Temel, Tugrul (2011): Estimation of a system of national accounts: implementation with mathematica.

PDF
MPRA_paper_35446.pdf Download (144kB)  Preview 
Abstract
This study implements Mathematica to estimate a system of national accounts. The estimation methods applied are portrayed in Danilov and Magnus (2008), including the Bayesian estimation, restricted and unrestricted leastsquares estimation and best linear unbiased estimation. Operationalizing these methods in the Mathematica environment is the main contribution of the current study. In light of the United Nations�e¤orts aimed to standardize across countries the compilation of national accounts, the Mathematica codes developed here should provide an important tool both for the estimation of unrealized or unavailable national accounts data and for conducting crosscountry and withincountry macroeconomic policy analysis.
Item Type:  MPRA Paper 

Original Title:  Estimation of a system of national accounts: implementation with mathematica 
English Title:  Estimation of a System of National Accounts: Implementation with Mathematica 
Language:  English 
Keywords:  System of national accounts; Social Accounting Matrix; Bayesian estimation; Leastsquares estimation; Best linear unbiased estimation; Linear programming 
Subjects:  C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology ; Computer Programs > C87  Econometric Software C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C67  InputOutput Models C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C11  Bayesian Analysis: General 
Item ID:  35446 
Depositing User:  Tugrul Temel 
Date Deposited:  17. Dec 2011 03:48 
Last Modified:  01. Mar 2013 04:24 
References:  [1] Abadir, K. M., and Magnus, J. R. (2005). Matrix Algebra. Econometric Exercises 1. Cambridge, UK: Cambridge University Press. [2] Danilov, D., and Magnus, J.R. (2007). Some equivalences in linear estimation. Online: http://cdata4.uvt.nl/website�les/magnus/paper77b.pdf. [3] Danilov, D., and Magnus, J. R. (2008). On the estimation of a large sparse Bayesian system: The Snaer program. Computational Statistics & Data Analysis, 52(9), 42034224. [4] Danilov, D., and Magnus, J. R. (2005). Leastsquares estimation in large sparse systems. Work in progress. [5] Golub, Loan. (1983). Matrix Computations. [6] Huang, C. J., and Crooke, P.S. (1997). Mathematics and Mathematica for economists. Oxford, UK: Blackwell Publishers. [7] Jing Xiao , Lan Liu , Lirong Xia and Tao Jiang. (2007). Fast Elimination of Redundant Linear Equations and Reconstruction of RecombinationFree Mendelian Inheritance on a Pedigree � Venue: Proc. of 18th Annual ACMSIAM Symoposium on Discrete Algorithms. [8] LaMacchia, Odlyzko. 1990. Solving large sparse linear systems over �nite �elds. [9] Magnus, J. R., and Neudecker, H. (1999). Matrix dixoerential calculus with applications in statistics and econometrics. New York, USA: John Wiley & Sons Ltd. [10] Magnus, J. R., van Tongeren, J., and Vos. (2000). National accounts estimation using indicator ratios. Review of Income and Welath, Series 46, No. 3, p329350. [11] Van Tongeren, Jan W., and J.R. Magnus. (2011). Bayesian integration of large SNA data frameworks with an application to Guatemala. Tilburg University, CentER Discussion Paper No. 2011022 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/35446 