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The spread of the COVID-19 pandemic in 2020 has contributed a large impact on various economic sectors and the energy sector is no exception. In this paper, we analyze the time-varying correlation between COVID-19 shocks (positive and negative) and energy markets (natural gas, gasoil, heating oil, coal, and crude oil) in the time-varying environment. This study adds to the literature by implementing the Markov-switching dynamic copula with Student-t distribution to explore the unexpected COVID-19 pandemic shock effects on energy markets. Our results revealed that (i) there is evidence of correlation between COVID-19 shocks and all energy markets; (ii) the contributions of COVID-19 shocks on energy markets are not constant along 2020. (iii), there is evidence of a similar response of the energy markets to the positive and negative COVID-19 shocks.
Paravee Maneejuk; Sukrit Thongkairat; Wilawan Srichaikul. Time-varying co-movement analysis between COVID-19 shocks and the energy markets using the Markov Switching Dynamic Copula approach. Energy Reports 2021, 1 .
AMA StyleParavee Maneejuk, Sukrit Thongkairat, Wilawan Srichaikul. Time-varying co-movement analysis between COVID-19 shocks and the energy markets using the Markov Switching Dynamic Copula approach. Energy Reports. 2021; ():1.
Chicago/Turabian StyleParavee Maneejuk; Sukrit Thongkairat; Wilawan Srichaikul. 2021. "Time-varying co-movement analysis between COVID-19 shocks and the energy markets using the Markov Switching Dynamic Copula approach." Energy Reports , no. : 1.
This study aims at examining the predictability of the autoregressive integrated moving average and deep learning methods consisting of the artificial neural network, recurrent neural network, long short-term memory (LSTM), and support vector machine. We will use these tools to estimate the parameters for predicting the accuracy of the foreign exchange returns. This study compares the forecasting performance between the autoregressive integrated moving average and deep learning methods. The comparison is based on the mean absolute percentage error, the root-mean-squared error, the mean absolute error, and Theil U. The empirical results indicate that the LSTM seems to outperform the other deep learning models as well as the traditional regression models.
Paravee Maneejuk; Wilawan Srichaikul. Forecasting foreign exchange markets: further evidence using machine learning models. Soft Computing 2021, 25, 7887 -7898.
AMA StyleParavee Maneejuk, Wilawan Srichaikul. Forecasting foreign exchange markets: further evidence using machine learning models. Soft Computing. 2021; 25 (12):7887-7898.
Chicago/Turabian StyleParavee Maneejuk; Wilawan Srichaikul. 2021. "Forecasting foreign exchange markets: further evidence using machine learning models." Soft Computing 25, no. 12: 7887-7898.
As the conventional models for time series forecasting often use single-valued data (e.g., closing daily price data or the end of the day data), a large amount of information during the day is neglected. Traditionally, the fixed reference points from intervals, such as midpoints, ranges, and lower and upper bounds, are generally considered to build the models. However, as different datasets provide different information in intervals and may exhibit nonlinear behavior, conventional models cannot be effectively implemented and may not be guaranteed to provide accurate results. To address these problems, we propose the artificial neural network with convex combination (ANN-CC) model for interval-valued data. The convex combination method provides a flexible way to explore the best reference points from both input and output variables. These reference points were then used to build the nonlinear ANN model. Both simulation and real application studies are conducted to evaluate the accuracy of the proposed forecasting ANN-CC model. Our model was also compared with traditional linear regression forecasting (information-theoretic method, parametrized approach center and range) and conventional ANN models for interval-valued data prediction (regularized ANN-LU and ANN-Center). The simulation results show that the proposed ANN-CC model is a suitable alternative to interval-valued data forecasting because it provides the lowest forecasting error in both linear and nonlinear relationships between the input and output data. Furthermore, empirical results on two datasets also confirmed that the proposed ANN-CC model outperformed the conventional models.
Woraphon Yamaka; Rungrapee Phadkantha; Paravee Maneejuk. A Convex Combination Approach for Artificial Neural Network of Interval Data. Applied Sciences 2021, 11, 3997 .
AMA StyleWoraphon Yamaka, Rungrapee Phadkantha, Paravee Maneejuk. A Convex Combination Approach for Artificial Neural Network of Interval Data. Applied Sciences. 2021; 11 (9):3997.
Chicago/Turabian StyleWoraphon Yamaka; Rungrapee Phadkantha; Paravee Maneejuk. 2021. "A Convex Combination Approach for Artificial Neural Network of Interval Data." Applied Sciences 11, no. 9: 3997.
Motivated by the advances in the estimation of parameters in linear models by regularization methods such as Ridge and Lasso regularizations, we investigate regularization of Generalized Maximum Entropy, which is an alternative estimation method in linear models. Our simulations confirm the better performance of the regularized Generalized Maximum Entropy estimation method, which could stimulate further theoretical research. An application of the new estimation method is illustrated with data from Thailand concerning the effect of education on economic growth.
Paravee Maneejuk. On regularization of generalized maximum entropy for linear models. Soft Computing 2021, 25, 7867 -7875.
AMA StyleParavee Maneejuk. On regularization of generalized maximum entropy for linear models. Soft Computing. 2021; 25 (12):7867-7875.
Chicago/Turabian StyleParavee Maneejuk. 2021. "On regularization of generalized maximum entropy for linear models." Soft Computing 25, no. 12: 7867-7875.
This study investigates the nonlinear impact of various modes of transportation (air, road, railway, and maritime) on the number of foreign visitors to China originating from major source countries. Our nonlinear tourism demand equations are determined through the Markov-switching regression (MSR) model, thereby, capturing the possible structural changes in Chinese tourism demand. Due to many variables and the limitations from the small number of observations confronted in this empirical study, we may face multicollinearity and endogeneity bias. Therefore, we introduce the two penalized maximum likelihoods, namely Ridge and Lasso, to estimate the high dimensional parameters in the MSR model. This investigation found the structural changes in all tourist arrival series with significant coefficient shifts in transportation variables. We observe that the coefficients are relatively more significant in regime 1 (low tourist arrival regime). The coefficients in regime 1 are all positive (except railway length in operation), while the estimated coefficients in regime 2 are positive in fewer numbers and weak. This study shows that, in the process of transportation, development and changing inbound tourism demand from ten countries, some variables with the originally strong positive effect will have a weak positive effect when tourist arrivals are classified in the high tourist arrival regime.
Woraphon Yamaka; Xuefeng Zhang; Paravee Maneejuk. Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches. Mathematics 2021, 9, 515 .
AMA StyleWoraphon Yamaka, Xuefeng Zhang, Paravee Maneejuk. Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches. Mathematics. 2021; 9 (5):515.
Chicago/Turabian StyleWoraphon Yamaka; Xuefeng Zhang; Paravee Maneejuk. 2021. "Analyzing the Influence of Transportations on Chinese Inbound Tourism: Markov Switching Penalized Regression Approaches." Mathematics 9, no. 5: 515.
Let \(\gamma = 4z-1\) be an unit of Type \((*^{-})\) of the Galois ring \({{\,\mathrm{GR}\,}}(2^a, m)\). The \(\gamma\)-constacyclic codes of length \(2^s\) over the Galois ring \({{\,\mathrm{GR}\,}}(2^a, m)\) are precisely the ideals \(\langle (x +1)^i \rangle\), \(0 \le i \le 2^sa\) of the chain ring \(\mathfrak {R}(a,m, \gamma ) = \dfrac{{{\,\mathrm{GR}\,}}(2^a,m)[x]}{\langle {x^{2^s}} - \gamma \rangle }\). This structure is used to determine the symbol pair distance of \(\gamma\)-constacyclic codes of length \(2^s\) over \({{\,\mathrm{GR}\,}}(2^a, m)\). The exact symbol-pair distances for all such \(\gamma\)-constacyclic codes of length \(2^s\) over \({{\,\mathrm{GR}\,}}(2^a, m)\) are obtained. Also, we provide the MDS symbol-pair codes of length \(2^s\) over \({{\,\mathrm{GR}\,}}(2^a, m)\) and some examples are computed.
Hai Q. Dinh; Narendra Kumar; Abhay Kumar Singh; Manoj Kumar Singh; Indivar Gupta; Paravee Maneejuk. On the symbol-pair distance of some classes of repeated-root constacyclic codes over Galois ring. Applicable Algebra in Engineering, Communication and Computing 2021, 1 -18.
AMA StyleHai Q. Dinh, Narendra Kumar, Abhay Kumar Singh, Manoj Kumar Singh, Indivar Gupta, Paravee Maneejuk. On the symbol-pair distance of some classes of repeated-root constacyclic codes over Galois ring. Applicable Algebra in Engineering, Communication and Computing. 2021; ():1-18.
Chicago/Turabian StyleHai Q. Dinh; Narendra Kumar; Abhay Kumar Singh; Manoj Kumar Singh; Indivar Gupta; Paravee Maneejuk. 2021. "On the symbol-pair distance of some classes of repeated-root constacyclic codes over Galois ring." Applicable Algebra in Engineering, Communication and Computing , no. : 1-18.
: This study analyzed the nonlinear impacts of education, particularly higher education, on economic growth in the ASEAN-5 countries (i.e., Thailand, Indonesia, Malaysia, Singapore, and the Philippines) over the period 2000–2018. The impacts of education on economic growth are assessed through various education indicators, consisting of public expenditure on tertiary education per student, enrolment rates of primary, secondary, and tertiary levels, educated workforce, and the novel indicator of unemployment rates with advanced education. This study establishes nonlinear regression models—the time-series kink regression and the panel kink regression—to investigate the kink effects of education on the individual country’s economic growth and the ASEAN-5 region, respectively. There are three main findings. Firstly, the nonlinear effects of the government expenditure per tertiary student on economic growth are confirmed for the ASEAN-5 region. However, the impacts do not follow the law of diminishing returns. Secondly, our findings reveal that an increase in unemployment of advanced educated workers can positively or negatively impact economic growth, which requires an appropriate policy to handle the negative impacts. Lastly, secondary and higher education enrollment rates can contribute to the ASEAN-5’s economic growth (both the individual and regional levels). However, the regional analysis reveals that higher education impacts become twice as strong when the enrollment rates are greater than a certain level (a kink point). Therefore, we may conclude that secondary enrollment rates positively affect economic growth; however, higher education is the key to future growth and sustainability.
Paravee Maneejuk; Woraphon Yamaka. The Impact of Higher Education on Economic Growth in ASEAN-5 Countries. Sustainability 2021, 13, 520 .
AMA StyleParavee Maneejuk, Woraphon Yamaka. The Impact of Higher Education on Economic Growth in ASEAN-5 Countries. Sustainability. 2021; 13 (2):520.
Chicago/Turabian StyleParavee Maneejuk; Woraphon Yamaka. 2021. "The Impact of Higher Education on Economic Growth in ASEAN-5 Countries." Sustainability 13, no. 2: 520.
This study aims to improve the copula-based stochastic frontier quantile model by treating the quantile as the unknown parameter. This method can solve the problem of quantile selection bias as the quantile will be estimated simultaneously with other parameters in the model. We then evaluate the performance and accuracy of the proposed model by conducting two simulation studies and a real data analysis with two different data sets. The overall results reveal that our proposed model can beat the conventional stochastic frontier model and also the copula-based stochastic frontier model with a given quantile.
Paravee Maneejuk; Woraphon Yamaka. Copula-Based Stochastic Frontier Quantile Model with Unknown Quantile. Econometrics for Financial Applications 2020, 445 -458.
AMA StyleParavee Maneejuk, Woraphon Yamaka. Copula-Based Stochastic Frontier Quantile Model with Unknown Quantile. Econometrics for Financial Applications. 2020; ():445-458.
Chicago/Turabian StyleParavee Maneejuk; Woraphon Yamaka. 2020. "Copula-Based Stochastic Frontier Quantile Model with Unknown Quantile." Econometrics for Financial Applications , no. : 445-458.
This study aims to examine the relationship between economic development and environmental degradation based on the Environmental Kuznets Curve (EKC) hypothesis. The level of CO2 emissions is used as the indicator of environmental damage to determine whether or not greater economic growth can lower environmental degradation under the EKC hypothesis. The investigation was performed on eight major international economic communities covering 44 countries across the world. The relationship between economic growth and environmental condition was estimated using the kink regression model, which identifies the turning point of the change in the relationship. The findings indicate that the EKC hypothesis is valid in only three out of the eight international economic communities, namely the European Union (EU), Organization for Economic Co-operation and Development (OECD), and Group of Seven (G7). In addition, interesting results were obtained from the inclusion of four other control variables into the estimation model for groups of countries to explain the impact on environmental quality. Financial development (FIN), the industrial sector (IND), and urbanization (URB) were found to lead to increasing CO2 emissions, while renewable energies (RNE) appeared to reduce the environmental degradation. In addition, when we further investigated the existence of the EKC hypothesis in an individual country, the results showed that the EKC hypothesis is valid in only 9 out of the 44 individual countries.
Nutnaree Maneejuk; Sutthipat Ratchakom; Paravee Maneejuk; Woraphon Yamaka. Does the Environmental Kuznets Curve Exist? An International Study. Sustainability 2020, 12, 9117 .
AMA StyleNutnaree Maneejuk, Sutthipat Ratchakom, Paravee Maneejuk, Woraphon Yamaka. Does the Environmental Kuznets Curve Exist? An International Study. Sustainability. 2020; 12 (21):9117.
Chicago/Turabian StyleNutnaree Maneejuk; Sutthipat Ratchakom; Paravee Maneejuk; Woraphon Yamaka. 2020. "Does the Environmental Kuznets Curve Exist? An International Study." Sustainability 12, no. 21: 9117.
Paravee Maneejuk; Woraphon Yamaka; Songsak Sriboonchitta. Entropy inference in smooth transition kink regression. Communications in Statistics - Simulation and Computation 2020, 1 -24.
AMA StyleParavee Maneejuk, Woraphon Yamaka, Songsak Sriboonchitta. Entropy inference in smooth transition kink regression. Communications in Statistics - Simulation and Computation. 2020; ():1-24.
Chicago/Turabian StyleParavee Maneejuk; Woraphon Yamaka; Songsak Sriboonchitta. 2020. "Entropy inference in smooth transition kink regression." Communications in Statistics - Simulation and Computation , no. : 1-24.
In many practical situations, it is desirable to predict binary (“yes”–“no”) decisions made by people. The traditional approach to this prediction assumes that the utility linearly depends on the corresponding parameters, and that the distribution of the difference between predicted and actual utility is symmetric — usually normal or logistic; the corresponding techniques are known as, correspondingly, probit and logit. In real life, utility often non-linearly depends on the parameters, and the corresponding distributions are asymmetric (skewed). There are techniques for dealing with non-linearity; the most widely used such technique — called kink regression — uses piece-wise linear approximations to the utility. There are also techniques that take into account the distribution’s asymmetry; usually, they are based on using special asymmetric distributions: skew-normal and skew-logistic. In this paper, we show how these two techniques to be combined to take into account both non-linearity and asymmetry. On a real-life example, we show that the new technique indeed leads to a better description of human binary decision-making.
Paravee Maneejuk. How to Take Both Non-Linearity and Asymmetry (Skewness) into Account in Binary Decision Making: Skew-Probit and Skew-Logit in Binary Kink Regression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2020, 28, 39 -49.
AMA StyleParavee Maneejuk. How to Take Both Non-Linearity and Asymmetry (Skewness) into Account in Binary Decision Making: Skew-Probit and Skew-Logit in Binary Kink Regression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2020; 28 (Supp01):39-49.
Chicago/Turabian StyleParavee Maneejuk. 2020. "How to Take Both Non-Linearity and Asymmetry (Skewness) into Account in Binary Decision Making: Skew-Probit and Skew-Logit in Binary Kink Regression." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. Supp01: 39-49.
In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.
Ruofan Liao; Paravee Maneejuk; Songsak Sriboonchitta. Beyond Deep Learning: An Econometric Example. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2020, 28, 31 -38.
AMA StyleRuofan Liao, Paravee Maneejuk, Songsak Sriboonchitta. Beyond Deep Learning: An Econometric Example. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2020; 28 (Supp01):31-38.
Chicago/Turabian StyleRuofan Liao; Paravee Maneejuk; Songsak Sriboonchitta. 2020. "Beyond Deep Learning: An Econometric Example." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. Supp01: 31-38.
This study aims to investigate whether small and medium-sized enterprises (SMEs) in Thailand have the propensity to catching up with the global development agenda, sustainable entrepreneurship. This study collects the data from 231 entrepreneurs in the service sector in Thailand and designs the questionnaire according to the concept of sustainable entrepreneurship. Then, in the data analysis process, we introduce the logistic regression with the shrinkage method alternatively to the classical logistic regression model. A brief idea of this method is to involve penalizing size of the coefficients in the likelihood function, in which three penalty functions namely ridge regression, the lasso, and the elastic net, are considered in this study. Then, the performance of the models is evaluated through prediction accuracy and AUC. The results show that the Elastic Net slightly outperforms the other models. Also, the results reveal that the characteristics of enterprises and entrepreneurs, and the opinions on sustainable entrepreneurship aspects have the essential roles and positive effects on the propensity of entrepreneurs’ interest in sustainable entrepreneurship.
Chalerm Jaitang; Paravee Maneejuk; Pitchaya Boonsrirat. Sustainable Entrepreneurship on Thailand’s SMEs. Econometrics for Financial Applications 2020, 423 -436.
AMA StyleChalerm Jaitang, Paravee Maneejuk, Pitchaya Boonsrirat. Sustainable Entrepreneurship on Thailand’s SMEs. Econometrics for Financial Applications. 2020; ():423-436.
Chicago/Turabian StyleChalerm Jaitang; Paravee Maneejuk; Pitchaya Boonsrirat. 2020. "Sustainable Entrepreneurship on Thailand’s SMEs." Econometrics for Financial Applications , no. : 423-436.
The discussion on the use and misuse of p-values in 2016 by the American Statistician Association was a timely assertion that statistical concept should be properly used in science. Some researchers, especially the economists, who adopt significance testing and p-values to report their results, may felt confused by the statement, leading to misinterpretations of the statement. In this study, we aim to re-examine the accuracy of the p-value and introduce an alternative way for testing the hypothesis. We conduct a simulation study to investigate the reliability of the p-value. Apart from investigating the performance of p-value, we also introduce some existing approaches, Minimum Bayes Factors and Belief functions, for replacing p-value. Results from the simulation study confirm unreliable p-value in some cases and that our proposed approaches seem to be useful as the substituted tool in the statistical inference. Moreover, our results show that the plausibility approach is more accurate for making decisions about the null hypothesis than the traditionally used p-values when the null hypothesis is true. However, the MBFs of Edwards et al. [Bayesian statistical inference for psychological research. Psychol. Rev. 70(3) (1963), pp. 193–242]; Vovk [A logic of probability, with application to the foundations of statistics. J. Royal Statistical Soc. Series B (Methodological) 55 (1993), pp. 317–351] and Sellke et al. [Calibration of p values for testing precise null hypotheses. Am. Stat. 55(1) (2001), pp. 62–71] provide more reliable results compared to all other methods when the null hypothesis is false.
Paravee Maneejuk; Woraphon Yamaka. Significance test for linear regression: how to test without P-values? Journal of Applied Statistics 2020, 48, 827 -845.
AMA StyleParavee Maneejuk, Woraphon Yamaka. Significance test for linear regression: how to test without P-values? Journal of Applied Statistics. 2020; 48 (5):827-845.
Chicago/Turabian StyleParavee Maneejuk; Woraphon Yamaka. 2020. "Significance test for linear regression: how to test without P-values?" Journal of Applied Statistics 48, no. 5: 827-845.
Let p be an odd prime, s, m be positive integers such that pm ≡ 2 (mod 3). In this paper, using the relationship about Hamming distances between simple-root cyclic codes and repeated-root cyclic codes, the Hamming distance of all cyclic codes of length 6ps over finite field Fpm is obtained. All maximum distance separable (MDS) cyclic codes of length 6ps are established.
Hai Q. Dinh; Xiaoqiang Wang; Paravee Maneejuk. On the Hamming Distance of Repeated-Root Cyclic Codes of Length 6ps. IEEE Access 2020, 8, 39946 -39958.
AMA StyleHai Q. Dinh, Xiaoqiang Wang, Paravee Maneejuk. On the Hamming Distance of Repeated-Root Cyclic Codes of Length 6ps. IEEE Access. 2020; 8 (99):39946-39958.
Chicago/Turabian StyleHai Q. Dinh; Xiaoqiang Wang; Paravee Maneejuk. 2020. "On the Hamming Distance of Repeated-Root Cyclic Codes of Length 6ps." IEEE Access 8, no. 99: 39946-39958.
For any prime number p, positive integers m,k,n, where n satisfies gcd(p,n)=1, and for any non-zero element λ0 of the finite field Fpm of cardinality pm, we prove that any λ0pk-constacyclic code of length pkn over the finite field Fpm is monomially equivalent to a matrix-product code of a nested sequence of pk λ0-constacyclic codes with length n over Fpm. As an application, we completely determine the Hamming distances of all negacyclic codes of length 7⋅2l over F7 for any integer l≥3.
Yonglin Cao; Yuan Cao; Hai Q. Dinh; Fang-Wei Fu; Paravee Maneejuk. On matrix-product structure of repeated-root constacyclic codes over finite fields. Discrete Mathematics 2019, 343, 111768 .
AMA StyleYonglin Cao, Yuan Cao, Hai Q. Dinh, Fang-Wei Fu, Paravee Maneejuk. On matrix-product structure of repeated-root constacyclic codes over finite fields. Discrete Mathematics. 2019; 343 (4):111768.
Chicago/Turabian StyleYonglin Cao; Yuan Cao; Hai Q. Dinh; Fang-Wei Fu; Paravee Maneejuk. 2019. "On matrix-product structure of repeated-root constacyclic codes over finite fields." Discrete Mathematics 343, no. 4: 111768.
This study extends the work of Harvey and Sucarrat [15] and present Markov regime-switching (MS) Beta-skewed-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) model to predict the volatility. To examine the performance of our model, in-sample point forecast precision and AIC and BIC weights are conducted. We study the volatility of five Exchange Traded Fund returns for period from January 2012 to October 2018. Our proposed model is not found to outperform all the other models. However, the dominance of MS-Beta-skewed-t-EGARCH for SPY, VGT, and AGG may support the application of the MS-Beta-skewed-t-EGARCH model for some financial data series.
Woraphon Yamaka; Paravee Maneejuk; Songsak Sriboonchitta. Markov Switching Beta-skewed-t EGARCH. Computer Vision 2019, 184 -196.
AMA StyleWoraphon Yamaka, Paravee Maneejuk, Songsak Sriboonchitta. Markov Switching Beta-skewed-t EGARCH. Computer Vision. 2019; ():184-196.
Chicago/Turabian StyleWoraphon Yamaka; Paravee Maneejuk; Songsak Sriboonchitta. 2019. "Markov Switching Beta-skewed-t EGARCH." Computer Vision , no. : 184-196.
Our main concern is to investigate effectively and more realistically the linkage as well as a contagion effect among the stock markets of Thailand, United States of America, and Japan. To obtain the regime dependent correlation and co-skewness, we construct the Markov Switching model based on skew-normal and skewed student-t distributions as the extension of conventional Markov Switching model. The result shows that this model outperforms the conventional Markov Switching model in terms of the lowest AIC and BIC. Furthermore, we can confirm the existence of the contagion effect and co-skewness among the three stock markets with the nonlinearity and contagion LR-tests.
Woraphon Yamaka; Payap Tarkhamtham; Paravee Maneejuk; Songsak Sriboonchitta. A Regime Switching Skew-Distribution Model of Contagion. Econometrics for Financial Applications 2018, 439 -450.
AMA StyleWoraphon Yamaka, Payap Tarkhamtham, Paravee Maneejuk, Songsak Sriboonchitta. A Regime Switching Skew-Distribution Model of Contagion. Econometrics for Financial Applications. 2018; ():439-450.
Chicago/Turabian StyleWoraphon Yamaka; Payap Tarkhamtham; Paravee Maneejuk; Songsak Sriboonchitta. 2018. "A Regime Switching Skew-Distribution Model of Contagion." Econometrics for Financial Applications , no. : 439-450.
This paper uses the Cox proportional hazards model to examine which of the structural characteristics could resist the US financial crisis survival countries. The dependent variable in this model is generated from GDP, and the Markov Switching Autoregressive (MS-AR) technique is used to detect the survival period as well as the crisis occurrence in each country. The survival of a country is found to be influenced by continents (Asia, Australia and Africa) and the higher development level. However, being the member of economic communities, APEC and WTO, increase the chance of the crisis occurrence.
Wachirawit Puttachai; Woraphon Yamaka; Paravee Maneejuk; Songsak Sriboonchitta. Analysis of the Global Economic Crisis Using the Cox Proportional Hazards Model. Econometrics for Financial Applications 2018, 863 -872.
AMA StyleWachirawit Puttachai, Woraphon Yamaka, Paravee Maneejuk, Songsak Sriboonchitta. Analysis of the Global Economic Crisis Using the Cox Proportional Hazards Model. Econometrics for Financial Applications. 2018; ():863-872.
Chicago/Turabian StyleWachirawit Puttachai; Woraphon Yamaka; Paravee Maneejuk; Songsak Sriboonchitta. 2018. "Analysis of the Global Economic Crisis Using the Cox Proportional Hazards Model." Econometrics for Financial Applications , no. : 863-872.
Threshold effect manifests itself in many situations where the relationship between independent variables and dependent variable changes abruptly signifying the shift into another state or regime. In this paper, we propose a nonlinear logistic kink regression model to deal with this complicated and nonlinear effect of input factors on binary choice dependent variable. The Bayesian approach is suggested for estimating the unknown parameters in the models. The simulation study is conducted to demonstrate the performance and accuracy of our estimation in the proposed model. Also, we compare the performance of Bayesian and the Maximum Likelihood estimators. This simulation study demonstrates that the Bayesian method works viably better when sample size is less than 500. The application of our methods with a birthweight data and risk factors associated with low infant birth weight reveals interesting insights
Paravee Maneejuk; Woraphon Yamaka; Duentemduang Nachaingmai. Bayesian Analysis of the Logistic Kink Regression Model Using Metropolis-Hastings Sampling. Econometrics for Financial Applications 2018, 1073 -1083.
AMA StyleParavee Maneejuk, Woraphon Yamaka, Duentemduang Nachaingmai. Bayesian Analysis of the Logistic Kink Regression Model Using Metropolis-Hastings Sampling. Econometrics for Financial Applications. 2018; ():1073-1083.
Chicago/Turabian StyleParavee Maneejuk; Woraphon Yamaka; Duentemduang Nachaingmai. 2018. "Bayesian Analysis of the Logistic Kink Regression Model Using Metropolis-Hastings Sampling." Econometrics for Financial Applications , no. : 1073-1083.