scholarly journals Exchange rate uncertainty and trade growth?a comparison of linear and non-linear (forecasting) models

2005 ◽  
Vol 21 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Helmut Herwartz ◽  
Henning Weber
2021 ◽  
Vol 17 (3) ◽  
pp. 47-55
Author(s):  
Jane Kaboro ◽  
Naftaly Mose

Abstract Macroeconomic convergence is critical for member states to achieve the level of harmonization required for establishing a stable and resilient monetary union. The East African Community (EAC) member states, therefore, established set targets for macroeconomic convergence, intending to eliminate exchange rate uncertainty within the bloc and reduce the costs of the monetary union. However, recent empirical studies indicate that the rate of convergence of the member states to the set macroeconomic targets has been very slow, resulting in high exchange rate uncertainty within the region. It is against this backdrop that this research was conceptualized to examine the influence of convergence in macroeconomic variables on the exchange rate uncertainty of EAC states using secondary panel data. The study made use of standard deviation and the Levin Lin Chu (LLC) test to determine convergence and unit root respectively. The panel ordinary least squares (OLS) regression findings showed that all the explanatory variables had a negatively significant effect on exchange rate uncertainty. This implies that convergence in macroeconomic variables among the member countries slows exchange rate uncertainty. Thus, policy should be made towards controlling this negative effect resulting from macroeconomic variables as East Africa bids for monetary union.


2020 ◽  
Vol 54 (2) ◽  
pp. 597-614
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar

In this paper, fuzzified Choquet integral and fuzzy-valued integrand with respect to separate measures like fuzzy measure, signed fuzzy measure and intuitionistic fuzzy measure are used to develop regression model for forecasting. Fuzzified Choquet integral is used to build a regression model for forecasting time series with multiple attributes as predictor attributes. Linear regression based forecasting models are suffering from low accuracy and unable to approximate the non-linearity in time series. Whereas Choquet integral can be used as a general non-linear regression model with respect to non classical measures. In the Choquet integral based regression model parameters are optimized by using a real coded genetic algorithm (GA). In these forecasting models, fuzzified integrands denote the participation of an individual attribute or a group of attributes to predict the current situation. Here, more generalized Choquet integral, i.e., fuzzified Choquet integral is used in case of non-linear time series forecasting models. Three different real stock exchange data are used to predict the time series forecasting model. It is observed that the accuracy of prediction models highly depends on the non-linearity of the time series.


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