Smooth Transition Autoregressive Research Articles

Investigating environmental Kuznets curve from an energy intensity perspective: Empirical evidence from China

This study pioneers investigating the environmental Kuznets curve (EKC) hypothesis and its possible regional differential characteristics in China. Based on 2003–2017 years of provincial panel data, this research employs panel smooth transition auto regression (PSTR) model to analyze the impact of income levels on environmental pollution and identify the EKC threshold of energy intensity. Then, 30 Chinese provinces were categorized by energy intensity to examine the inter-provincial and interregional differences in the EKC threshold of energy intensity, rather than the traditional threshold of income. The results indicate that China's pollution emissions and energy intensities show a stepwise decreasing pattern from the western region to the eastern region. In addition, the impact of income levels on pollution emissions is non-linear, and the critical value of energy intensity is 0.9168 between the high- and low regimes. An inverted U-shaped environmental Kuznets curve is accepted for energy intensity, with 0.7670 as its threshold value. When the energy intensity is higher (lower) than the threshold value, the income elasticity of pollution emission is positive (negative). The more developed provinces and municipalities mainly in the eastern region are proved to exceed the threshold than the provinces in the central and western regions within the sample period. Given these findings, this study further divides China's provinces into eco-friendly, low-pollution, and high-pollution provinces, and accordingly, the most important policy recommendations were discussed for policy-makers and researchers. This research can provide a new insight to investigate countries with unbalanced development levels within and a reference for environmental governance policies in sub-regions.

A remaining useful life prediction method for airborne fuel pump after maintenance

Remaining useful life prediction is the core of condition-based maintenance under the technology framework of prognostic and health management. But the remaining useful life of airborne fuel pump after maintenance is difficult to predict because of the multi-stage noise and small data size. A new method is proposed to solve the remaining useful life prediction of repaired fuel pump. Firstly, an alternative smooth transition auto-regression model logistic smooth transition auto-regression or exponential smooth transition auto-regression is proposed to reduce the multi-stage noise. Secondly, random effect Wiener process is utilized to model the de-noised degradation data, and the posterior parameters of remaining useful life prediction after maintenance are derived by the Bayesian method based on the parameters before maintenance. Finally, the method proposed above is compared with the methods which neglect the multi-stage noise and information before maintenance, comparative results show that the proposed method can improve the remaining useful life prediction accuracy significantly.

Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years’ weekly data and then out-of-sample forecast next 12 weeks’ return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period.

Sentiment-Induced Bubbles in the Cryptocurrency Market

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect.

Estimation of STAR–GARCH Models with Iteratively Weighted Least Squares

This study applies the Iteratively Weighted Least Squares (IWLS) algorithm to a Smooth Transition Autoregressive (STAR) model with conditional variance. Monte Carlo simulations are performed to measure the performance of the algorithm, to compare its performance to the established methods in the literature, and to see the effect of the initial value selection method. The simulation results show that lower bias and mean squared error values are received for the slope parameter estimator from the IWLS algorithm in comparison to the other methods when the real value of the slope parameter is low. In an empirical illustration, the STAR–GARCH model is used to forecast daily US Dollar/Australian Dollar and the FTSE Small Cap index returns. 1-day ahead out-of-sample forecast results show that the forecast performance of the STAR–GARCH model improves with the IWLS algorithm and the model performs better than the benchmark model.

Döviz Kurunun Yurt İçi Fiyatlara Geçiş Etkisinin Yumuşak Geçişli Regresyon Modeliyle Tahmini

Döviz kurundaki değişimin yurt içi fiyatlar üzerindeki etkisinin bilinmesi enflasyon hedeflemesi stratejisini benimseyen ekonomiler için oldukça önemlidir. Türkiye’de ithal ara malı kullanımı yüksek olduğundan, başta Merkez Bankası olmak üzere iktisadi birimler döviz kurunun fiyatlara geçiş etkisini yakından takip etmektedir. Bu nedenle, geçiş etkisi güncel olarak araştırılması gereken bir konudur. Doğrusal modeller kullanılarak yapılan çalışmalarda geçiş etkisinin simetrik olduğu varsayılmaktadır. Bu çalışmanın amacı, enflasyon düzeyinin, döviz kurunun tüketici fiyatlarına geçiş etkisini azaltacağı yönündeki Taylor(2000) hipotezinin geçerliliğini Türkiye için incelemektir. Bu incelemede doğrusal olmayan zaman serisi yöntemlerinden yumuşak geçişli regresyon modelden (STR modelinden) yararlanılmıştır. Bu amaçla 2004:01–2018:07 dönemine ilişkin aylık veriler kullanılmıştır. Yıllık tüketici fiyat enflasyonu %7’yi aştığında döviz kurunun tüketici fiyatlarına geçiş etkisi %7,6’dan %11,6’ya çıkmaktadır. Aynı şekilde yıllık üretici fiyat enflasyonu %4,4’ü aştığında döviz kurunun üretici fiyatlarına geçiş etkisinin %24,1’den %37,5’e çıktığı görülmüştür. Çalışmanın sonunda döviz kurunun üretici ve tüketici fiyatlarına geçiş etkisinin enflasyon düzeyine göre doğrusal olmadığı ortaya konulmuş ve Taylor(2000) hipotezinin ilgili dönemde Türkiye’de geçerli olduğu sonucuna ulaşılmıştır.

Bayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models

The smooth transition autoregressive (STAR)(k)–GARCH(l, m) model is a non-linear time series model that is able to account for changes in both regime and volatility respectively. The model can be widely applied to analyse the dynamic behaviour of data exhibiting these two phenomenons in areas such as finance, hydrology and climate change. The main aim of this paper is to perform a Bayesian analysis of STAR(k)–GARCH(l, m) models. The estimation procedure will include estimation of the mean and variance coefficient parameters, the parameters of the transition function, as well as the model orders (k, l, m). To achieve this aim, the joint posterior distribution of the model orders, coefficient and implicit parameters in the logistic STAR(k)–GARCH(l, m) model is presented. The conditional posterior distributions are then derived, followed by the design of a posterior simulator using a combination of MCMC algorithms which includes Metropolis–Hastings, Gibbs Sampler and Reversible Jump MCMC algorithms. Following this are extensive simulation studies and a case study presenting the methodology.

Revisiting the Effects of Free Trade Agreement (FTA) on PPP Using the ESTAR Model: Evidence From South Korea

I explore whether the Free Trade Agreement (FTA) has a positive influence on PPP between South Korea and its FTA partners in this study. There are a few studies on the effects of FTA on PPP, but they do not find positive effects of FTA on PPP even though trade barriers, especially, tariffs have decreased or eliminated after FTA. One reason for this may be that the empirical models used in earlier studies were incorrectly specified. In particular, the models are unable to assess whether trade does not occur. By using the Exponential Smooth Transition Autoregressive (ESTAR) model, which separates the case where trade does not occur from the case where trade occurs, I revisit the effects of FTA on PPP between South Korea and its FTA partners in the sample periods over January 1998 to May 2018. It was found that the speed of mean-reversion of price differential is faster after FTA for most ASEAN FTA partners, but the same thing is not observed for other FTA partners. These findings imply that the effects of FTA on PPP are different across FTA partners, which means the effects of FTA on market integration are different across FTA partners.

Asymmetric effects of monetary policy in the U.S and Brazil

We empirically test the effects of anticipated and unanticipated monetary policy shocks on the growth rate of real industrial production and explicitly test for different types of asymmetries in monetary policy implementation for two major international economies, the U.S. and Brazil. We depart from the conventional method of VAR analysis to estimate unanticipated monetary shocks, and instead, we use a combination of alternative methodologies. We first identify the Taylor rule that best describes the reaction of both central banks and then we test both forward looking linear and nonlinear models. We conclude that a Logistic Smooth Transition Autoregressive (LSTAR) forward looking model of the Taylor rule best describes the US FED Funds rate while a linear Taylor rule with the inclusion of a dummy variable best describes the reaction of the Central Bank of Brazil (BCB). We then use in-sample forecast errors in order to derive and identify the unexpected monetary policy shocks for both countries. In line with Cover (1992), we use these shocks to explore any asymmetries in the conduct of monetary policy on the growth rate of the real industrial production. We also find asymmetries between anticipated and unanticipated monetary shocks as well as between effects of positive and negative shocks.

Forecasting dynamically asymmetric fluctuations of the U.S. business cycle

The generalized smooth transition autoregression (GSTAR) parametrizes the joint asymmetry in the duration and length of cycles in macroeconomic time series by using particular generalizations of the logistic function. The symmetric smooth transition and linear autoregressions are nested in the GSTAR. A test for the null hypothesis of dynamic symmetry is presented. Two case studies indicate that dynamic asymmetry is a key feature of the U.S. economy. The GSTAR model beats its competitors for point forecasting, but this superiority becomes less evident for density forecasting and in uncertain forecasting environments.

Nonlinear and asymmetric pricing behaviour in the Spanish gasoline market

Abstract Over the last decades a transition from a state-own monopoly to a private business took place in the Spanish fuel sector. To figure out whether downstream prices react differently to upstream price increases than to price decreases, alternative dynamic nonlinear and asymmetric error correction models are applied to weekly price data. This paper analyse the existence of price asymmetries in the fuel market in Spain during the 2011–2016 period. In comparison with traditional asymmetric price theory literature, this paper introduces a new double threshold error correction (ECM) model (DT-ECM) and new double logistic ECM models and compares them with more common linear ECM, time varying parameter models (TV-ECM), threshold autoregressive models (T-ECM), smooth transition autoregressive (STAR) models and nonlinear error correction (Logistic-ECM) and double threshold Logistic (DT-Logistic ECM). The asymmetric results found in Spain in the oil sector, show that sophisticated bivariate short-run nonlinearities are present in the gasoline market prices and that those price reactions depend on two main aspects; whether the oil price increases or decreases and on the stage (above of below) of the prices of gasoline relative to their long-run expected crude oil prices (error correction term). Those empirical results are consistent with the economic explanations based on market power-collusion and/or with consumer having search costs.

Forecasting Inflation Uncertainty in the G7 Countries

There is substantial evidence that inflation rates are characterized by long memory and nonlinearities. In this paper, we introduce a long-memory Smooth Transition AutoRegressive Fractionally Integrated Moving Average-Markov Switching Multifractal specification [ STARFIMA ( p , d , q ) - MSM ( k ) ] for modeling and forecasting inflation uncertainty. We first provide the statistical properties of the process and investigate the finite sample properties of the maximum likelihood estimators through simulation. Second, we evaluate the out-of-sample forecast performance of the model in forecasting inflation uncertainty in the G7 countries. Our empirical analysis demonstrates the superiority of the new model over the alternative STARFIMA ( p , d , q ) - GARCH -type models in forecasting inflation uncertainty.

Comparing the forecasting ability of financial conditions indices: The case of South Africa

In this paper we test the forecasting ability of three estimated financial conditions indices (FCIs) with respect to key macroeconomic variables of output growth, inflation and interest rates. We do this by forecasting the aforementioned macroeconomic variables based on the information contained in the three alternative FCIs using a Bayesian VAR (BVAR), nonlinear logistic vector smooth transition autoregression (VSTAR) and nonparametric (NP) and semi-parametric (SP) regressions, and compare the results with the standard benchmarks of random-walk, univariate autoregressive and classical VAR models. The three FCIs are constructed using rolling-window principal component analysis (PCA), dynamic model averaging (DMA) in the context of a time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model, and a time-varying parameter vector autoregressive (TVP-VAR) model with constant factor loadings. Our results suggest that the VSTAR model performs best in the case of forecasting output and inflation, while a SP specification proves to be the best for forecasting the interest rate. More importantly, statistical testing for significant differences in forecast errors across models corroborates the finding of superior predictive ability of the nonlinear models.

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

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

WITHDRAWN: Forecasting performance of smooth transition autoregressive (STAR) model on travel and leisure stock index

WITHDRAWN: Forecasting performance of smooth transition autoregressive (STAR) model on travel and leisure stock index

Government Size and Economic Growth in Asia - Evidence from China and Japan

In this paper, we examined the relationship between government size, proxied as general government consumption expenditure in GDP and economic growth, measured as real per capita GDP growth under Smooth Transition Autoregressive (STAR) approach in China (a developing country) and Japan (a developed country) over 1971-2013 period. Results show that Exponential STAR (ESTAR) is better fitted for China. Meanwhile, there is no convergence for Japan, means that this relationship should be explained by an alternative non-linear model. The threshold value of government size for China is found at 14.23% (or 14.18%). However, BARS curve is not really supported. Economic growth still is marginally positive when government expenditure exceeds this value. In spite of that, this also implies inefficient use of resources and government should pay more attention on this issue to enhance economic growth.

Forecasting performance of smooth transition autoregressive (STAR) model on travel and leisure stock index

Travel and leisure recorded a consecutive robust growth and become among the fastest economic sectors in the world. Various forecasting models are proposed by researchers that serve as an early recommendation for investors and policy makers. Numerous studies proposed distinct forecasting models to predict the dynamics of this sector and provide early recommendation for investors and policy makers. In this paper, we compare the performance of smooth transition autoregressive (STAR) and linear autoregressive (AR) models using monthly returns of Turkey and FTSE travel and leisure index from April 1997 to August 2016. MSCI world index used as a proxy of the overall market. The result shows that nonlinear LSTAR model cannot improve the out-of-sample forecast of linear AR model. This finding demonstrates little to be gained from using LSTAR model in the prediction of travel and leisure stock index.

Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

Nonlinear impacts of debt ratio and term spread on inward FDI performance persistence

This paper adopts panel smooth transition autoregression models with the proxies of monetary and fiscal policies (i.e., term spread and debt ratio) as the transition variables to estimate inward foreign direct investment (FDI) performance and its persistence. The models can trace the characteristics of inflow FDI performance in nonlinearity, heterogeneity, and persistence. Empirical results show that the inward FDI performance and its persistence for the ten OECD countries during 1996–2010 are nonlinear and vary with time and across countries, depending on the proxies in different regimes. The threshold values for the term spread and debt ratio are 2.51 and 64.9847%, respectively. Moderately enhancing the term spread and the debt ratio is helpful to exclude specific exogenous disturbance on FDI inflows and stabilize the persistence of FDI inflow performance. However, debt issuance causes a transitorily volatile effect on the persistence (i.e., a negative persistence effect) more easily than monetary policy.