Conditional Granger Causality Tests Research Articles

Physics-guided graph learning soft sensor for chemical processes

The surge in data-driven soft sensors for industrial processes is evident. However, most of them suffer from the limitation of being black-box models and this will hamper their widespread use. In response to this challenge, this study proposes a physics-guided graph-learning soft sensor that integrates a physical understanding of industrial processes by incorporating graph-based concepts with process physics. The soft sensor first constructs physical information based on causal relationships between variables using the conditional Granger causality test. Subsequently, it autonomously learns the unique sample information of each observation while employing a regularization loss to ensure the sparsity of the learned information. The model employs a two-stream structure for spatiotemporal encoding of both the physical and sample information. The modeling and prediction results on a penicillin fermentation process indicate that, using the proposed method, the knowledge gained from the data aligns with existing prior knowledge. This approach shows promise in filling the gap between data-driven and physics-based modeling in chemical processes.

Agriculture-induced environmental Kuznets curve: the case of China.

This study investigates the long-run equilibrium relationship among carbon dioxide (CO2) emissions, real income, energy consumption, and agriculture, thus testing the existence of the agriculture-induced environmental Kuznets curve (EKC) hypothesis in the case of China for the period of 1971-2014. The level relationship among the variables in the conducted model is confirmed by the bounds test approach under the autoregressive distributed lag (ARDL) mechanism. Error correction model under the ARDL mechanism suggests that short-run values of CO2 emissions converge to its long-run equilibrium level by 73.8% speed of adjustment every year by the contribution of energy consumption, real income, and agriculture. ARDL estimation results suggest that real income and energy consumption have a positive, elastic impact; agricultural development has positive, inelastic impact on CO2 emissions where squared real income has a negative and inelastic impact on air pollution. Conditional Granger causality test results reveal that there are unidirectional causalities running from real income, squared real income, energy consumption, and agricultural development in long run as well as in the short run.

Measuring network systemic risk contributions: A leave-one-out approach

The aim of this paper is to propose a new network measure of systemic risk contributions that combines the pair-wise Granger causality approach with the leave-one-out concept. This measure is based on a conditional Granger causality test and consists of measuring how far the proportion of statistically significant connections in the system breaks down when a given financial institution is excluded. We analyse the performance of our measure of systemic risk by considering a sample of the largest banks worldwide over the 2003–2018 period. We obtain three important results. First, we show that our measure is able to identify a large number of banks classified as global systemically important banks (G-SIBs) by the Financial Stability Board (FSB). Second, we find that our measure is a robust and statistically significant early-warning indicator of downside returns during the last financial crisis. Finally, we investigate the potential determinants of our measure of systemic risk and find similar results to the existing literature. In particular, our empirical results suggest that the size and the business model of banks are significant drivers of systemic risk.

Measuring Network Systemic Risk Contributions: A Leave-One-Out Approach

Granger-causality measures of interconnectedness between financial institutions are useful indicators of systemic risk (Billio et al., 2012) [Journal of Financial Economics], as they help in evaluating how far the distress of one institution is disseminated across the whole of the financial system through networks. This article provides a critical assessment of Granger-causality networks, showing that they can lead to inconsistent measures of systemic risk contributions because of the presence of spurious causalities arising from indirect contagion effects. The traditional solutions for controlling for these effects using inference on conditional Granger-causality lead to the curse of dimensionality though. To solve this, we provide a measure of financial network systemic risk contributions that is based on the leave-one-out (LOO) concept. For a given financial institution, the new measure evaluates how far the total number of significant Granger-causalities breaks down when this institution is excluded from the system. We control for spurious causalities between the remaining institutions due to the indirect contagion effect of the excluded financial institution using a conditional Granger-causality test, which is free of the curse of dimensionality. Empirical applications are conducted using daily market returns for a sample of the world’s largest banks. The results show that our measure gives a meaningful ranking of the systemic importance of financial institutions that is consistent with the ranking of global systemically important banks (G-SIBs) provided by the Financial Stability Board (FSB). Moreover, our measure is shown to be a robust and significant early-warning indicator of large losses from a systemic event, and is strongly driven by balance-sheet variables related to size, business model and profitability.

Testing the EKC hypothesis by considering trade openness, urbanization, and financial development: the case of Turkey.

This study investigates the environmental Kuznets curve (EKC) hypothesis for the case of Turkey from 1960 to 2013 by considering energy consumption, trade, urbanization, and financial development variables. Although previous literature examines various aspects of the EKC hypothesis for the case of Turkey, our model augments the basic model with several covariates to develop a better understanding of the relationship among the variables and to refrain from omitted variable bias. The results of the bounds test and the error correction model under autoregressive distributed lag mechanism suggest long-run relationships among the variables as well as proof of the EKC and the scale effect in Turkey. A conditional Granger causality test reveals that there are causal relationships among the variables. Our findings can have policy implications including the imposition of a "polluter pays" mechanism, such as the implementation of a carbon tax for pollution trading, to raise the urban population's awareness about the importance of adopting renewable energy and to support clean, environmentally friendly technology.

The mutual causality analysis between the stock and futures markets

In this paper we employ the conditional Granger causality model to estimate the information flow, and find that the improved model outperforms the Granger causality model in revealing the asymmetric correlation between stocks and futures in the Chinese market.First, we find that information flows estimated by Granger causality tests from futures to stocks are greater than those from stocks to futures. Additionally, average correlation coefficients capture some important characteristics between stock prices and information flows over time.Further, we find that direct information flows estimated by conditional Granger causality tests from stocks to futures are greater than those from futures to stocks. Besides, the substantial increases of information flows and direct information flows exhibit a certain degree of synchronism with the occurrences of important events.Finally, the comparative analysis with the asymmetric ratio and the bootstrap technique demonstrates the slight asymmetry of information flows and the significant asymmetry of direct information flows. It reveals that the information flows from futures to stocks are slightly greater than those in the reverse direction, while the direct information flows from stocks to futures are significantly greater than those in the reverse direction.

Interactions between energy consumption, international trade, and real income in Canada: An empirical investigation from a new version of the Solow growth model

ABSTRACTThis study investigates the empirical relationship between energy consumption, international trade, and real income in Canada which has an important role in global energy and trade. It employs bound tests to level relationships and conditional error correction models through ARDL specification to a new version of the Solow Growth model. Using annual data of the 1960–2010 period, results reveal a long-term relationship between energy consumption, international trade, and real income in Canada. It is also found that energy exporting activity is the determinant (driver) of energy consumption through the channel of real income and energy consumption is the determinant (driver) of exports through the channel of real income in the long term of the Canadian economy. Exports and energy use are the determinants (drivers) of real income in the long term of the Canadian economy; therefore, as conditional Granger causality tests suggest there is feedback relationship between energy consumption, international trade, and real income in the long term of the Canadian economy. The present study suggests that any energy conservation policies are likely to have negative influence on output and international trade in Canada.

The time-varying causality between spot and futures crude oil prices: A regime switching approach

One puzzling result in the literature on the linkages between the spot and futures crude oil prices is the sensitivity of causality tests to the sample period that one considers, which is usually handled by sample splitting. In order to overcome this difficulty, we propose a model that allows time-varying Granger causality between these prices. The model is used to investigate the time-varying causal linkages between the daily spot and futures prices for maturities of one, two, three and four months of the West Texas Intermediate (WTI) crude oil benchmark over the period January 2, 1986–July 31, 2013. The results indicate that the causal links between these oil prices are strongly time-varying. Both variables have a predictive power for each other during various subperiods, but not in all periods. Further, these periods coincide with major influential changes in the oil and stock markets and the geopolitics, implying that the findings are not statistical artifacts, but reflect real economic, financial and geopolitical regime changes. The full sample's conditional Granger causality tests based on the Markov-switching vector-error correction (MS-VEC) model reject both the causal impact of the lagged futures prices on the spot prices and the causal impact from the lagged spot prices on the futures prices. Therefore, the results show that the lead–lag relationship between the spot and futures oil markets exists only temporarily. While encompassing the previous findings, they also offer new insights into the nature of the lead–lag relationships between the spot and futures oil markets by taking non-linearity and time-variation into account.

Energy consumption, international trade, and real income in the USA: An empirical investigation using conditional error correction models

This study investigates the empirical relationship between energy consumption, international trade, and real income in the USA, which is the second largest energy consuming country following China. It employs bounds tests to level relationships and conditional error correction models through Auto-Regressive Distributed Lag specification. Using annual data from the 1960 to 2010 period, the results reveal a long-term relationship between energy consumption, international trade, and real income in the USA. It is also found that energy consumption is determinant of exports, imports, and real income. Conditional Granger causality tests suggest that there is a feedback relationship between energy consumption, international trade, and real income in the long-term for the USA economy.

Estimating tourism-induced energy consumption and CO2 emissions: The case of Cyprus

The present study investigates the long-run equilibrium relationship among international tourism, energy consumption, and carbon dioxide emissions (CO2), and the direction of causality among these variables in the case of a small island, Cyprus, which attracts more than 2 million international tourists every year. Results from “tourism-induced models” reveal that international tourism is in a long-run equilibrium relationship with energy consumption and carbon dioxide emissions; international tourist arrivals have positive, statistically significant, and inelastic impacts on the level of energy consumption and carbon dioxide emissons (which means negative impact for climate change). Error correction models reveal that carbon dioxide emission converges to its long-term equilibrium path by 95.4 percent speed of adjustment through the channels of tourism and energy consumption while, on the other hand, energy consumption converges to its long-term equilibrium path by 13.5 percent speed of adjustment through the channels of tourism and CO2 emissions. Finally, the major finding from conditional Granger causality tests is that international tourism is a catalyst for energy consumption and for an increase in the level of carbon dioxide emissions in Cyprus.

TOURISM AND GROWTH IN SINGAPORE: NEW EXTENSION FROM BOUNDS TEST TO LEVEL RELATIONSHIPS AND CONDITIONAL GRANGER CAUSALITY TESTS

This paper empirically investigates the tourism-led growth (TLG) hypothesis in the case of Singapore by employing the bounds test to cointegration, error correction models and Granger causality tests using annual data from 1960 to 2007. Results confirm the existence of long-term equilibrium relationship between international tourism and economic growth in the case of Singapore; real income growth converges to its long-term equilibrium level significantly by 51.4% in the TLG model. The major finding of this study is that the TLG hypothesis is confirmed for the Singaporean economy in the long-term as a result of conditional Granger causality tests.

Research Note: Testing the Tourism-Led Growth Hypothesis for Singapore – An Empirical Investigation from Bounds Test to Cointegration and Granger Causality Tests

This paper empirically investigates the tourism-led growth (TLG) hypothesis in the case of Singapore by employing the bounds test for cointegration, error correction models and Granger causality tests using annual data from 1960 to 2007. The results confirm the existence of a long-term equilibrium relationship between international tourism and economic growth in the case of Singapore; real income growth converges significantly toward its long-term equilibrium level of 51.4% in the TLG model. The major finding of this study is that the TLG hypothesis is confirmed for the Singaporean economy in the long term as a result of conditional Granger causality tests.