Ways of improving the efficiency of Monte-Carlo (MC) techniques are studied for dynamic models. Such models cause the conventional Antithetic Variate (AV) technique to fail, and will be proved to reduce the benefit from using Control Variates with nearly nonstationary series. This paper suggests modifications of the two conventional variance reduction techniques to enhance their efficiency. New classes of AVs are also proposed. Methods of reordering innovations are found to do less well than others which rely on changing some signs in the spirit of the traditional AV. Numerical and analytical calculations are given to investigate the features of the proposed techniques.

On efficient simulation in dynamic models

PARUOLO, PAOLO
2009-01-01

Abstract

Ways of improving the efficiency of Monte-Carlo (MC) techniques are studied for dynamic models. Such models cause the conventional Antithetic Variate (AV) technique to fail, and will be proved to reduce the benefit from using Control Variates with nearly nonstationary series. This paper suggests modifications of the two conventional variance reduction techniques to enhance their efficiency. New classes of AVs are also proposed. Methods of reordering innovations are found to do less well than others which rely on changing some signs in the spirit of the traditional AV. Numerical and analytical calculations are given to investigate the features of the proposed techniques.
2009
9780199237197
File in questo prodotto:
File Dimensione Formato  
Abadir_Paruolo_2009_OUP_ch-11.pdf

non disponibili

Tipologia: Documento in Post-print
Licenza: DRM non definito
Dimensione 420.48 kB
Formato Adobe PDF
420.48 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2829
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact