Time-Varying Causality Impact of Global Economic Conditions Index on Remittances in Lebanon

Causality between volatility and the weekly economic index during COVID-19: The predictive power of efficient markets and rational expectations

The main objective of this study is to empirically re-investigate the money-prices nexus for Malaysia through the Johansen multivariate cointegration and the modified Wald (MWALD) causality techniques. This study covered the monthly dataset from 1971:M1 to 2008:M11. The Johansen cointegration test suggests that the variables under investigation are co-move in the long run. Furthermore, the MWALD causality test shows a bidirectional causal relationship between money supply (M2) and aggregate prices, meaning that both the monetarist's and also the structuralists' views are vindicated in the Malaysian economy. However, the time-varying cointegration and causality tests indicate that the cointegrating and also the causal relationships are not stable over the analysis period. These results suggest that inflation in Malaysia is not purely a monetary phenomenon. Therefore, implementing a tighter monetary policy may not be an effective macro-economic instrument in managing the inflationary behaviour in the Malaysian economy.

The purpose of this paper is to provide an overview of various economic growth and employment approaches that have been popular throughout economic history, with a focus on linking and delinking aspects of GDP and employment in the Indian economy. The semi-log quadratic model was used for the trends of GDP and employment across the selected countries. For the determination of the linkages between the variables, the Granger causality test promulgated by Engel and Granger (1987), and the maximum likelihood-based technique of Johansen and Julius (1990) and Johansen (1992) were used in the study. The results found that employment and GDP are two different aspects of an economy not only in the Indian economy but across the majority of the world economies. The results of model specification proved the presence of both long-run and short-run relationships between GDP and employment by the Johansen co-integration test in the Indian economy over the period. It has been found that the increase in GDP is negatively influencing the employment level by the VECM model as a 1 per cent increase in GDP results in 0.28 per cent of job losses in the Indian economy. This study aims to provide an overview of several approaches to economic growth and employment that have been popular throughout economic history up to the present, with an emphasis on linking and delinking aspects of economic growth and employment. In the context of an economy, economic growth and employment are two distinct aspects that should be tackled with separate economic strategies.

Neurons in many brain regions change their spiking responses and interactions among them to relevant stimuli. Tracking the dynamics of neural system is crucial for understanding how neural systems adapt their responses to relevant biological information. Granger causality [1] has been effectively used to assess directional interactions between continuous neural signals, but it cannot be directly applied to neural spike trains viewed as point processes. Recently, methods that extend Granger’s viewpoint to the point process modality have been developed [2], [3] to identify causal interactions between neural spike trains. These methods, however, depend upon stationarity assumptions – which might not be valid when the interactive causal influences themselves are time-varying. We propose a novel probabilistic method for tracking the time-varying causal neural interactions based on sequential prediction of point process models. The time-varying causality from neuron x to y is assessed by the variability of a windowed version of the point process log-likelihood ratio (LLR), where one model incorporates only the past y and the other incorporates the past of both x and y. The proposed method successfully tracks the time-varying causal network in simulated data, and when applied to real neural data recorded in the rat insular cortex, it identifies the change of causal relationships between neurons to a relevant behavioral stimulus (see Figure ​Figure11). Figure 1 Tracking time-varying causality network. Sij(t) represents time-varying causality effect from neuron i to j. A. Simulation: Proposed time-varying causality measure had larger values when x caused y than when x did not cause y. B. Real data analysis: Time-varying ...

This study is the first attempt to investigate the relationship between CO2 emissions, energy consumption, and economic growth at a state level, for the 50 US states, through a time-varying causality approach using annual data over the periods 1960-2010. The time-varying causality test facilitates the better understanding of the causal relationship between the covariates owing to the fact that it might identify causalities when the time-constant hypothesis is rejected. Our findings indicate the existence of a time-varying causality at the state level. Specifically, the results probe eight bidirectional time-varying causalities between energy consumption and CO2 emission, six cases of two-way time-varying causalities between economic growth and energy consumption, and five bidirectional time-varying causalities between economic growth and CO2 emission. Moreover, we examine the traditional environmental Kuznets curve hypothesis for the states. Notably, our results do not endorse the validity of the EKC, albeit the majority of states support an inverted N-shaped relationship. Lastly, we can identify multiple policy implications based on the empirical results.

Purpose The importance of banking and insurance, as an important part of the financial system, has been well accepted in the growth literature. Acting as financial intermediaries they perform important functions that may contribute in economic growth. Addressing this issue, the purpose of this paper is to empirically examine the relationship between banking, insurance and economic growth in India in the post-liberalized era when the private sector was allowed to operate banking and insurance business. Design/methodology/approach In order to find the long-run and short-run relationship between banking, insurance and economic growth, the study uses the VAR-vector error correction model (VECM) along with Granger causality test to explore any causal relationship. Findings The results indicate that there is the long-term relationship between banking, insurance and economic growth and the causality results show a bi-directional relationship between insurance activity and economic growth; however, banking is not granger cause of insurance or economic growth rather it is economic growth that cause banking development. Research limitations/implications The only limitation to the study is the non-availability of monthly figures of GDP. The study therefore, as suggested by RBI, uses monthly data set of Index of Industrial Production to measure economic growth. Practical implications The findings of the study give policy directions to the policymakers to make strategies that are conducive toward boosting development in insurance in order to achieve the targeted economic growth. Originality/value This work is the first attempt to study the conjoint relationship between banking, insurance and economic growth on the Indian economy after the reforms were initiated in the financial sector.

The aim of this studyis to determine asymmetric causal relationship between Turkish economy andfreight rates in the ISTFIX region by separating the positive and negativeshocks in the variables. Therefore, asymmetric causality test developed byHatemi-J is used. Unlike other studies, some stock market variables in Turkeyare selected as economic indicators and included in the analysis. Selectedstock market variables are BIST 100, BIST Industrial and BIST Transportationindices. The data set used in the study consists of 558 observations on aweekly basis covering the dates between 1st January 2018 and 10th September2018. As a result of the study, while causal relationships are expected betweenboth negative and positive shocks, only negative shocks in all three stockexchanges are found to cause negative shocks in the ISTFIX index. This resultssuggest that negative news in the economy is felt directly in the maritimemarket, but the impact of positive news is not immediately reflected.Furthermore, it is thought that the producers refrain from ordering more rawmaterials when they encounter negative shocks in the market and negativelyaffect the demand for sea transportation, which also causes to decrease in thefreight rates. These results also indicate that the stock values in Turkey maybe a leading indicator for the freight market in the ISTFIX region.

PurposeThis study aims to attempt to investigate the time-varying causality and price spillover effects between crude oil and exchange rate markets in G7 economies during the COVID-19 and Russia–Ukraine crises.Design/methodology/approachThis study uses time-varying Granger causality test and spillover index.FindingsThis study finds a time-varying causality between exchange rate returns and oil prices, implying that crude oil prices have the predictive power of the foreign exchange rate markets in G7 economies in their domain. Furthermore, the total spillover index is estimated to fall significantly around COVID-19 and war events. However, this index is relatively high – more than 57% during the first wave of COVID-19 and decreasing slightly during the Russia–Ukraine conflict.Practical implicationsThis outcome supports the hypothesis that the majority of the time-varying interaction between exchange rates and oil prices takes place in the short term. As a result, the time-varying characteristics provide straightforward insight for investors and policymakers to fully understand the intercorrelation between oil prices and the G7 exchange rate markets.Originality/valueFirst, this study has reexamined the oil–exchange rate nexus to highlight new evidence using novel time-varying Granger causality model recently proposed by Shi et al. (2018) and the spillover index proposed by Diebold and Yilmaz (2012). These approaches allow the author to improve understanding of time-varying causal associations and return transmission between exchange rates and oil prices. Second, compared to past papers, this paper has used data from December 31, 2019, to October 31, 2022, to offer a fresh and accurate structure between the markets, which indicates the unique experience of the COVID-19 outbreak and Russia–Ukraine war episodes. Third, this study analyzes a data set of seven advanced economies (G7) exhibiting significant variations in their economic situations and responding to global stress times.

Purpose - The objective of this paper is to discover if there exists a relationship between the economic index and distribution industry index in Korean. Because of the distribution industry boom in the recent years, a lot of interest in the relationship between the economic index and distribution industry index in Korean and the economy has been generated. This article examine on the mutual influence between economic index and distribution industry index in Korean. Research design, data, and methodology - For this purpose, we use the vector-auto regression model, impulse response function and variance decomposition of the economic index and distribution industry index, Granger causality test using weekly data on the economic index and distribution industry price index in korea. The sample period is covering from January 2, 2010 to August 31, 2019. The VAR model can also be linked to cointegration analysis. Cointegration Analysis makes possible to find a mechanism causing x and y to move around a long-run equilibrium (Engle and Granger, 1987). This equilibrium means that external shocks may separate the series temporarily at any particular time, but there will be an overall tendency towards some type of long-run equilibrium. If variables are found to have this tendency they are said to be cointegrated and a long-run relationship between these series is established. These econometric tools have been applied widely into economics and business areas to analyze intertemporal linkages between different time series. Results - This research showed following main results. First, from the basic statistic analysis of the economic index and distribution industry index in Korean, the economic index and the distribution industry index in korea have unit roots. Second, there is at least one cointegration between the economic index and distribution industry index in Korean. Finally, the correlation between of the economic index and the distribution industry index in korea is (+) 0.528876. Conclusions - We find that the distribution industry price index Granger cause the economic index in korea. As a consequence, the distribution industry index affect the economic index in Korean. The distribution industry index to the economic index is stronger than that from the economic index to the distribution industry index.

The study investigates the predictive efficacy of various machine learningmet-hodologies, encompassing Random Forest (RF) regression, Gradient Boosting (GB), Xtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and a deep learning technique, specifically Long Short-Term Memory (LSTM). The benchmark method employed is the autoregressive (AR) model of order 1. With a focus on forecasting money demand for the Indian economy, a crucial component for achieving the Cent­ral Bank of India's inflation targeting objective, a comprehensive monthly dataset from 1997 to 2021 is utilized.The obtained results underline the robust predictive capabilities of the employed models concerning both narrow and broad money demand forecasts. By em­ploying a range of evaluation metrics, the study rigorously compares the predictive performance of these models. Using the expanding window cross validation with time series split, the models are cross-validated to ensure accurate forecastsof monetary aggregates. Moreover, the Diebold – Mariano test is utilized to evaluate and compare the quality of forecasts.In particular, the research finds the superiority of LSTM and LASSO in predictive capabilities for narrow and broad money demand, respectively. These findings collectively contribute to enhancing the understanding of money demand prediction, thus facilitating informed decision-making within the realm of monetary policy.

PurposeThe primary objective of the paper is to examine the asymmetric Cointegration and asymmetric causality between financial development and poverty alleviation on annual data in Indian context over the period from 1980 to 2019.Design/methodology/approachFirst nonlinearity test by Brooks et al. (1999) is applied to ascertain the nonlinear behavior of the variables used. Once the nonlinear behavior of variables is confirmed, asymmetric and nonlinear unit root tests by Kapetanios and Shin (2008) are applied to check for the order of integration of selected variables. Next, nonlinear autoregressive distributed lag model (NARDL) is employed to analyze the asymmetric Cointegration. Finally, Hatemi-j- asymmetric causality tests is applied to work out the direction of asymmetric causality.FindingsThe empirical findings document the existence of asymmetries in the short-run as well as long-run between poverty and financial development. The asymmetry reveals that negative financial development shocks leave a more profound impact on poverty alleviation than their positive equivalents. The findings of Wald's test also confirm the presence of asymmetric Cointegration. The asymmetric cumulative dynamic multipliers used to examine the behavior of asymmetries and adjustments with respect to time lend credence to the results calculated using NARDL estimator. This result exhibits the robustness of the model. Furthermore, the result emanating from recently introduced asymmetric causality test reveals a unidirectional asymmetric causality between negative shocks in financial development and poverty. The findings of the present study necessitate the need for investigating asymmetric and nonlinear effects in finance–poverty nexus, which existent literature has completely neglected, in order to have relevant policy conclusions.Research limitations/implicationsThe study used “Per capita consumption expenditure” as a measure for poverty due to lack of continuous time series data on headcount ratio. In future, researchers can extend this study by incorporating headcount ratio as a measure of poverty in their respective works. There is further scope of research on this issue by finding out the impact of formal and informal sources of credit on poverty separately. A panel data study for developing countries over a period of time could further confirm/negate the findings of the present study.Originality/valueTo the best of the authors’ knowledge none of the studies in Indian context has scrutinized asymmetric and nonlinear impact of financial development on poverty. To dredge up asymmetric structures at work, the authors have used the highly celebrated NARDL estimator. To enrich the existent body of knowledge along the lines of asymmetric (nonlinear) linkages, the authors have also used recently introduced asymmetric causality test by Hatemi-j-(2012) to find out the direction asymmetric causality.

An analysis of the time-varying causality and dynamic correlation between green bonds and US gas prices

The interaction between the private and public sectors is one of the main focuses of economics. They affect each other positively or negatively. This paper aims to determine the potential dynamic impacts of the public investments on the private investments in Turkey by running asymmetric causality and to detect a structural relationship of two sectors by using nonlinear and time-varying causality. The result illustrates that there is a crowding-out effect from the public to private investment. On the other side, time-varying and nonlinear causality reach an inverse direction for the causal effects stemming from the private to the public.

Time-varying causality between renewable and non-renewable energy consumption and real output: Sectoral evidence from the United States

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