Statistical Issues in Macroeconomic Modelling
Working paper
Published version
Permanent lenke
http://hdl.handle.net/11250/2500396Utgivelsesdato
2000Metadata
Vis full innførselSamlinger
Sammendrag
The paper describes the influx of mathematical statistics in economics. It focuses on an approach to macroeconometric modelling which is based on fundamental statistical concepts like the joint distribution function of all observable variables for the whole sample period. The methodology relies on valid conditioning and marginalisation of this function in order to arrive at tractable subsystems, which can be analysed with statistical methods. Two case studies - the modelling of the household sector and the modelling of wages and prices in the Norges Bank RIMINI model - highlight this.
Utgiver
Norges BankSerie
Working Papers;12/2000
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Beslektede innførsler
Viser innførsler beslektet ved tittel, forfatter og emneord.
-
Modelling Inflation in the Euro Area
Jansen, Eilev S. (Working Papers;10/2004, Working paper, 2004)The paper presents an incomplete competition model (ICM), where inflation is determined jointly with unit labour cost growth. The ICM is estimated on data for the Euro area and evaluated against existing models, i.e. the ... -
Term Structure Forecasting Using Macro Factors and Forecast Combination
de Pooter, Michiel; Ravazzolo, Francesco; van Dijk, Dick (Working Papers;1/2010, Working paper, 2010)We examine the importance of incorporating macroeconomic information and, in particular, accounting for model uncertainty when forecasting the term structure of U.S.interest rates. We start off by analyzing and comparing ... -
Combining VAR and DSGE Forecast Densities
Bache, Ida Wolden; Jore, Anne Sofie; Mitchell, James; Vahey, Shaun P. (Working Papers;23/2009, Working paper, 2009)A popular macroeconomic forecasting strategy takes combinations across many models to hedge against instabilities of unknown timing; see (among others) Stock and Watson (2004), Clark and McCracken (2010), and Jore et al. ...