Threshold Autoregressive Research Articles

The Potential of The Hotel Industry In Pandemic Era Based On Finance Performance Point Of View, Case Study: Indonesia

This article aims to investigate and analyze the potential of the hospitality industry by comparing the potential occupancy rates and hotel revenues of foreign and domestic tourists. This investigation uses an investigation of company data obtained from reports from hotel companies throughout Indonesia which are listed on the Indonesia Stock Exchange and secondary data obtained from world banks and other reliable data. This study uses behavioral data analysis using Threshold Autoregressive from 2000 to 2019. It was found that domestic tourists are a new hope that needs to be considered in surviving and restoring the hospitality industry after being exposed to the COVID-19 pandemic which has led hotel companies. temporarily closed operations and part of the hotel went bankrupt. Optimization of domestic tourists allowed the hotel industry to develop rapidly after the Covid-19 pandemic ended.

MONETARY OUTLOOK

This paper aims to study the transformation of money in the United States using qualitative content analysis and predict the stability of the internal exchange rate of money by comparing the internal exchange rate of commodity money proxied by gold against crude oil internally. The exchange rate of fiat money proxied by the USD against crude oil and the internal exchange rate of synthetic money proxied by bitcoin against crude oil use the autoregressive threshold (TAR) method in the exchange period. In the great depression, fiat standards, subprime mortgage crisis, Europe experienced a debt crisis until 2017 (1960 - 2017). We compare the internal stability of money for each period in the aggregate using TAR to describe the overall fluctuation of internal exchange rate stability. So it can be seen that the behavior of data movements based on the crisis period experienced is the basis for predicting the stability of the internal exchange rate in the future.

Timing of tick size reduction: Threshold and smooth transition model analysis

“Tick size” is the minimum subdivision of price at which investors can buy or sell an asset in a financial market. Tick size is reduced to improve market quality and liquidity. This study analyzes the volume dimension of liquidity—specifically, trading volume—to determine the time of impact of the 2014 tick size reduction policy, implemented in two phases (P1 and P2) by the Tokyo Stock Exchange. Threshold autoregressive (TAR) and logistic smooth transition autoregressive (LSTAR) models are used to estimate this time of impact. The results show that the use of the TAR model changed the trading volumes for P1 and P2 on December 13, 2013, and June 30, 2014, respectively. With the addition of the LSTAR model, the trading volumes of P1 and P2 changed on December 16, 2013, and September 3, 2014, respectively. These results suggest that empirical studies on tick size reduction need longer interruptions.

Türkiye’de Sektör ve Kaynak Bazlı Enerji Kullanımları Yakınsıyor mu? Panel TAR ve Çoklu Kırılmalı Birim Kök Bulguları

Alternative solutions such as green growth, sustainable growth and increasing the share of renewable resources in energy use are proposed to eliminate the environmental pressure caused by fossil fuels. Based on this, in this study, a possible convergence relationship among the energy sources on a sectoral basis, and if so, whether this relationship is realized through fossil or renewable resources are empirically tested in the period of 1970-2017 for Turkey where sectoral transformations have taken place. Panel TAR (Threshold Autoregressive Models) data analysis findings show that there is no convergence relationship between the relevant sectors under the linear model, but there is a partial convergence under two regimes with different forms. In addition, the findings of Lumsdaine-Papell multiple structural breaks unit root analysis provide evidence that this partial convergence is realized through fossil fuels used in the relevant sectors. Renewable resources do not show convergence in any sector due to the presence of limited use areas within the sectors. This finding is considered as an indicator that the renewable energy policies and incentives in Turkey are insufficient in practice.

Environmental sustainability statement of economic regimes with energy intensity and urbanization in Turkey: a threshold regression approach.

In recent time, the investigation of the state of environmental quality has largely been conducted with less attention on the situation of environment sustainability especially in different economic regimes (expansion and recession). In the current context, the role of income per capita, energy intensity, and urbanization in driving the ecological footprint of Turkey is examined in the framework of Environmental Kuznets Curve (EKC) hypothesis over the period of 1990-2015. Considering the potential evidence of regime switching, we employed the Threshold Autoregressive Model (TAR) method with a regime change threshold of 14.43505 per hectare per capita and found that the EKC hypothesis is valid for all the 4 models. Moreover, eight observations are below the threshold value in the first regime while fifteen observations are equal or higher than the threshold value in the second regime. With a threshold per capita income of 9340.1326 USD, the study found that Turkey begin to experience a decline in environmental degradation resulting from income growth in 2015. However, this desirable outcome was short-lived in 2018 because the per capita income slightly decreases to 9340.1326 USD. In addition, increases in energy intensity and urbanization level hamper environmental sustainability drive of the country. The frequency domain causality test further supports the nexus evidence among the implied variables. By virtue of observation, this study offers that the government should work toward achieving a sustainable growth in order to attain the country's environmental sustainability agenda.

Threshold autoregressive model blind identification based on array clustering

In this paper, we propose a new algorithm to estimate all the parameters of a Self Exited Threshold AutoRegressive (SETAR) model from an observed time series. The aim of this algorithm is to relax all the hypotheses concerning the SETAR model for instance, the knowledge (or assumption) of the number of regimes, the switching variables, as well as of the switching function. For this, we reverse the usual framework of SETAR model identification of the previous papers, by first identifying the AR models using array clustering (instead of the switching variables and function) and second the switching conditions (instead of the AR models). The proposed algorithm is a pipeline of well-known algorithms in image/data processing allowing us to deal with the statistical non-stationarity of the observed time series. We pay a special attention on the results of each step over the possible discrepancies over the following step. Since we do not assume any SETAR model property, asymptotical properties of the identification results are difficult to derive. Thus, we validate our approach on several experiment sets. In order to assess the performance of our algorithm, we introduce global metrics and ancillary metrics to validate each step of the proposed algorithm.

Convergence analysis of the ecological footprint: theory and empirical evidence from the USMCA countries.

The lower ecological footprint (EF) is the sine qua non condition of cleaner energy. The purpose of this study is to investigate the convergence of per capita ecological footprint for the USMCA (The North American Free Trade Agreement) countries, involving the USA, Canada, and Mexico, over the 1961 to 2016. To this aim, the TAR (threshold autoregressive) panel unit root test is applied. Empirical findings indicate that convergence of the EF exists in the second regime, which represents 48.08 percent of the sample, and divergence in the first. Canada is the transition country between two regimes. These results signify common environmental policies-actions among the USMCA countries to mitigate-stop their environmental degradation. Additionally, detected convergence and divergence also might help the policymakers of the USMCA countries to understand which strategies-policies-actions converge or diverge them in the case of EF.

Financial Development and Economic Growth in Nigeria: New Evidence from a Threshold Autoregressive and Asymmetric Analysis

Financial development is concerned with policies, procedures and initiatives to increase the financial access, depth and productivity of financial institutions and financial markets. To that regards, this paper investigates the asymmetric relationship between financial development and economic growth by incorporating financial globalization uncertainty and inflation into a production function covering the Nigerian economy from 1980–2019. The threshold Autoregressive co-integration and nonlinear autoregressive distributed lag techniques are applied to examine the asymmetric cointegration between the variables, as well as the degree of the relationship among the variables. The results indicate long-run relationship between the parameters in the light of asymmetries. The asymmetric estimation results show that positive shock of financial development dampen economic growth. By comparison, symmetrically, the negative shock of the financial development improves the productivity growth of the Nigerian economy. Finally, while inflation shows a positive relationship, the financial globalization uncertainty indicates insignificant result. The implications of the said findings for productivity policies in Nigeria are also discussed.

An Investigation of Multiple Asymmetric Threshold Contagions Effects of U.S. Stock Market to Major Industrialized Countries under Turbulent Economic Conditions

Researchers and academic institutions frequently use the conventional Threshold AR model. However, the model uses zero or a single random value as a threshold to convey limited information. This study intends to explore multiple asymmetric threshold effects of U.S. stock market to five major industrialized countries during turbulent economic conditions. Daily stock index returns, ranging from 1998 to 2019, are collected. A three random thresholds TAR model is built; Four hypotheses are proposed; Grid search algorithm is programmed; The testing procedure includes: linear and non-linear unit roots, structural break, likelihood ratio, Wald, and residual diagnoses are tested. The findings are as follows: The four hypotheses are significant in most of five major countries except for U.K. and Germany of contagions effects. The newly proposed multiple TAR model is superior to the traditional TAR model. During the financial crisis period, the contagions effects are greater, the threshold effects are significant, and the combined threshold contagions effects are stronger.

The Role of Cash-Waqf Capital Ventures to Supply Shock Liquidity of Sharia Banks in East Java: Multivariate Threshold Autoregressive Simulation Model

Adequacy of liquidity is very important for Islamic banks to carry out their main functions as Islamic financial intermediaries and provide financing for the real sector. However, many Islamic banks are proven to have a shock in providing liquidity due to the weak capital structure of the bank. While cash-waqf funds can be a potential source of capital for sharia banks if managed as capital investments in sharia banks with a mudharabah or musyrakah contract. Therefore, using the autoregressive multivariate threshold simulation model approach, this study attempts to analyze the dynamic interaction between the monetary policy regime, cash-capital capital ventures, Islamic bank liquidity and inflation in East Java. The participation of waqf funds on Islamic bank capital is simulated at 1%, 5% and 10% with two regimens of threshold models, namely a tightening monetary policy regime and a monetary policy easing regime. The data in this study were obtained from Statistik Perbankan Syariah (SFS) published by the OJK and BI with the observation period of 2014.M1 - 2018.M6. The results show that the inclusion of waqf funds in Islamic bank capital is able to reduce the length of the period and the sharia bank liquidity shock both in high interest rate policy regime and low interest rate regime.

Asymmetric Movements between the US and Hong-Kong Stock Market Price Indices

In the financial field, we are often faced with data that are somewhat non-linear. Thus, this research investigates the asymmetric behavior of the series through the Threshold Autoregressive and Momentum Threshold asymmetry tests between price index using diary data between of the total price index of actions in stock markets of United States of America (SP500) and Hong Kong (HANG SENG), from January 2, 1995 to March 29, 2013. With the empirical analysis it was possible to see the asymmetry in the series analysed through the TAR and M-TAR models. These oscillations demonstrate that behaviors are distinct in contractions and expansions.

IDENTIFIKASI MODEL SELF-EXCITING THRESHOLD AUTOREGRESSIVE DENGAN SWITCHING TWO REGIME (KASUS PADA DATA EKSPOR AGRIKULTUR DI INDONESIA)


 A time series model that explain the structural changes associated with data in a certain time period is the Threshold Autoregressive (TAR) model. The basic of the TAR model there are some different usage regimes in autoregressive analysis. One model based on TAR is a self-exciting threshold autoregressive (SETAR) model with the same delay parameters for each regimen. The SETAR model has a linear nature in each regime but being nonlinear if the models of each regime are combined. In addition, this model can improve jump data that cannot be captured by linear time series models. This means that the SETAR model has high-level parameters through an appropriate switching regime that is applied to agricultural export data in Indonesia. The purpose of this reseach is to test the estimated SETAR parameter model and apply it to Indonesian agricultural export data. There are three methods that can be done for estimating of parameter of SETAR model, namely the conditional quadratic sequential method, ordinary least square (OLS) and nonlinear least square (NLS). In this research, the two stage parameter estimation method is used with OLS and the second stage parameter estimation is used to optimisze the parameter values ​​that are not significant in the model. In its application, the SETAR model (2,1,1) was obtained to model agricultural export data in Indonesia and the MAPE value was 25%.

Dissecting Tether’s Nonlinear Dynamics during Covid-19

The present study is on the five cryptocurrency daily mean return time series linearity dynamics during the Covid-19 period. These cryptocurrencies were chosen based on their influence on the market, primarily driven by its market capitalisation. Tether is included as the most important stable coin on the market, nominally pegged to the U.S. dollar (USD). The reason to investigate it is that there are some inconsistencies in its behaviour as opposed to the other four cryptocurrencies. This study found that the behaviour of Tether cryptocurrency daily average return time series pattern is highly nonlinear and chaotic in nature, whereas the other four cryptocurrencies (namely Bitcoin, Ethereum, XRP and Bitcoin Cash) daily average return time series were found to be linear in nature. To further study Tether’s nonlinear time series rich dynamics, this study deployed one category of the regime switching models popularly known as the threshold regressions. The study estimates fairly suggest that both the threshold autoregression (TAR) and smooth transition autoregressive (STAR) models with lag 1 are adequate to capture the rich nonlinear and chaotic dynamics of Tether’s daily average return time series.

Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting

Exchange rate movements can significantly impact not only foreign trade, capital flows, and asset portfolio management, but also real economic activity. Therefore, the forecast of exchange rates has always been of great interest among academics, economic agents, and institutions. However, exchange rate series are essentially dynamic and nonlinear in nature and thus, forecasting exchange rates is a difficult task. On the other hand, deep learning models in solving time series forecasting tasks have been proposed in the last half-decade. But the number of formal comparative study in terms of exchange rate forecasting with deep learning models is quite limited. For this purpose, this study applies ten different models (Random Walk, Autoregressive Moving Average, Threshold Autoregression, Autoregressive Fractionally Integrated Moving Average, Support Vector Regression, Multilayer Perceptron, Recurrent Neural Network, Long Short Term Memory, Gated Recurrent Unit and Autoregressive Moving Average-Long Short Term Memory Hybrid Models) and two forecasting modes (recursive and rolling window) to predict three major exchange rate returnsnamely, the Canadian dollar, Australian dollar and British pound against the US Dollar in monthly terms. To evaluate the forecasting performances of the models, we used Model Confidence Set procedure as an advanced test. According to our results, the proposed hybrid model produced the best out-of-sample forecast performance in all samples, without exception.

Frequent or systematic changes? discussion on “Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection.”

We discuss Fryzlewicz’s paper that proposes WBS2.SDLL approach to detect possibly frequent changes in mean of a series. Our focus is on the potential issues related to the model misspecification. We present some numerical examples such as the self-exciting threshold autoregression and the unit root process, that can be confused as a frequent change-points model.

Bayesian estimation of subset threshold autoregressions: short-term forecasting of traffic occupancy

Traffic management authorities in metropolitan areas use real-time systems that analyze high-frequency measurements from fixed sensors, to perform short-term forecasting and incident detection for various locations of a road network. Published research over the last 20 years focused primarily on modeling and forecasting of traffic volumes and speeds. Traffic occupancy approximates vehicular density through the percentage of time a sensor detects a vehicle within a pre-specified time interval. It exhibits weekly periodic patterns and heteroskedasticity and has been used as a metric for characterizing traffic regimes (e.g. free flow, congestion). This article presents a Bayesian three-step model building procedure for parsimonious estimation of Threshold-Autoregressive (TAR) models, designed for location- day- and horizon-specific forecasting of traffic occupancy. In the first step, multiple regime TAR models reformulated as high-dimensional linear regressions are estimated using Bayesian horseshoe priors. Next, significant regimes are identified through a forward selection algorithm based on Kullback-Leibler (KL) distances between the posterior predictive distribution of the full reference model and TAR models with fewer regimes. Given the regimes, the forward selection algorithm can be implemented again to select significant autoregressive terms. In addition to forecasting, the proposed specification and model-building scheme, may assist in determining location-specific congestion thresholds and associations between traffic dynamics observed in different regions of a network. Empirical results applied to data from a traffic forecasting competition, illustrate the efficacy of the proposed procedures in obtaining interpretable models and in producing satisfactory point and density forecasts at multiple horizons.

Testing for regime-switching behaviour in Finnish agricultural land prices

PurposeThis study analyses agricultural land price dynamics in order to better understand price development and to improve forecast accuracy. Understanding the evolution of agricultural land prices is important when considering sound investment decisions.Design/methodology/approachThis study applies threshold autoregression to model agricultural land prices. The data includes quarterly observations on Finnish agricultural land prices.FindingsThe study shows that Finnish agricultural land prices exhibit regime-switching behaviour when using past changes in prices as a threshold variable. The threshold autoregressive model not only fits the data better but also improves the accuracy of price forecasts compared to the linear autoregressive model.Originality/valueThe results show that a sharp fall in agricultural land prices temporarily changes the regular development of prices. This information significantly improves the accuracy of price predictions.

Market Integration and Price Transmission in the Vertical Supply Chain of Rice: An Evidence from Bangladesh

As a staple food, rice has an enormous market in Bangladesh in terms of market participants and the volume of the product. As the price of rice is always a sensitive factor for producers, poor consumers and policy makers, this paper investigates market integration and price transmission along the vertical supply chain of rice. Johansen’s test of co-integration confirmed that farm, wholesale and retail prices are co-integrated in the long-run. A causality test revealed that prices were found to be at wholesale levels for both the upstream and downstream markets. The asymmetry error correction model (ECM) has discovered short-run and long-run asymmetry in price transmission in the vertical supply chain where both producers and consumers were being affected due to positive and negative asymmetry. Threshold autoregressive (TAR) and momentum threshold autoregressive (M-TAR) models have confirmed threshold co-integration as well as threshold effect on asymmetry in price transmission. The results highlight the inevitability of policy implementations and increased public interventions to reduce asymmetry for engendering greater pricing efficiency in Bangladesh rice markets.

Bayesian Analysis of Multiplicative Seasonal Threshold Autoregressive Processes

Seasonal fluctuations are often found in many time series. In addition, non-linearity and the relationship with other time series are prominent behaviors of several, of such series. In this paper, we consider the modeling of multiplicative seasonal threshold autoregressive processes with exogenous input (TSARX), which explicitly and simultaneously incorporate multiplicative seasonality and threshold nonlinearity. Seasonality is modeled to be stochastic and regime dependent. The proposed model is a special case of a threshold autoregressive process with exogenous input (TARX). We develop a procedure based on Bayesian methods to identify the model, estimate parameters, validate the model and calculate forecasts. In the identification stage of the model, we present a statistical test of regime dependent multiplicative seasonality. The proposed methodology is illustrated with a simulated example and applied to economic empirical data.

A Study on the Factors Affecting Trade Costs between South Korea and its Trading Partners

Purpose - Trade costs may play an important role in limiting trade across countries. There are various factors, including exchange rate volatility, distance, and trade barriers that affect trade costs across countries. This research imputes trade costs from price differentials between South Korea and its trading partners and explore what factors do affect trade costs. Design/Methodology/Approach - This research employs the Threshold Autoregressive (TAR) Model and Ordinary Least Squares (OLS) Regression Model with monthly data during the sample period from January 1998 to November 2019. Findings - The results show that exchange rate volatility increases trade costs, while FTA reduces trade costs between South Korea and its trading partners. No statistically significant evidence was found to support the effects of distance, trade barriers (tariffs and non-tariffs), and English as a common language on trade costs. Research Implications - The findings suggest that Korean policymakers may need to consider how to keep stable exchange rates with trading partners to reduce trade costs, and that an FTA works well to reduce trade costs between South Korea and its trading partners.