A Generalized Information Theoretical Approach to Non-linear Time Series Model

Sriboochitta, Songsak; Yamaka, Woraphon; Maneejuk, Paravee; Pastpipatkul, Pathairat

doi:10.1007/978-3-319-50742-2_20

Cited by 15 publications

(2 citation statements)

References 6 publications

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“…In practice, it is difficult to determine the appropriate bounds in the panel kink regression model. Following the suggestion of Pukelsheim [33], Golan, Judge, and Miller [16] on the 3σ rule for setting the error bounds, then v it1 = −3σ and v itM = 3σ, where σ is the standard deviation of the dependent variable. Finally, the individual-specific effect error component u i can be expressed as…”

Section: Estimationmentioning

confidence: 99%

Tourism Development and Economic Growth in Southeast Asian Countries under the Presence of Structural Break: Panel Kink with GME Estimator

Maneejuk¹,

Yamaka²,

Srichaikul³

2022

Mathematics

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This study examines the nonlinear impact of tourism development on economic growth in Southeast Asian countries using the panel kink regression model. Due to the paucity of Southeast Asian data, we may face the overparameterization problem in our model. To deal with this problem, this study proposes the Generalized Maximum Entropy (GME) estimator to estimate the unknown parameters in this nonlinear model. Several important tourism development indicators consisting of the total international arrivals, international tourism expenditure, and tourism receipts are considered. In addition, we also consider the gross capital formation and real effective exchange rate as a control variable in our nonlinear model. Our findings show that the effect of international tourist arrivals on economic growth should be separated into two regimes, while other factors do not exhibit a nonlinear relationship with Southeast Asian economic growth. Thus, we construct the empirical model with the kink effect in the variable of international tourist arrivals. To confirm the performance of the GME estimator, we compare it to the ordinary least squares and the fixed effect estimators. According to the mean squared and root means squared errors, we find that our GME estimator performs better than the ordinary least squares and the fixed effect estimators. This indicates that GME estimation is an applicable method for estimating the nonlinear effect of tourism growth on economic growth. Our empirical results show that there are positive impacts of tourism growth on economic growth for regime 1 (low tourism demand) and regime 2 (high tourism demand) with the effect of the low tourist arrivals regime being relatively larger. We also find a positive influence of gross capital formation, real effective exchange rate, international tourism expenditure, and tourism receipts on Southeast Asian economic growth.

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Section: Estimationmentioning

confidence: 99%

Tourism Development and Economic Growth in Southeast Asian Countries under the Presence of Structural Break: Panel Kink with GME Estimator

Maneejuk¹,

Yamaka²,

Srichaikul³

2022

Mathematics

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“…Thus, using the Lagrange multipliers, we maximize the log empirical likelihood in equation ( 4) subject to the constraints in equation. (5) and equation (6) as:…”

Section: Constructing Empirical Likelihood For Sfmmentioning

confidence: 99%

An empirical likelihood estimator of stochastic frontier model

Pastpipatkul

Yamaka

Maneejuk

et al. 2018

J. Phys.: Conf. Ser.

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We consider a stochastic frontier model, with independent observation errors identically distributed with an unknown probability density function. Instead of maximizing the parametric version of the likelihood function, which requires knowledge about the error distribution, we replace the parametric likelihood with an empirical likelihood. A simulation and experiment study are presented to illustrate the finite-sample of this estimator in terms of its accuracy and robustness. Our proposed estimation is competitive and allows for better analysis of datasets than existing parametric methods.

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