 
      Statistics, Econometrics and Forecasting e-bog
        
        
        948,41 DKK
        
        (inkl. moms 1185,51 DKK)
        
        
        
        
      
      
      
      Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate time series forecasting models and dynamic, time...
        
        
      
            E-bog
            948,41 DKK
          
          
        
    Forlag
    Cambridge University Press
  
  
  
    Udgivet
    15 maj 2006
    
  
  
  
  
    Genrer
    
      Econometrics and economic statistics
    
  
  
  
  
    Sprog
    English
  
  
    Format
    pdf
  
  
    Beskyttelse
    LCP
  
  
    ISBN
    9780511189562
  
Based on two lectures presented as part of The Stone Lectures in Economics series, Arnold Zellner describes the structural econometric time series analysis (SEMTSA) approach to statistical and econometric modeling. Developed by Zellner and Franz Palm, the SEMTSA approach produces an understanding of the relationship of univariate and multivariate time series forecasting models and dynamic, time series structural econometric models. As scientists and decision-makers in industry and government world-wide adopt the Bayesian approach to scientific inference, decision-making and forecasting, Zellner offers an in-depth analysis and appreciation of this important paradigm shift. Finally Zellner discusses the alternative approaches to model building and looks at how the use and development of the SEMTSA approach has led to the production of a Marshallian Macroeconomic Model that will prove valuable to many. Written by one of the foremost practitioners of econometrics, this book will have wide academic and professional appeal.
       Dansk
                Dansk
             
            