Academic literature on the topic 'Exponential smooth transition autoregressive model'

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Journal articles on the topic "Exponential smooth transition autoregressive model"

1

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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2

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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3

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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4

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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5

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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6

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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7

Yoon, Gawon. "Nonlinear mean-reversion to purchasing power parity: exponential smooth transition autoregressive models and stochastic unit root processes." Applied Economics 42, no. 4 (2010): 489–96. http://dx.doi.org/10.1080/00036840701604552.

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8

Otero, Jesús, and Jeremy Smith. "Response Surface Models for OLS and GLS Detrending-based Unit-root Tests in Nonlinear ESTAR Models." Stata Journal: Promoting communications on statistics and Stata 17, no. 3 (2017): 704–22. http://dx.doi.org/10.1177/1536867x1701700310.

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In this article, we calculate response surface models for a large range of quantiles of the Kapetanios, Shin, and Snell (2003, Journal of Econometrics 112: 359–379) and Kapetanios and Shin (2008, Economics Letters 100: 377–380) tests for the null hypothesis of a unit root against the alternative—that the series of interest follows a globally stationary exponential smooth transition autoregressive process. The response surface models allow estimation of finite-sample critical values and approximate p-values for different combinations of the number of observations, T, and the lag order in the test regression, p. The latter can be either specified by the user or optimally selected using a data-dependent procedure. We present the new commands kssur and ksur and illustrate their use with an empirical example.
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9

Cagli, Efe Caglar, Dilvin Taskin, and Pınar Evrim Mandaci. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models." Energy Economics 84 (October 2019): 104540. http://dx.doi.org/10.1016/j.eneco.2019.104540.

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10

Gregoriou, Andros. "Modelling non-linear behaviour of block price deviations when trades are executed outside the bid-ask quotes." Journal of Economic Studies 44, no. 2 (2017): 206–13. http://dx.doi.org/10.1108/jes-03-2016-0050.

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Purpose The purpose of this paper is to test and model non-linearities in block price deviations when they are executed outside the bid-ask quotes. The author conducts an empirical analysis on 662,312 transactions that were traded outside the bid-ask quotes in 2014 on the London Stock Exchange. Design/methodology/approach The tests reject the linearity hypothesis and the paper shows that the exponential smooth transition autoregressive model is capable of capturing the non-linear behaviour of block price misalignments. Findings The findings imply that when the deviation of block prices from their quoted value is small (large), trading will occur slowly (rapidly) to restore equilibrium, suggesting that trading costs eliminate continuous trading and that the block trade market is efficient. Originality/value The purpose of this paper is to re-model block price deviations from the bid-ask quotes. The major contribution is that the paper presents new empirical evidence, which explicitly allows for the possibility that block price misalignments from the bid-ask quotes can be characterized by a non-linear mean reverting process. The author demonstrates that the presence of transaction costs induces non-linear adjustments of block trade prices.
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