Nonlinear Cointegration Analysis Research Articles

Testing for asymmetric cointegration of Italian agricultural commodities prices: Evidence from the futures-spot market relationship

The volatility of food prices still raises concerns among agricultural market players, increasing interest in the futures markets, thus calling for a better understanding of the connection between the futures and the Italian spot prices. This study uses symmetric and asymmetric vector error correction models to investigate the relationship between futures and spot prices for the Italian agricultural markets of soybean, corn, and milling wheat. The results confirm the leading role of the futures contract prices for all the considered commodities. Moreover, the non-linear cointegration analysis results suggest price transmission's asymmetries for all the agricultural commodity prices. This research provides critical insight into the shape of the futures-spot price transmission.

The effects of environmental tax on Ecological Footprint and Carbon dioxide emissions: a nonlinear cointegration analysis on Turkey.

This article presents a nonlinear analysis in Turkey on the effect of an environmental tax (ET) on the ecological footprint (EFP) and carbon dioxide (CO2) emissions. In the literature, most of the studies examining the effects of environmental taxes (ETs) on environmental degradation (ED) have used linear methods. The number of studies examining this relationship with nonlinear methods is few. However, there is no study examining the long-run effects of ETs on the EFP, which is one of the most important indicators of ED, using nonlinear analysis. This study contributes to the literature by investigating the long-run effects of ETs on EFP and CO2 emissions in Turkey by nonlinear analysis. Therefore, the model consisting of annual data for the period 1994–2019 was estimated by Dufrénot et al. (2006) nonlinear cointegration test. According to the estimation results obtained, ETs do not have any long-run effects on EFP and CO2 emissions. Accordingly, it can be concluded that ETs in Turkey do not affect preventing ED.

Ethics de' Competitiveness in Finance: An Emancipative Structured Assessment and Evaluation of Indian Finance Industry

The details of a central bank’s monetary policy are based on assumptions about the money demand. This requires researches that aim to investigate money demand dynamics. Knowing these dynamics will support the identification of risks that may pose a threat to price stability in the long run. This study aims to analyze the changes observed in the demand for money during the last 35 years (1986-2020) in Turkey. When the analyzing period is considered as a whole in the study, it is determined that the demand for money is not stable. However, the nonlinear cointegration analysis used within the framework of soft transition models indicates that the money demand model can be divided into two different regimes with stability. In this case, it is possible to talk about the existence of a transition period in which stability is lost in the demand for money. The analyzing technique used allows the coefficients obtained for money demand to change over time according to the regime in which the economy operates. Nonlinear estimation results indicate that there is a long-term relationship between the demand for money and its macroeconomic determinants such as price level, income, interest rate, and money holding preferences of economic agents.

Is There a Change in the Money Demand Stability in Turkey? A Nonlinear Approach

The details of a central bank’s monetary policy are based on assumptions about the money demand. This requires researches that aim to investigate money demand dynamics. Knowing these dynamics will support the identification of risks that may pose a threat to price stability in the long run. This study aims to analyze the changes observed in the demand for money during the last 35 years (1986-2020) in Turkey. When the analyzing period is considered as a whole in the study, it is determined that the demand for money is not stable. However, the nonlinear cointegration analysis used within the framework of soft transition models indicates that the money demand model can be divided into two different regimes with stability. In this case, it is possible to talk about the existence of a transition period in which stability is lost in the demand for money. The analyzing technique used allows the coefficients obtained for money demand to change over time according to the regime in which the economy operates. Nonlinear estimation results indicate that there is a long-term relationship between the demand for money and its macroeconomic determinants such as price level, income, interest rate, and money holding preferences of economic agents.

A nonlinear full condition process monitoring method for hot rolling process with dynamic characteristic

As a typical complex industrial process, hot rolling process (HRP) is different from chemical process. Strip steels are produced coil by coil, that means there is a long idle period between coils. The rolling speed is very high and the producing time of each coil is usually a few minutes. Previous researches mostly focus on fault detection in loaded condition and very few attempts have been made to exploit the monitoring of idle condition. In order to monitor the whole process, not only the loaded condition, but also the idle one, a novel nonlinear full condition process monitoring model is developed in this work. First, a dissimilarity index (DI) is defined for condition identification and a support data vector description (SVDD) model is established to monitor the idle condition. Second, t-distributed stochastic neighbor embedding (t-SNE) is used to extract nonlinear principal components (NPC) for slow feature analysis (SFA) and cointegration analysis (CA). Nonlinear cointegration analysis (NCA) can reveal the long-run dynamic relations of nonstationary parts, while nonlinear slow feature analysis (NSFA) can extract the latent temporal dynamic and static variations of stationary ones. Finally, the monitoring performance of the proposed model is verified through a real HRP.

New Evidence on Exchange-Rate Volatility and Export Flows in Thailand: Nonlinearity and Asymmetric ARDL Investigation

ABSTRACT This is the first study of Thailand’s export demand relations utilizing the new nonlinear autoregressive distributed lag (NARDL) technique by Shin, Yu, and Greenwood-Nimmo. The study reports long-run elasticities and exposes the short-run adjustment of real exports to changes in foreign economic activity, relative export price, and exchange-rate risk using long-run and short-run asymmetry as well as nonlinear asymmetric cointegration. Results from nonlinear and asymmetry cointegration analysis and short-run asymmetric error-correction modeling indicate cointegration and negative effects of exchange-rate risks on export volume in both the long run and short run.

Monetary policy uncertainty, investor sentiment, and US stock market performance: New evidence from nonlinear cointegration analysis

AbstractThe purpose of this paper is to examine non‐linear and cointegrating relationships between monetary policy uncertainty, investor sentiment, and stock market for the US economy, via controlling for potential macroeconomic risk factors. We mainly utilize non‐linear autoregressive distributed lag (NARDL) approach and findings support an existing cointegration between the aforementioned variables. Our results also suggest that there is a bidirectional and negative relationship between US stock market performance and monetary policy uncertainty in the short‐run. Furthermore, the effect of monetary policy uncertainty on investor sentiment is significantly negative and not strongly asymmetric in the long‐run. On the other hand, in the short‐run, increasing investor sensitivity to macroeconomic shocks strongly increases monetary policy uncertainty, while reducing sensitivity does not have a significant impact on the monetary policy uncertainty. Finally, we find a positive and bi‐directional relationship between stock prices and investor sentiment both in the short‐ and long‐run. In the long‐run, decreasing investor sensitivity to macroeconomic fluctuations (becoming more optimistic) has a greater positive influence on stock prices than a negative influence on stock prices, since investors are becoming more pessimistic. Implications from our analysis are important to policy makers and investors for determining effective economic policy decisions and proper investment strategies.

The Dynamic Links between Oil Prices and Economic Growth: Recent Evidence from Nonlinear Cointegration Analysis for the ASEAN-5 Countries

ABSTRACT This article investigates the asymmetric relationship between real oil prices and real GDP for selected ASEAN countries (ASEAN-5), using the nonlinear autoregressive distributed lag (NARDL) model. The sample data consist of annual frequencies from 1970 to 2015. Furthermore, the article pays attention to structural breaks in testing the asymmetric nexus. The empirical findings demonstrate long-run asymmetry for the ASEAN-5 countries, however, short-run asymmetry is found for Malaysia and Singapore only. Moreover, the empirical findings show that the increase in oil prices leads to positive effect on GDP for all countries (highly significant for all countries except for the Philippines), yet, such outcome does not go in line with the vast majority of the studies conducted. As for the direction of causality between oil prices and GDP, the article finds mixed results by using the Toda and Yamamoto noncausality test.

Modeling the employment–oil price nexus: A non-linear cointegration analysis for the U.S. market

ABSTRACTTheoretically, fluctuations in oil prices are expected to affect production costs and may force businesses to delay their investment decisions, triggering pressures on employment. Following these theoretical notions, this paper investigates the asymmetric impact of oil prices on employment (measured as total employment, male employment, and female employment), in a nonlinear cointegration structure for the U.S. market. In doing so, this paper adopts the nonlinear autoregressive distributed lags (NARDL) model to shed light on such asymmetric association, as the NARDL model recently emerged as a new direction in examining nonlinear cointegration and asymmetry. The empirical findings document a long-run asymmetric effect in case of total employment and male employment only. Furthermore, the short-run asymmetric effect was detected for all three employment categories. As a final point, the Granger Causality test documents a unidirectional causality running from oil price decrease to both total employment and male employment.

A cointegrating polynomial regression analysis of the material kuznets curve hypothesis

This paper investigates the Material Kuznets Curve (MKC) hypothesis, postulating an inverted U-shaped relationship between a country's level of economic development and its intensity of material use, by means of nonlinear cointegration analysis. We use consumption data for aluminum, lead and zinc for eight OECD countries spanning from 1900 to 2006 and employ the tests and estimation techniques for nonlinear cointegration developed by Saikkonen and Choi (2004), Wagner (2013) as well as Wagner and Hong (2016). We find evidence for the prevalence of a cointegrating quadratic MKC for about half of the country-metal pairs.

Financial development and tax revenues in Turkey: A non-linear cointegration analysis

Financial development and tax revenues in Turkey: A non-linear cointegration analysis

Towards homoscedastic nonlinear cointegration for structural health monitoring

The paper presents the homoscedastic nonlinear cointegration. The method leads to stable variances in nonlinear cointegration residuals. The adapted Breusch–Pagan test procedure is developed to test for the presence of heteroscedasticity (or homoscedasticity) in the cointegration residuals obtained from the nonlinear cointegration analysis. Three different time series – i.e. one with a nonlinear quadratic deterministic trend, simulated vibration data and experimental wind turbine data – are used to illustrate the application of the proposed method. The proposed approach can be used for effective removal of nonlinear trends from various types of data and for reliable structural damage detection based on data that are corrupted by environmental and/or operational nonlinear trends.

Testing the expectations hypothesis for the Eurozone: A nonlinear cointegration analysis

Most studies examining the expectations hypothesis (EH) of the term structure of interest rates assume that the adjustment process between short term and long term interest rates is linear. However, ignoring the possible nonlinearity between interest rates may result in misleading empirical results. In this paper, we investigate the term structure of interest rates for selected Eurozone countries using the nonlinear cointegration tests introduced by Kapetanios et al. (2006). Accounting for the effects of global financial and debt crisis, we find supportive evidence for the EH for Greece during the period covering the sovereign debt crisis.

Renewable energy consumption and unemployment: evidence from a sample of 80 countries and nonlinear estimates

This article contributes to the discussion on the dynamic nexus of renewable energy consumption and unemployment by incorporating nonlinear cointegration and causality analysis. Using a sample of 80 countries spanning the period 1990–2013 and the advanced generation of unit root, cointegration and nonlinear Granger causality methodological approaches in panel data, we obtain mixed results about the impact of renewable energy consumption on unemployment. Although the total findings document a positive impact of renewable energy consumption on unemployment, disaggregated data across specific regions, such as Asia and Latin America, highlight the favourable effect on unemployment, implying that the effect of renewable energy consumption on jobs creation depends on the cost of adopting renewable energy technologies and energy efficiencies that seem to vary across the regions under investigation.

Nonlinear Cointegration Approach for Condition Monitoring of Wind Turbines

Monitoring of trends and removal of undesired trends from operational/process parameters in wind turbines is important for their condition monitoring. This paper presents the homoscedastic nonlinear cointegration for the solution to this problem. The cointegration approach used leads to stable variances in cointegration residuals. The adapted Breusch-Pagan test procedure is developed to test for the presence of heteroscedasticity in cointegration residuals obtained from the nonlinear cointegration analysis. Examples using three different time series data sets—that is, one with a nonlinear quadratic deterministic trend, another with a nonlinear exponential deterministic trend, and experimental data from a wind turbine drivetrain—are used to illustrate the method and demonstrate possible practical applications. The results show that the proposed approach can be used for effective removal of nonlinear trends form various types of data, allowing for possible condition monitoring applications.

A New Framework for Nonlinear Cointegration Analysis and Its Application to Finance

With the purpose of analyzing non-stationary time series, this paper innovates the nonlinear cointegration dis- criminate analysis by introducing support vector machine and innovated feature-weighting model to overcome existing limitations of two methods, that is, the statistical approach and the neural network used by the nonlinear cointegration theory. Then, the application of the innovated method for the investigation of the connection between financial markets is explored. The empirical analysis demonstrates that the support vector machine is effective in analyzing the money de- mand function and its stability and has advantages in dealing with nonlinear cointegration relations and estimating nonlin- ear functions over the two methods above.

Non-linear cointegration between crude oil and stock markets: evidence from Asia-Pacific countries

This paper aims to investigate the possible linkage and non-linear interaction between crude oil and stock markets using cointegration and causality tests. Daily data on world crude oil prices and the stock indices of Japan, Taiwan, South Korea, Australia, Indonesia, India, Singapore and Malaysia are selected for this study. Unlike the previous literature, by conducting a non-linear cointegration analysis, we obtain evidence of non-linear long-term equilibrium between crude oil and stock markets for the eight Asia-Pacific markets. The Granger causality tests demonstrate the existence of bidirectional short-run Granger causality between crude oil and these Asia-Pacific stock markets. The long-run Granger causality between them is unidirectional. However, the speed of adjustment toward long-run equilibrium is relatively slow.

Oil prices and stock markets in GCC countries: new evidence from nonlinear cointegration analysis

PurposeThe purpose of this research is to examine the linkages between oil prices and stock market in Gulf Cooperation Council (GCC) countries. Prior work argues that oil prices and the GCC stock markets are not related. This conclusion could be due to the fact that only linear linkages have been examined.Design/methodology/approachThis study employs newly developed techniques of rank tests of nonlinear cointegration analysis proposed by Breitung and Gourieroux and Breitung. The Breitung's method is selected in this study due its potential superiority at detecting cointegration when the error‐correction mechanism is nonlinear.FindingsThe empirical analysis of the paper supports that oil price impact the stock price indices in GCC countries in a nonlinear fashion. Thus, the statistical analysis in this paper obviously supports a nonlinear modeling of the relationship between oil and the economy.Research limitations/implicationsThe paper contains the normal limitations associated with the econometric method including statistical bias.Practical implicationsThe implication of this paper findings is that policy makers at GCC countries should keep an eye on the effects of changes in oil price levels on their own economies and stock markets. For individual and institutional investors, the nonlinear relationship between oil and stock markets imply predictability in the GCC stock markets.Originality/valueThe paper presents new findings on the relationships between oil prices and the stock market in GCC countries. These findings should be of interest to researchers, regulators, and market participants.

International Stock Market Integration: Evidence from Nonlinear Cointegration Analysis

This paper employs newly developed techniques of nonlinear cointegration analysis to study international stock market integration. The stock price indexes of Australia, Japan, New Zealand, the United Kingdom and the United States are used in both linear and nonlinear cointegration tests on bivariate and various multivariate models. Much more evidence of market integration emerges from nonlinear cointegration analysis than linear analysis. It appears, therefore, that many of the conclusions reached in prior work that used traditional methodologies need to be reconsidered.