma de la CE 31 Marzo 2016

Fecha de inicio 6 Septiembre 2016

Fecha de finalización 5 Septiembre 2018

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 151 648,80

Aportación de la UE

€ 151 648,80

Coordinado por

UNIVERSITY OF CYPRUS
Cyprus

Objetivo

Recently there has been considerable focus on methods that enable time varying estimation of parameters in econometric models in the presence of, possibly stochastic, structural change. An interesting strand of this literature dispenses with the, computationally expensive and theoretically unclear, standard Bayesian estimation methods in favour of kernel estimation. In this project we intend to extend that strand of the literature to the case of large dimensional datasets and perhaps the most commonly explored problem of covariance estimation. Our primary focus will be to combine kernel estimation with fixed coefficient estimation methods for large dimensional covariance matrices. We will then try to provide theoretical results that allow for time variation in the large data generating process. This is a novel extension in the literature. To strengthen our theoretical results, we aim to provide an extensive Monte Carlo analysis and illustrate the utility of our methods in terms of out of sample forecasting. The proposed estimators have many interesting empirical applications. On top of the theoretical paper, our aim is to provide, two empirical papers, in the area of Macroeconomic Forecasting and Optimal portfolio allocation. To this end, we will use the proposed estimators, to forecast key macro variables with large dimensional linear regression, and compare with similar, data rich methods, that are currently used in the literature. Finally we will combine our methodological advancements with optimal portfolio allocation theories. Our preliminary empirical results show that the benefits from the proposed estimators are expected to be high and significant.

Ámbito científico (EuroSciVoc)

• ciencias sociales › economía y empresa › ciencia económica › econometría

Programa(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions PROGRAMA PRINCIPAL
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Tema(s)

• MSCA-IF-2015-EF - Marie Skłodowska-Curie Individual Fellowships (IF-EF)
  

Régimen de financiación

MSCA-IF-EF-ST - Standard EF

Convocatoria de propuestas

H2020-MSCA-IF-2015

Coordinador

UNIVERSITY OF CYPRUS

• Aportación neta de la UEn: € 151 648,80
• Dirección: AVENUE PANEPISTIMIOU 2109 AGLANTZI ; 1678 Nicosia ; Chipre
• Región: Κύπρος › Κύπρος › Κύπρος
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: € 151 648,80