Threshold Autoregressive Model Research Articles

The Comparation of Foreign Financial Investment and Human Investment Effect on Economic in Indonesia Base on Macro Economic Point of View

The purpose of this study is to compare the impact of the direction of the relationship between education and health investment with economic growth in Indonesia, with the impact and direction of the relationship of foreign direct investment and portfolio investment with economic growth in Indonesia. This study uses a quantitative method using the Threshold Autoregressive model. This study uses secondary data from the world bank in the annual time period from 2000 to 2019. We found that Investment in education and health has an impact on increasing productivity which drives economic growth because labor productivity directly drives the real sector. However, FDI and FPI changed the financial position in terms of capital. Direct investment increases real capital which has an impact on the creation of new sources of economic production but has consequences in the form of income transfers abroad, resulting in cash outflows. The existence of these foreign transfers continuously when economic conditions are stable and the real sector grows and generates profits that can be greater than the cash inflows obtained when foreign direct investments are made.

A new assessment of economic integration in East Asia: Application of an industry-specific G-PPP model

It is well known that intra-industry trade and cross-border production networks have promoted economic growth and regional integration in East Asia. However, regional supply and production chains may have been formed differently across industries, reflecting different degrees and a different scope of regional economic linkages at an industry level. This paper makes a threefold contribution. First, to assess industry-level differences, this study adopts the generalized purchasing power parity (G-PPP) model using industry-specific producer prices. Second, the momentum threshold autoregressive (M-TAR) model is employed to allow for possible nonlinearity arising from the dynamic nature of regional economic growth and development. Third, the Granger causality test is also conducted to assess whether regional economies have autonomously integrated. The empirical results reveal that economic integration has progressed more autonomously in the electrical industry, as well as in the transportation equipment industry, as China and the ASEAN countries have become the final destination markets for finished products in these two industries.

Nonlinear price transmission in the rice market in Senegal: a model-based recursive partitioning approach

ABSTRACT This paper analyzes the nonlinear effects in price transmission from international markets to the local rice market of Dakar (Senegal) focusing on asymmetries through threshold effects. We use recent machine learning methods (model-based recursive partitioning trees) to detect asymmetries in the price transmission mechanism. Using a model based recursive partitioning algorithm, we identify a threshold and confirm the asymmetry in the price transmission. Local retail prices are more sensitive to world price increases than to declines. Only 11.80% of positive deviations (international prices go down) are eliminated at the end of the subsequent month, while 39.50% of negative deviations (world prices go up) are eliminated after one month. These results highlight the role of transaction costs and the market power of commercial intermediaries in price transmission in the sense that margins are corrected more rapidly when they are squeezed relative to their long run level than when they are stretched. Our results are confirmed by the traditional Threshold Autoregressive (TAR) model.

How is price explosivity triggered in the cryptocurrency markets?

We shed light on how the price explosivity characterising Bitcoin and other major cryptocurrencies is triggered, by employing the Quantile Self-Exciting Threshold Autoregressive (QSETAR) model. Our results for Bitcoin, Ripple, and Stellar reveal that the explosive behaviour originates from the extreme upper tails of the return distributions following a price increase in the preceding day. We do not find evidence of explositivity in the price of Litecoin.

Subgeometric ergodicity and β-mixing

AbstractIt is well known that stationary geometrically ergodic Markov chains are $\beta$ -mixing (absolutely regular) with geometrically decaying mixing coefficients. Furthermore, for initial distributions other than the stationary one, geometric ergodicity implies $\beta$ -mixing under suitable moment assumptions. In this note we show that similar results hold also for subgeometrically ergodic Markov chains. In particular, for both stationary and other initial distributions, subgeometric ergodicity implies $\beta$ -mixing with subgeometrically decaying mixing coefficients. Although this result is simple, it should prove very useful in obtaining rates of mixing in situations where geometric ergodicity cannot be established. To illustrate our results we derive new subgeometric ergodicity and $\beta$ -mixing results for the self-exciting threshold autoregressive model.

The Effect of Inflation Targeting Policy on Price Stability in South Korea, Utilizing Threshold Autoregressive Model

Purpose - The purpose of Inflation Targeting (hereafter IT) Policy is price stability. To see whether IT policy has positive effects on price stability in South Korea, whether or not inflation rates are less persistent after an IT policy is explored. Design/Methodology/Approach - To estimate inflation persistence for the inflation rates within and deviated from the target range of 2% ~ 4% during the period of the post-IT policy, respectively, a Threshold Autoregressive (TAR) Model with aggregate CPI and disaggregate CPIs for 20 items is utilized. In addition, I investigate whether inflation persistence is different depending on whether the inflation rates undershoot or overshoot toward the target range. Findings - It was found that inflation persistence was reduced after an IT policy. However, there was no difference in inflation persistence depending on whether inflation rates are within and deviated from the target range of 2% ~ 4% and also whether they overshoot or undershoot toward the target range. I also found that the same results from the robustness test, which set the target range of 2.5% ~3.5%. Research Implications - The findings suggest that an IT policy has a positive effect on price stability in South Korea regardless of overshooting or undershooting toward the target ranges, which means that even if there is an economic shock in South Korea, its influence on the inflation rates would be temporary.

Mixed random forest, cointegration, and forecasting gasoline prices

One of the most successful forecasting machine learning (ML) procedures is random forest (RF). In this paper, we propose a new mixed RF approach for modeling departures from linearity that helps identify (i) explanatory variables with nonlinear impacts, (ii) threshold values, and (iii) the closest parametric approximation. The methodology is applied to weekly forecasts of gasoline prices, cointegrated with international oil prices and exchange rates. Recent specifications for nonlinear error correction (NEC) models include threshold autoregressive models (TAR) and double-threshold smooth transition autoregressive (STAR) models. We propose a new mixed RF model specification strategy and apply it to the determinants of weekly prices of the Spanish gasoline market from 2010 to 2019. In particular, the mixed RF is able to identify nonlinearities in both the error correction term and the rate of change of oil prices. It provides the best weekly gasoline price forecasting performance and supports the logistic error correction model (ECM) approximation.

Forecasting interval-valued crude oil prices using asymmetric interval models

Practitioners and policy makers rely on accurate crude oil forecasting to avoid price risks and grasp investment opportunities, but the core of existing predictive models for such prices is based on point-valued inputs and outputs, which may suffer from informational loss of volatility. This paper addresses this issue by proposing a modified threshold autoregressive interval-valued models with interval-valued factors (MTARIX), as extended by Sun et al. [Threshold autoregressive models for interval-valued time series. J. Econom., 2018, 206, 414–446], to analyze and forecast interval-valued crude oil prices. In contrast to point-valued data methods, MTARIX models simultaneously capture nonlinear features in price trend and volatility, and this informational gain can produce more accurate forecasts. Several interval-valued factors and point-valued threshold variables are analyzed, including supply and demand, speculation, stock market, monetary market, technical factor, and search query data. Empirical results suggest that MTARIX models with appropriate threshold variables outperform other competing forecast models (ACIX, CR-SETARX, ARX, and VARX). The findings indicate that oil price range information is more valuable than oil price level information in forecasting crude oil prices.

Income thresholds in the remittances-growth association? a case study of Fiji

ABSTRACT Research indicates that remittances have mixed effects on growth. In this study, we investigate the role of income thresholds in this association for Fiji. Income thresholds are measured by thresholds in per-worker real GDP and are identified using the self-exciting threshold autoregression model over the period 1980 to 2017. A threshold autoregressive distributed lag model is subsequently developed. A single threshold in per-worker real GDP is estimated at US$5891. Remittances promote growth if income exceeds the threshold but is negative otherwise. Hence, remittances support growth-enhancing activities at high-income levels but may finance imports at the expense of domestic production at lower income levels.

Forecasting with Multivariate Threshold Autoregressive Models


 
 
 An important stage in the analysis of time series is the forecasting. How- ever, the forecasting in non-linear time series models is not straightforward as in linear time series models because an exact analytical of the conditional expectation is not easy to obtain. Therefore, a strategy of forecasting with multivariate threshold autoregressive(MTAR) models is proposed via predictive distributions through Bayesian approach. This strategy gives us the forecast for the response and exogenous vectors. The coverage percentages of the forecast intervals and the variability of the predictive distributions are analysed in this work. An application to Hydrology is presented.
 
 
 
 
 
 
 
 
 

The integration and efficiency of BRICS and Pakistan stock markets: An analysis using asymmetric cointegration and MF-DFA approaches

This study examines the asymmetric cointegration and efficiency between Pakistan and BRICS equity markets using monthly data from October 1997 to September 2018. The results reveal that the Brazilian stock market is the most efficient during the global financial crisis. Threshold and momentum threshold autoregressive models (TAR and M.TAR) confirm the presence of a long-run relationship between BRICS and Pakistan stock markets, where the speed of negative shocks is higher and significant for Pakistan Russian, Pakistan South Africa stock market pair. This infers quick adjustment of stock prices to negative shocks (bad news) as compared to positive shocks (good news). The speed of adjustment of positive shocks for Pakistan China is higher. The results of asymmetric error correction model (AECM) show results of unidirectional causality between Pakistan China and Pakistan Brazil stock market pairs, while bidirectional causality runs from Pakistan Russia, Pakistan India, and Pakistan South Africa stock market pairs. Thus the Pakistan stock market has short-run and long-run relationships with most other stock markets. Multi-fractal detrended fluctuation analysis supports long run efficiency of Brazilian markets during the global financial crisis. It suggests that investors pay keen attention to the Pakistan stock market when investing in BRICS stock market.

Threshold Effects of Infectious Disease Outbreaks on Livestock Prices: Cases of African Swine Fever and Avian Influenza in South Korea

In this paper we demonstrate the threshold effects of infectious diseases on livestock prices. Daily retail prices of pork and chicken were used as structured data; news and SNS mentions of African Swine Fever (ASF) and Avian Influenza (AI) were used as unstructured data. Models were tested for the threshold effects of disease-related news and SNS frequencies, specifically those related to ASF and AI, on the retail prices of pork and chicken, respectively. The effects were found to exist, and the values of ASF-related news on pork prices were estimated to be −9 and 8, indicating that the threshold autoregressive (TAR) model can be divided into three regimes. The coefficients of the ASF-related SNS frequencies on pork prices were 1.1666, 0.2663 and −0.1035 for regimes 1, 2 and 3, respectively, suggesting that pork prices increased by 1.1666 Korean won in regime 1 when ASF-related SNS frequencies increased. To promote pork consumption by SNS posts, the required SNS frequencies were estimated to have impacts as great as one standard deviation in the pork price. These values were 247.057, 1309.158 and 2817.266 for regimes 1, 2 and 3, respectively. The impact response periods for pork prices were estimated to last 48, 6, and 8 days for regimes 1, 2 and 3, respectively. When the prediction accuracies of the TAR and autoregressive (AR) models with regard to pork prices were compared for the root mean square error, the prediction accuracy of the TAR model was found to be slightly better than that of the AR. When the threshold effect of AI-related news on chicken prices was tested, a linear relationship appeared without a threshold effect. These findings suggest that when infectious diseases such as ASF occur for the first time, the impact on livestock prices is significant, as indicated by the threshold effect and the long impact response period. Our findings also suggest that the impact on livestock prices is not remarkable when infectious diseases occur multiple times, as in the case of AI. To date, this study is the first to suggest the use of SNS to promote meat consumption.

Transmission of world price shocks - Evidence from GCC countries

This study examines whether the positive and negative shocks in world commodity prices transmit symmetrically to the domestic prices of GCC economies while using threshold autoregressive (TAR) model. The empirical results indicate that the positive shocks in world prices do transmit to the domestic prices of these economies whereas, the negative shocks are not transmitted. Moreover, for certain commodity-groups, which respond to both positive and negative shocks, the speed of transmission is relatively faster with respect to the positive shocks as compared to negative shocks. These findings indicate that the agents in these economies prefer to choose inaction in response to negative world price shocks whereas, the positive shocks of even smaller size trigger quicker upward adjustments in the domestic prices. Consistent with the predictions of menu cost models, these results imply that the shocks in world prices which raise the desired level of domestic prices, cause quick and large increase in domestic prices whereas the shocks that lower the desired prices, cause a small or no decrease in these prices. These results underline the fact that despite adopting a number of trade reforms, the markets of these economies are still characterized with lower degrees of competitiveness, higher imperfections and other related inefficiencies.

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.

Impacts of COVID-19 and Price Transmission in U.S. Meat Markets.

Coronavirus 2019 (COVID‐19) has caused ongoing disruptions to U.S. meat markets via demand and supply‐side shocks. Abnormally high prices have been reported at retail outlets and meat packers have been accused of unfair business practices because of widening price spreads. Processing facilities have experienced COVID‐19 outbreaks resulting in shutdowns. Using weekly data on wholesale and retail prices of beef, pork, and poultry, we characterize the time series behavior and dynamic linkages of U.S. meat prices before the COVID‐19 pandemic. We model vertical price transmission using both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. With the estimated models, we then compare price movements under COVID‐19 to model predictions. All three meat markets are well‐integrated and we observe unexpected, large price movements in April and May of 2020. Early COVID‐19 related shocks appear to be transitory with prices returning to expected levels at a pace consistent with the speed of transmission prior to the pandemic. This well‐functioning market process suggests a degree of resilience in U.S. meat supply chains.

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.

Uncertain threshold autoregressive model with imprecise observations

Uncertain time series analysis is a methodology that deals with expert’s experimental time series data. Previous studies mainly focus on linear models such as an uncertain autoregressive (UAR) model. Nevertheless, the laws of motion in the real world are usually non linear. In order to model the observations that periodically vary over time, this article introduces an uncertain threshold autoregressive (UTAR) model. Then unknown parameters in the UTAR model can be estimated with the least squares estimation and residual analysis is presented. Furthermore, we discuss the forecast value and confidence interval for variables in the next periods. Ultimately, a numerical example is given.

Empirical likelihood inference for threshold autoregressive conditional heteroscedasticity model

This paper considers the parameter estimation problem of a first-order threshold autoregressive conditional heteroscedasticity model by using the empirical likelihood method. We obtain the empirical likelihood ratio statistic based on the estimating equation of the least squares estimation and construct the confidence region for the model parameters. Simulation studies indicate that the empirical likelihood method outperforms the normal approximation-based method in terms of coverage probability.

Revisiting the Canadian Lynx Time Series Analysis Through TARMA Models

The class of threshold autoregressive models has been proven to be a powerful and appropriate tool to describe many dynamical phenomena in different fields. In this work, we deploy the threshold autoregressive moving-average framework to revisit the analysis of the benchmark Canadian lynx time series. This data set has attracted great attention among non-linear time series analysts due to its asymmetric cycle that makes the investigation very challenging. We compare some of the best threshold autoregressive models (TAR) proposed in literature with a selection of thresholdautoregressive moving-average models (TARMA). The models are compared under different prospectives: (i) goodness-of-fit through information criteria, (ii) their ability to reproduce characteristic cycles, (iv) their capability to capture multimodality and (iii) forecasting performance. We found TARMAmodels that perform better than TAR models with respect to all these aspects.