Presentation of Simultaneous Equations Model with Error Components Structure and Estimation of the Reduced Form

Author(s):  
Jayalakshmi Krishnakumar
2021 ◽  
pp. 98-109
Author(s):  
A. N. Tsatsulin ◽  
B. A. Tsatsulin

In continuation of the article, the authors of the study devoted to the problems of scenario modeling and solving specific problems of management and development of the health care system of the Perm Territory, built the author’s dynamic multivariate model, which was based on an authoritative approach and consists of a set of five structural simultaneous equations. As a result, each equation of the system is a linear form of recursive regression, where the independent variable as a factor-factor taken into account in one equation becomes a depend- ent variable as an effective factor-factor. In order to eliminate the phenomenon of autocor- relation of residual values, the method of time lagging was used. To estimate the parameters of the reduced form of structural simultaneous equations, the two-step least squares method was used as a special case of the maximum likelihood method. The obtained parameter esti- mates on the whole turned out to be effective with moderate consistency and satisfactory bias. The constructed model made it possible to carry out a short-term forecast of the most important target socio-economic indicator of the success of healthcare development in the region until 2023. The authors considered the national goal as such a priority indicator — the expected (future) life expectancy of the population of the study area. At the end of the article, conclusions were drawn and the prospects for further scientific research of the authors were outlined.


2016 ◽  
Vol 23 (5) ◽  
pp. 981-992 ◽  
Author(s):  
Luca Zamparini ◽  
Anna Serena Vergori ◽  
Serena Arima

Traditional analysis of tourism demand has been mainly based on the consideration of economic variables aiming at explaining the evolution of either tourists’ expenditure or overnight stays or arrivals. This study is based on a collection of both economic variables and non-economic factors surveyed since 1998–2013 for 99 Italian NUTS3 (Nomenclature of Units for Territorial Statistics) regions (provinces). It is the first study in which such a wide array of non-economic and economic variables has been investigated for a panel data at this geographical level by using a simultaneous equations model. The analysis shows that all considered variables are significant for the evolution of tourism demand and that climate, tourism supply and entrepreneurial capabilities have the largest impacts.


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