scholarly journals Explaining Fiscal Balances with a Simultaneous  Equation Model of Revenue and Expenditure: A Case Study of Swiss Cantons Using Panel Data

2010 ◽  
Vol 30 (2) ◽  
pp. 69-94 ◽  
Author(s):  
JAYA KRISHNAKUMAR ◽  
MARC-JEAN MARTIN ◽  
NILS SOGUEL
Keyword(s):  
Panel Data ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Simultaneous Equation Model ◽  
Swiss Cantons

scholarly journals ESTIMASI PARAMETER PADA SISTEM MODEL PERSAMAAN SIMULTAN DATA PANEL DINAMIS DENGAN METODE 2 SLS GMM-AB

2018 ◽  
Vol 11 (2) ◽  
pp. 79-91
Author(s):  
Arya Fendha Ibnu Shina
Keyword(s):  
Panel Data ◽  
Structural Equation ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Dynamic Panel Data ◽  
Least Square ◽  
System Model ◽  
Reduced Form ◽  
Simultaneous Equation Model ◽  
Dynamic Panel

Single equation models ignore interdependencies or two-way relationships between response variables. The simultaneous equation model accommodates this two-way relationship form. Two Stage Least Square Generalized Methods of Moment Arellano and Bond (2 SLS GMM-AB) is used to estimate the parameters in the simultaneous system model of dynamic panel data if each structural equation is exactly identified or over identified. In the simultaneous equation system model with dynamic panel data, each structural equation and reduced form is a dynamic panel data regression equation. Estimation of structural equations and reduced form using Ordinary Least Square (OLS) resulted biased and inconsistent estimators. Arellano and Bond GMM method (GMM AB) estimator produces unbiased, consistent, and efficient estimators.The purpose of this paper is to explain the steps of 2 SLS GMM-AB method to estimate parameter in simultaneous equation model with dynamic panel data.  Keywords:2 SLS GMM-AB, Arellano and Bond estimator, Dynamic Panel Data, Simultaneous Equations

scholarly journals ESTIMATING A LINEAR SIMULTANEOUS EQUATION MODEL WITH PANEL DATA

2005 ◽  
Vol 50 (spec01) ◽  
pp. 475-494 ◽  
Author(s):  
SOO-BIN PARK
Keyword(s):  
Panel Data ◽  
Single Equation ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Small Sample ◽  
Simultaneous Equation Model ◽  
Individual Effects ◽  
Linear Simultaneous Equation ◽  
The Individual ◽  
Time Invariant

In this paper we consider a class of the GMM estimators for a linear simultaneous equation model with panel data. The model is characterized by the presence of unobserved individual effects as part of structural disturbances and by the correlation of certain time-varying and time-invariant exogenous variables with the individual effects. A class of single-equation and system GMM estimators is proposed on the basis of a class of instruments as a unifying theme. Small-sample performance of this class of estimators is also investigated by a series of sampling experiments.

scholarly journals Analysis of Social, Economic and Population in Central Java Using the Dynamic Data Panel Simultaneous Equation Model

2019 ◽  
Vol 3 (1) ◽  
pp. 79
Author(s):  
Supriyanto Supriyanto
Keyword(s):  
Panel Data ◽  
Structural Equation ◽  
Participation Rate ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Dynamic Panel Data ◽  
Least Square ◽  
Simultaneous Equation Model ◽  
Dynamic Panel ◽  
Central Java

A single equation model that is often used ignores the interdependence between response variables. Frequently encountered variables that have a two-way relationship. These interrelated two-way relationships can be summarized in a simultaneous equation model system. There is a relationship between variables which are in fact dynamic. In the system model of simultaneous equations with dynamic panel data, each structural equation is a dynamic panel data regression equation. The estimation of using Ordinary Least Square (OLS) in the dynamic panel data model results in biased and inconsistent predictors because there is a lag of the dependent variable that correlates with the error. First difference in dynamic panel models is used to eliminate individual effects. Instrument variables are needed, namely variables that do not correlate with errors. Therefore, dynamic panel data models are more suitable to be used in analyzing poverty and social change. From the simultaneous equation model obtained, the dominant factors affecting the level of poverty in Central Java Province are the unemployment rate, Human Development Index, labor force participation rate, population, and Gross Regional Domestic Product.

Remittances, Economic Growth and Poverty: A Case of African OIC Member Countries

2018 ◽  
Vol 57 (2) ◽  
pp. 121-143
Author(s):  
Nasim Shah Shirazi ◽  
Sajid Amin Javed ◽  
Dawood Ashraf
Keyword(s):  
Economic Growth ◽  
Financial Development ◽  
Poverty Reduction ◽  
Simultaneous Equation ◽  
Equation Model ◽  
Annual Data ◽  
Simultaneous Equation Model ◽  
African Countries ◽  
Alternative Source ◽  
The Impact

This paper investigates the impact of remittance inflows on economic growth and poverty reduction for seven African countries using annual data from 1992-2010. By using the depth of hunger as a proxy for poverty in a Simultaneous Equation Model (SEM), we find that remittances have statistically significant growth enhancing and poverty reducing impact. Drawing on our estimates, we conclude that financial development level significantly increases the remittances inflows and strengthens poverty alleviating impact of remittances. Results of our study further show a signficant interactive imapct of remittances and finacial develpment on economic growth, suggesting the substitutability between remittance inflows and financial development. We further find that 3 percentage point increase in credit provision to the private sector (financial development) can help eliminate the severe depth of hunger in the region. Remittances, serving an alternative source of private credit, can be effective in this regard. Keywords: Remittance Inflow, Poverty Alleviation, Financial Development, Simultaneous Equation Model

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