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Journal articles on the topic 'Smooth Transition Autoregressive Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

Ekhosuehi, Nosa. "Interval Forecast for Smooth Transition Autoregressive Model." AFRREV STECH: An International Journal of Science and Technology 5, no. 1 (2016): 27. http://dx.doi.org/10.4314/stech.v5i1.3.

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2

Ubilava, David, and C. Gustav Helmers. "Forecasting ENSO with a smooth transition autoregressive model." Environmental Modelling & Software 40 (February 2013): 181–90. http://dx.doi.org/10.1016/j.envsoft.2012.09.008.

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3

Lundbergh, Stefan, Timo Teräsvirta, and Dick van Dijk. "Time-Varying Smooth Transition Autoregressive Models." Journal of Business & Economic Statistics 21, no. 1 (2003): 104–21. http://dx.doi.org/10.1198/073500102288618810.

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4

Dueker, Michael J., Zacharias Psaradakis, Martin Sola, and Fabio Spagnolo. "State-Dependent Threshold Smooth Transition Autoregressive Models*." Oxford Bulletin of Economics and Statistics 75, no. 6 (2012): 835–54. http://dx.doi.org/10.1111/j.1468-0084.2012.00719.x.

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5

LUUKKONEN, RITVA, PENTTI SAIKKONEN, and TIMO TERÄSVIRTA. "Testing linearity against smooth transition autoregressive models." Biometrika 75, no. 3 (1988): 491–99. http://dx.doi.org/10.1093/biomet/75.3.491.

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6

Beg, A. B. M. Rabiul Alam, Mervyn Joseph Silvapulle, and Paramsothy Silvapulle. "Robust Tests Against Smooth Transition Autoregressive Models." Journal of Statistical Computation and Simulation 72, no. 2 (2002): 167–78. http://dx.doi.org/10.1080/00949650212142.

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7

Kresnawati, Gayuh, Budi Warsito, and Abdul Hoyyi. "PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR)." Jurnal Gaussian 7, no. 1 (2018): 84–95. http://dx.doi.org/10.14710/j.gauss.v7i1.26638.

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Smooth Transition Autoregressive (STAR) Model is one of time series model used in case of data that has nonlinear tendency. STAR is an expansion of Autoregressive (AR) Model and can be used if the nonlinear test is accepted. If the transition function G(st,γ,c) is logistic, the method used is Logistic Smooth Transition Autoregressive (LSTAR). Weekly IHSG data in period of 3 January 2010 until 24 December 2017 has nonlinier tend and logistic transition function so it can be modeled with LSTAR . The result of this research with significance level of 5% is the LSTAR(1,1) model. The forecast of IHSG data for the next 15 period has Mean Absolute Percentage Error (MAPE) 2,932612%. Keywords : autoregressive, LSTAR, nonlinier, time series

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8

Skalin, Joakim, and Timo Teräsvirta. "MODELING ASYMMETRIES AND MOVING EQUILIBRIA IN UNEMPLOYMENT RATES." Macroeconomic Dynamics 6, no. 2 (2002): 202–41. http://dx.doi.org/10.1017/s1365100502031024.

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The paper discusses a simple univariate nonlinear parametric time-series model for unemployment rates, focusing on the asymmetry observed in many OECD unemployment series. The model is based on a standard logistic smooth transition autoregressive model for the first difference of unemployment, but it also includes a lagged level term. This model allows for asymmetric behavior by permitting “local” nonstationarity in a globally stable model. Linearity tests are performed for a number of quarterly, seasonally unadjusted, unemployment series from OECD countries, and linearity is rejected for a number of them. For a number of series, nonlinearity found by testing can be modeled satisfactorily by use of our smooth transition autoregressive model. The properties of the estimated models, including persistence of the shocks according to them, are illustrated in various ways and discussed. Possible existence of moving equilibria in series not showing asymmetry is investigated and modeled with another smooth transition autoregressive model.

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9

Hubner, Stefan, and Pavel Čížek. "Quantile-based smooth transition value at risk estimation." Econometrics Journal 22, no. 3 (2019): 241–61. http://dx.doi.org/10.1093/ectj/utz009.

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Summary Value at risk models are concerned with the estimation of conditional quantiles of a time series. Formally, these quantities are a function of conditional volatility and the respective quantile of the innovation distribution. The former is often subject to asymmetric dynamic behaviour, e.g., with respect to past shocks. In this paper, we propose a model in which conditional quantiles follow a generalised autoregressive process governed by two parameter regimes with their weights determined by a smooth transition function. We develop a two-step estimation procedure based on a sieve estimator, approximating conditional volatility by using composite quantile regression, which is then used in the generalised autoregressive conditional quantile estimation. We show that the estimator is consistent and asymptotically normal, and we complement the results with a simulation study. In our empirical application, we consider daily returns of the German equity index (DAX) and the USD/GBP exchange rate. Although only the latter follows a two-regime model, we find that our model performs well in terms of out-of-sample prediction in both cases.

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10

Xia, Qiang, Zhiqiang Zhang, and Wai Keung Li. "A Portmanteau Test for Smooth Transition Autoregressive Models." Journal of Time Series Analysis 41, no. 5 (2019): 722–30. http://dx.doi.org/10.1111/jtsa.12512.

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11

Eitrheim, Øyvind, and Timo Teräsvirta. "Testing the adequacy of smooth transition autoregressive models." Journal of Econometrics 74, no. 1 (1996): 59–75. http://dx.doi.org/10.1016/0304-4076(95)01751-8.

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12

CHEN, Hao, Fangxing LI, and Yurong WANG. "Wind power forecasting based on outlier smooth transition autoregressive GARCH model." Journal of Modern Power Systems and Clean Energy 6, no. 3 (2016): 532–39. http://dx.doi.org/10.1007/s40565-016-0226-3.

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13

Terasvirta, Timo. "Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models." Journal of the American Statistical Association 89, no. 425 (1994): 208. http://dx.doi.org/10.2307/2291217.

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14

Teräsvirta, Timo. "Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models." Journal of the American Statistical Association 89, no. 425 (1994): 208–18. http://dx.doi.org/10.1080/01621459.1994.10476462.

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15

Dijk, Dick van, Timo Teräsvirta, and Philip Hans Franses. "SMOOTH TRANSITION AUTOREGRESSIVE MODELS — A SURVEY OF RECENT DEVELOPMENTS." Econometric Reviews 21, no. 1 (2002): 1–47. http://dx.doi.org/10.1081/etc-120008723.

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16

Zhang, Xiaolei, and Zhen He. "Integrated statistical and engineering process control based on smooth transition autoregressive model." Transactions of Tianjin University 19, no. 2 (2013): 147–56. http://dx.doi.org/10.1007/s12209-013-1892-0.

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17

Livingston, Glen, and Darfiana Nur. "Bayesian inference for smooth transition autoregressive (STAR) model: A prior sensitivity analysis." Communications in Statistics - Simulation and Computation 46, no. 7 (2017): 5440–61. http://dx.doi.org/10.1080/03610918.2016.1161794.

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18

Amaral, Luiz Felipe, Reinaldo Castro Souza, and Maxwell Stevenson. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting." International Journal of Forecasting 24, no. 4 (2008): 603–15. http://dx.doi.org/10.1016/j.ijforecast.2008.08.006.

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19

Yoon, Gawon. "Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?" Economic Modelling 27, no. 2 (2010): 605–12. http://dx.doi.org/10.1016/j.econmod.2009.11.015.

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20

Buncic, Daniel. "Identification and Estimation Issues in Exponential Smooth Transition Autoregressive Models." Oxford Bulletin of Economics and Statistics 81, no. 3 (2018): 667–85. http://dx.doi.org/10.1111/obes.12264.

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21

Baharumshah, Ahmad Zubaidi, and Venus Khim-Sen Liew. "Forecasting Performance of Exponential Smooth Transition Autoregressive Exchange Rate Models." Open Economies Review 17, no. 2 (2006): 235–51. http://dx.doi.org/10.1007/s11079-006-6812-7.

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22

Terasvirta, T., and H. M. Anderson. "Characterizing nonlinearities in business cycles using smooth transition autoregressive models." Journal of Applied Econometrics 7, S1 (1992): S119—S136. http://dx.doi.org/10.1002/jae.3950070509.

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23

He, Qi-zhi. "Empirical Research on Repo Rates Based on Exponenti- al Smooth Transition Autoregressive Model." Journal of Service Science and Management 01, no. 01 (2008): 77–82. http://dx.doi.org/10.4236/jssm.2008.11007.

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24

Huang, Alex YiHou, and Wen-Cheng Hu. "Regime switching dynamics in credit default swaps: Evidence from smooth transition autoregressive model." Physica A: Statistical Mechanics and its Applications 391, no. 4 (2012): 1497–508. http://dx.doi.org/10.1016/j.physa.2011.08.008.

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25

CHEN, Hao, Fangxing LI, and Yurong WANG. "Erratum to: Wind power forecasting based on outlier smooth transition autoregressive GARCH model." Journal of Modern Power Systems and Clean Energy 7, no. 6 (2017): 1749. http://dx.doi.org/10.1007/s40565-016-0250-3.

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26

McAleer, Michael, and Marcelo C. Medeiros. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries." Journal of Econometrics 147, no. 1 (2008): 104–19. http://dx.doi.org/10.1016/j.jeconom.2008.09.032.

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27

Lei, Jieqi, Xuyuan Wang, Yiming Zhang, Lian Zhu, and Lin Zhang. "Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model." Complexity 2021 (January 18, 2021): 1–13. http://dx.doi.org/10.1155/2021/6659117.

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As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model; then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged.

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28

Xaba, Diteboho, Ntebogang Dinah Moroke, Johnson Arkaah, and Charlemagne Pooe. "A Comparative Study Of Stock Price Forecasting Using Nonlinear Models." Risk Governance and Control: Financial Markets and Institutions 7, no. 2 (2017): 7–17. http://dx.doi.org/10.22495/rgcv7i2art1.

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This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

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29

Yaya, OlaOluwa S., and Olanrewaju I. Shittu. "Symmetric Variants of Logistic Smooth Transition Autoregressive Models: Monte Carlo Evidences." Journal of Modern Applied Statistical Methods 15, no. 1 (2016): 711–37. http://dx.doi.org/10.22237/jmasm/1462077240.

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30

Bekiros, Stelios D. "A robust algorithm for parameter estimation in smooth transition autoregressive models." Economics Letters 103, no. 1 (2009): 36–38. http://dx.doi.org/10.1016/j.econlet.2009.01.020.

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31

Deschamps, Philippe J. "Comparing smooth transition and Markov switching autoregressive models of US unemployment." Journal of Applied Econometrics 23, no. 4 (2008): 435–62. http://dx.doi.org/10.1002/jae.1014.

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32

Hsu, Kuang-Chung, and Hui-Chu Chiang. "Nonlinear effects of monetary policy on stock returns in a smooth transition autoregressive model." Quarterly Review of Economics and Finance 51, no. 4 (2011): 339–49. http://dx.doi.org/10.1016/j.qref.2011.08.003.

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33

Silvennoinen, A., and T. Terasvirta. "Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model." Journal of Financial Econometrics 7, no. 4 (2009): 373–411. http://dx.doi.org/10.1093/jjfinec/nbp013.

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34

Umer, Usman M., Tuba Sevil, and Güven Sevil. "Forecasting performance of smooth transition autoregressive (STAR) model on travel and leisure stock index." Journal of Finance and Data Science 4, no. 2 (2018): 90–100. http://dx.doi.org/10.1016/j.jfds.2017.11.006.

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35

Umer, Usman M., Tuba Sevil, and Güven Sevil. "Forecasting performance of smooth transition autoregressive (STAR) model on travel and leisure stock index." Journal of Finance and Data Science 5, no. 1 (2019): 12–21. http://dx.doi.org/10.1016/j.jfds.2018.02.004.

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36

Wai, Seuk, Mohd Tahir Ismail ., and Siok Kun Sek . "A Study of Intercept Adjusted Markov Switching Vector Autoregressive Model in Economic Time Series Data." Information Management and Business Review 5, no. 8 (2013): 379–84. http://dx.doi.org/10.22610/imbr.v5i8.1065.

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Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adjusted after a shift. This prove that MSI-VAR model is suitable to use in examine the changes of the economic model and able to provide significance, valid and reliable results. While oil price and gold price also proved that as a factor in affecting the stock exchange.

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37

Livingston, Glen, and Darfiana Nur. "Bayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models." Statistical Papers 61, no. 6 (2018): 2449–82. http://dx.doi.org/10.1007/s00362-018-1056-3.

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38

Adedokun, Adebayo, Philip Akanni Olomola, and James Temitope Dada. "Does non-linearity in exchange rate hold in Nigeria evidence from smooth transition autoregressive model." International Journal of Monetary Economics and Finance 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijmef.2020.10034068.

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39

Dada, James Temitope, Philip Akanni Olomola, and Adebayo Adedokun. "Does non-linearity in exchange rate hold in Nigeria evidence from smooth transition autoregressive model." International Journal of Monetary Economics and Finance 14, no. 2 (2021): 152. http://dx.doi.org/10.1504/ijmef.2021.114024.

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40

Odelia, Maria, Di Asih I. Maruddani, and Hasbi Yasin. "PERAMALAN HARGA SAHAM DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR) (Studi Kasus pada Harga Saham Mingguan PT. Bank Mandiri Tbk Periode 03 Januari 2011 sampai 24 Desember 2018)." Jurnal Gaussian 9, no. 4 (2020): 391–401. http://dx.doi.org/10.14710/j.gauss.v9i4.29403.

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Series such as financial and economic data do not always form a linear model, so a nonlinear model is needed. One of the popular nonlinear models is the Smooth Transition Autoregressive (STAR). STAR has two possible suitable transition function such as logistic and exponential that need to be test to find the appropriate transition function. The purpose of writing this thesis is to determine the LSTAR model, then use the model to predict the stock price of PT Bank Mandiri. This study uses the data of the weekly stock price of PT Bank Mandiri from the period of January 3, 2011 to December 24, 2018 as insample data and the period of January 1, 2019 to December 30, 2019 as outsample data. The research procedure begins with modeling the data with the Autoregressive (AR) process, testing the linearity of the data, modeling with LSTAR, forecasting, and finally evaluating the results of forecasting. Evaluating the results of the forecasting of the weekly share price of PT Bank Mandiri with the STAR model results in the best nonlinear model LSTAR (1,1). This model produces an highly accurate forecasting result with a value of symmetric Mean Square Error (sMAPE) to be 5.12%.Keywords: Nonlinear, Time Series, STAR, LSTAR.

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41

Alimi, Mohsen, Ahmed Rhif, and Abdelwaheb Rebai. "Nonlinear dynamic of the renewable energy cycle transition in Tunisia: Evidence from smooth transition autoregressive models." International Journal of Hydrogen Energy 42, no. 13 (2017): 8670–79. http://dx.doi.org/10.1016/j.ijhydene.2016.07.131.

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42

Huang, Ying, Carl R. Chen, and Maximo Camacho. "Determinants of Japanese Yen interest rate swap spreads: Evidence from a smooth transition vector autoregressive model." Journal of Futures Markets 28, no. 1 (2007): 82–107. http://dx.doi.org/10.1002/fut.20281.

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43

Aznarte M., José Luis, José Manuel Benítez, and Juan Luis Castro. "Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences." Fuzzy Sets and Systems 158, no. 24 (2007): 2734–45. http://dx.doi.org/10.1016/j.fss.2007.03.021.

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44

Qu, Hui, Wei Chen, Mengyi Niu, and Xindan Li. "Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models." Energy Economics 54 (February 2016): 68–76. http://dx.doi.org/10.1016/j.eneco.2015.12.001.

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45

Babangida, Jamilu S., and Asad-Ul I. Khan. "Effect of Monetary Policy on the Nigerian Stock Market: A Smooth Transition Autoregressive Approach." Central Bank of Nigeria Journal of Applied Statistics 12, No. 1 (2021): 1–21. http://dx.doi.org/10.33429/cjas.12121.1/6.

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This paper examines the nonlinear effect of monetary policy decisions on the performance of the Nigerian Stock Exchange market, by employing the Smooth Transition Autoregressive (STAR) model on monthly data from 2013 M4 to 2019 M12 for All Share Index and monetary policy instrument. This study considers the two regimes characterizing the stock market, which are the lower regime (the bear market) and the upper regime (the bull market). The results show evidence of nonlinear effect of monetary policy on the stock exchange market. Monetary policy rate, money supply, lagged monetary policy rate and lagged treasury bill rate are found to have significant positive effects on the stock exchange market in the lower regime while current treasury bill rate shows a negative effect. In the upper regime, money supply and lagged treasury bill rate have significant negative effect on the stock market. The current treasury bill rate is found to have a positive effect on the stock exchange market. It is recommended that the Central Bank of Nigeria should maintain a stable money supply growth that is consistent with increased activities in the Nigerian stock market.

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46

Shah, Ismail, Hasnain Iftikhar, and Sajid Ali. "Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique." Forecasting 2, no. 2 (2020): 163–79. http://dx.doi.org/10.3390/forecast2020009.

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The increasing shortage of electricity in Pakistan disturbs almost all sectors of its economy. As, for accurate policy formulation, precise and efficient forecasts of electricity consumption are vital, this paper implements a forecasting procedure based on components estimation technique to forecast medium-term electricity consumption. To this end, the electricity consumption series is divided into two major components: deterministic and stochastic. For the estimation of deterministic component, we use parametric and nonparametric models. The stochastic component is modeled by using four different univariate time series models including parametric AutoRegressive (AR), nonparametric AutoRegressive (NPAR), Smooth Transition AutoRegressive (STAR), and Autoregressive Moving Average (ARMA) models. The proposed methodology was applied to Pakistan electricity consumption data ranging from January 1990 to December 2015. To assess one month ahead post-sample forecasting accuracy, three standard error measures, namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), were calculated. The results show that the proposed component-based estimation procedure is very effective at predicting electricity consumption. Moreover, ARMA models outperform the other models, while NPAR model is competitive. Finally, our forecasting results are comparatively batter then those cited in other works.

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47

Franses, Philip Hans, and Timo Teräsvirta. "INTRODUCTION TO THE SPECIAL ISSUE: NONLINEAR MODELING OF MULTIVARIATE MACROECONOMIC RELATIONS." Macroeconomic Dynamics 5, no. 4 (2001): 461–65. http://dx.doi.org/10.1017/s136510050102301x.

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During the past decade, the popularity of nonlinear models in econometrics has been increasing quite rapidly. Nonlinear models are now widely used for modeling macroeconomic relationships, and they also are used frequently in financial econometrics. The most popular nonlinear models have been univariate. Threshold autoregressive, Markov switching autoregressive, and smooth-transition autoregressive models, just to name a few popular families of models, have been widely applied to modeling of macroeconomic series. Even nonlinear multivariate single-equation models have found application in areas where linear single-equation models traditionally have been used, such as modeling the demand for money, real exchange rates, consumption–income relationship, and house prices. Interest in nonlinearities in the Phillips curve also has grown recently.

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48

Li, Wenying, Yunhan Li, and Jeffrey H. Dorfman. "Dynamically Changing Cattle Market Linkages with Supply-Side-Controlled Transitions." Journal of Agricultural and Applied Economics 51, no. 3 (2019): 472–84. http://dx.doi.org/10.1017/aae.2019.14.

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AbstractCattle are costly to transport, which could lead to segmented regional cattle markets. The cointegration of cattle prices over regions has been of research interest for decades. This article investigates price cointegration between regional cattle markets in the United States and proposes a simple procedure for incorporating a flexible transition function into an economic indicator–controlled smooth transition autoregressive (ECON-STAR) model to evaluate market dynamics. The empirical results show that these markets have been highly integrated when excess supply exists, but when cattle inventories decrease, the market pattern becomes very regionally segmented.

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49

Karaoğlu, Nazlı, and Serdar Kılıçkaplan. "Estimation of Exchange Rate Pass-Through to Domestic Prices with Smooth Transition Autoregressive Models." Ekonomik Teori ve Analiz Dergisi 3, no. 3 (2018): 195–215. http://dx.doi.org/10.25229/beta.465635.

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50

Shintani, Mototsugu. "THE INF-TTEST FOR A UNIT ROOT AGAINST ASYMMETRIC EXPONENTIAL SMOOTH TRANSITION AUTOREGRESSIVE MODELS." Japanese Economic Review 64, no. 1 (2013): 3–15. http://dx.doi.org/10.1111/jere.12005.

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