Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M. and Pereira, M.C. (2007) Multiple Linear Regression and Artificial Neural Networks Based on Principal Components to Predict Ozone Concentrations. Environmental Modelling & Software, 22, 97-103.
http://dx.doi.org/10.1016/j.envsoft.2005.12.002

has been cited by the following article:

• TITLE: Estimating the New Keynesian Model by Bootstrap Method for Johor Economy Tourism

  AUTHORS: Lola Muhamad Safiih, Hussin Mohd Fadli, Zainuddin Nurul Hila, Ramlee Mohd Noor Afiq

  KEYWORDS: Bootstrap Method, Bootstrap Keynesian Model, Multiplier, Economic Tourism, Simulation

  JOURNAL NAME: Modern Economy, Vol.7 No.10, September 9, 2016

  ABSTRACT: The use of Keynesian Model (KM) is exposed to large error value as well as wide confidence intervals, which may affect the accuracy of the results. Therefore, this paper attempts to hybridize bootstrap approach with Keynesian Model to obtain accurate estimation results which are known as the Bootstrap Keynesian Model (BKM). The proposed model is applied to the data of Johor Economic Tourism. The findings indicate that the Bootstrap Keynesian Model is the best model compared to the Keynesian Model since the error produced using the Bootstrap Keynesian Model is smaller and the confidence intervals are shorter. This shows that the proposed model is best to use in the measurement of multiplier. It can be concluded that the application of MK with bootstrap method is very useful to provide more accurate decisions. Therefore, the multipliers obtained may provide useful information for policy makers and researchers in the field of tourism.