Re-Testing the Tourism-Led Growth Hypothesis Using Panel Smooth Transition Regression Models

2014 ◽  
Vol 20 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Sheng-Chieh Pan ◽  
Shiao-Yen Liu ◽  
Po-Chin Wu
Keyword(s):  
Regression Models ◽  
Smooth Transition ◽  
Smooth Transition Regression ◽  
Smooth Transition Regression Models ◽  
Panel Smooth Transition Regression ◽  
Growth Hypothesis

scholarly journals Tunisian Labor Market and Regional Heterogeneity: Application of PSTR Model

2017 ◽  
Vol 4 (1) ◽  
pp. 51
Author(s):  
Yosra Koubaa
Keyword(s):  
Regression Models ◽  
Matching Theory ◽  
Smooth Transition ◽  
Structural Factors ◽  
Job Seekers ◽  
Regional Heterogeneity ◽  
Yield Variation ◽  
Matching Process ◽  
Smooth Transition Regression ◽  
Smooth Transition Regression Models

This paper is devoted to investigating the matching process for Tunisia using desaggregated data by assuming that the rising of the unemployment rate result from regional disparities which yield variation of matching efficiencies across regions. Since most econometric aspects of spatial heterogeneity can be handled by means of the standard panel data methods, we focus our discussion on the new technique: Panel Smooth Transition Regression models (PSTR). The distinction is that we can compute regional specific sensibilities for 23 regions over the period 1984-2004. Given this objective, we consider three structural factors that allow explaining the regional imbalances. Contrary to the previous econometric techniques of the matching theory, estimates of the coefficients depend, of three transition variables. The results show that women insertion, the qualification share and population density significantly contribute to explain the asymmetry of the matching process across regions. Our main conclusion is that the hiring in Tunisia is driven essentially by the stock of vacancies about is the region. The willingness of job seekers, obviously, remains low, although it is different between regions and seems relatively important in urban zones.

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