scholarly journals Dynamically Changing Cattle Market Linkages with Supply-Side-Controlled Transitions

2019 ◽  
Vol 51 (3) ◽  
pp. 472-484
Author(s):  
Wenying Li ◽  
Yunhan Li ◽  
Jeffrey H. Dorfman
Keyword(s):  
Simple Procedure ◽  
Economic Indicator ◽  
The United States ◽  
Transition Function ◽  
Smooth Transition ◽  
Supply Side ◽  
Star Model ◽  
Cattle Markets ◽  
Cattle Market ◽  
Smooth Transition Autoregressive

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.

scholarly journals PERAMALAN INDEKS HARGA SAHAM GABUNGAN DENGAN METODE LOGISTIC SMOOTH TRANSITION AUTOREGRESSIVE (LSTAR)

2018 ◽  
Vol 7 (1) ◽  
pp. 84-95
Author(s):  
Gayuh Kresnawati ◽  
Budi Warsito ◽  
Abdul Hoyyi
Keyword(s):  
Time Series ◽  
Transition Function ◽  
Mean Absolute Percentage Error ◽  
Ar Model ◽  
Smooth Transition ◽  
Percentage Error ◽  
Star Model ◽  
Significance Level ◽  
Absolute Percentage Error ◽  
Smooth Transition Autoregressive

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

scholarly journals 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)

2020 ◽  
Vol 9 (4) ◽  
pp. 391-401
Author(s):  
Maria Odelia ◽  
Di Asih I Maruddani ◽  
Hasbi Yasin
Keyword(s):  
Nonlinear Model ◽  
Stock Price ◽  
Nonlinear Models ◽  
Transition Function ◽  
Share Price ◽  
Smooth Transition ◽  
Star Model ◽  
Process Testing ◽  
Research Procedure ◽  
Smooth Transition Autoregressive

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.

scholarly journals Short-Run Demand Relationships in the U.S. Fats and Oils Complex

2003 ◽  
Vol 35 (1) ◽  
pp. 171-184 ◽  
Author(s):  
Barry K. Goodwin ◽  
Daniel Harper ◽  
Randy Schnepf
Keyword(s):  
Fats And Oils ◽  
The United States ◽  
Transition Function ◽  
Smooth Transition ◽  
Demand System ◽  
Small Scale ◽  
Almost Ideal Demand System ◽  
System Specification ◽  
Edible Fats ◽  
Short Run

Fats and oils play a prominent role in U.S. dietary patterns. Recent concerns over the negative health consequences associated with fats and oils have led many to suspect structural change in demand conditions. Our analysis considers short run (monthly) demand relationships for edible fats and oils. In that monthly quantities of fats and oils are likely to be relatively fixed, an inverse almost ideal demand system specification is used. A smooth transition function is used to model a switching inverse almost ideal demand system that assesses short-run demand conditions for edible fats and oils in the United States. The results suggest that short-run demand conditions for fats and oils experienced a gradual structural shift that began in the late 1980s or early 1990s and persisted into the mid-1990s. Although this shift generally made price flexibilities more elastic, differences in scale flexibilities across regimes were modest in most cases. The results suggest that decreases in marginal valuations for most fats and oils in response to consumption increases are rather small. Scale flexibilities are relatively close to –1, suggesting near homothetic preferences for fats and oils.

scholarly journals Estimating Asymmetric Advertising Response: An Application to U.S. Nonalcoholic Beverage Demand

2008 ◽  
Vol 40 (3) ◽  
pp. 837-849 ◽  
Author(s):  
Yuqing Zheng ◽  
Harry M. Kaiser
Keyword(s):  
United States ◽  
Regime Switching ◽  
The United States ◽  
Transition Function ◽  
Smooth Transition ◽  
Regime Switching Model ◽  
Profound Impact ◽  
Switching Model ◽  
Model Features

We propose a regime-switching model that allows demand to respond asymmetrically to upward and downward advertising changes. With the introduction of a smooth transition function, the model features smooth rather than abrupt parameter changes between regimes. We apply the model to nonalcoholic beverage data in the United States for 1974 through 2005 to investigate asymmetric advertising response. Results indicate that a decrease in milk advertising had a more profound impact on milk demand than an increase did. An increase in milk advertising had no impact on milk demand, but a decrease could have an own-advertising elasticity up to 0.049.

scholarly journals Autoregressive modeling and diagnostics for qPCR amplification

2020 ◽  
Author(s):  
Benjamin Hsu ◽  
Valeriia Sherina ◽  
Matthew N McCall
Keyword(s):  
Quantitative Polymerase Chain Reaction ◽  
Parametric Models ◽  
Model Fit ◽  
Smooth Transition ◽  
Supplementary Information ◽  
Gradual Transition ◽  
Star Model ◽  
Fluorescence Measurements ◽  
Amplification Process ◽  
Smooth Transition Autoregressive

Abstract Motivation Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of the amplification process that underlies qPCR fluorescence measurements. Results We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals. Availability and implementation R scripts to reproduce all the analyses and results described in this manuscript can be found at: https://github.com/bhsu4/GAPDH.SO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Policy and Law Assessment of COVID-19 Based on Smooth Transition Autoregressive Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jieqi Lei ◽  
Xuyuan Wang ◽  
Yiming Zhang ◽  
Lian Zhu ◽  
Lin Zhang
Keyword(s):  
Prevention And Control ◽  
Control Measures ◽  
Smooth Transition ◽  
Cumulative Number ◽  
Star Model ◽  
The World ◽  
Transition Points ◽  
And Control ◽  
The Impact ◽  
Smooth Transition Autoregressive

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.

scholarly journals UNEMPLOYMENT HYSTERESIS IN TURKEY: EVIDENCE FROM NONLINEAR UNIT ROOT TESTS WITH FOURIER FUNCTION

2019 ◽  
Vol 20 (3) ◽  
pp. 178-188
Author(s):  
Burak Güriş ◽  
Gülşah Sedefoğlu
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Development Process ◽  
Structural Breaks ◽  
Unit Root Tests ◽  
Smooth Transition ◽  
Star Model ◽  
Fourier Function ◽  
Smooth Transition Autoregressive ◽  
Nonlinear Unit Root Tests

The purpose of the article is to give brief information about the development process of time series analysis and to test the validity of the unemployment hysteresis in Turkey for female and male graduates for the years from 1988 to 2013. For this purpose, Kapetanios et al. [2003], Sollis [2009] and Kruse [2011] nonlinear unit root tests are applied based on the smooth transition autoregressive (STAR) model. Besides, nonlinear unit root tests proposed by Christopoulos et al. [2010] and Guris [2018] are employed to model the structural breaks through Fourier approach and to model the nonlinearity through a STAR model.

scholarly journals The Cyclical Behaviour of the Small-Cap Premium: A Regime-Switching Approach

2020 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Lorne N. Switzer ◽  
Alan Picard
Keyword(s):  
Regime Switching ◽  
Factor Model ◽  
Smooth Transition ◽  
Star Model ◽  
Financial Variables ◽  
The Us ◽  
Model Star ◽  
Cyclical Behaviour ◽  
The Business Cycle ◽  
Smooth Transition Autoregressive

While the average annual small-cap premia for the US and Canada are substantial over long horizons, there is considerable time variation of this premium within and across these countries. For the US, during expansions, the average annualized premium is a sizable 5.44%, while during recessions, there is a small-cap discount of 6.23%. The differentials are less pronounced in Canada. This paper investigates the hypothesis that the variation of the small-cap premium is related to macroeconomic and financial variables that can be captured by a nonlinear time series econometric model, i.e., the smooth transition autoregressive model (STAR model), with different factor sets across regimes between and countries. The regimes reflect expansionary vs. contractionary phases of the business cycle. For the Canadian small-cap premium, an augmented factor model that includes US factors dominates a purely domestic factor model, which is consistent with integrated markets.

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