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The causal relationship between money and income (output) has been an important topic and has been extensively studied. However, those empirical studies are almost entirely on Granger-causality in the conditional mean. Compared to conditional mean, conditional quantiles give a broader picture of an economy in various scenarios. In this paper, we explore whether forecasting conditional quantiles of output growth can be improved using money growth information. We compare the check loss values of quantile forecasts of output growth with and without using past information on money growth, and assess the statistical significance of the loss-differentials. Using U.S. monthly series of real personal income or industrial production for income and output, and M1 or M2 for money, we find that out-of-sample quantile forecasting for output growth is significantly improved by accounting for past money growth information, particularly in tails of the output growth conditional distribution. On the other hand, money–income Granger-causality in the conditional mean is quite weak and unstable. These empirical findings in this paper have not been observed in the money–income literature. The new results of this paper have an important implication on monetary policy, because they imply that the effectiveness of monetary policy has been under-estimated by merely testing Granger-causality in conditional mean. Money does Granger-cause income more strongly than it has been known and therefore information on money growth can (and should) be more utilized in implementing monetary policy.

The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model.

The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these variables using the vector autoregression model. Further, the author estimates the EGARCH model based on daily data and test for Granger causality in variance between commodity futures using Hong’s (2001) approach.

The results of the RV approach reveal that the hypothesis on the existence of a mutual volatility spillover between crude oil and petroleum product markets is accepted. However, the results of the conventional approach indicate that all the hypotheses on Granger causalities in variance are rejected. The methodology based on intraday high-frequency data exhibits higher power than the conventional approach based on daily data.

This is the first paper to investigate Japan’s oil market using RV. The authors conclude that the approach based on RV is universally adoptable when testing for Granger causality in variance.

Causality between corporate financial performance (CFP) and corporate social performance (CSP) has been extensively debated in previous research works; however, little research has been done to investigate the long-run dynamics between these two constructs. The purpose of this paper is to enrich the CFP–CSP literature by estimating the long-run equilibrium relationship between financial performance and social performance in the banking sector in the Gulf Cooperation Council countries over the period 2009–2019.

The paper adopts an approach that is primarily used in financial economics: first, the authors perform panel long-run Granger causality following Canning and Pedroni’s procedure to indicate the direction of the causal relationship. Second, the authors estimate an error correction model using Chudik and Pesaran’s (2015) dynamic common correlated effects mean group estimator to determine the sign of the relationship.

The present research findings prove the existence of a long-run equilibrium relationship between CFP and CSP, while indicating at the same time that panel Granger causality runs positively from CSP to CFP, which means that changes in CSP produce lasting changes in CFP.

The findings of the paper would guide strategists to build fit for purpose corporate social responsibility (CSR) strategies in their firms and establish a continuous investment in CSR activities in the long run rather than harshly investing in CSR activities in the short run.

To the best of the authors’ knowledge, this paper is the first one to address heterogeneity in long-run Granger causality tests to estimate the relationship between CSP and CFP.

To explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.

Although housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.

We discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.

By excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

The purpose of this paper is to investigate both linear and/or nonlinear effects of inflation on income inequality and to test the Kuznets hypothesis using panel data of 24 developed countries (DCs) and 66 developing countries (LDCs) observed over the period of 1990–2014.

This paper explores the short- and long-run Granger causality relationship between inflation and income inequality using the Toda and Yamamoto (1995) procedure and a Vector Error Correction Model (VECM) approach. The existence of a nonlinear relationship between inflation and income inequality is confirmed implying as inflation rises income inequality decreases. Income inequality then reaches a minimum and then starts rising again. The findings of this paper show the existence of Kuznets “U-shaped” hypothesis between income inequality and real GDP per capita in DCs group, and the existence of Kuznets’ inverted “U-shaped” hypothesis for LDCs group.

The results indicate that there is no bi-directional Granger causality between inflation and income inequality in the short-run, but, there is bi-directional Granger causality in the long-run for both the DCs and LDCs group. The results help us to assess the effectiveness of monetary policy in reducing income inequality in both the DCs and LDCs group. As a policy implication, monetary policy is often aimed at controlling the annual rate of inflation in the long-run with a short-run focus on reducing output gaps and creating employment. However, managing inflation may have implications for income inequality.

This is original research paper which analyzes the “U-shaped” and inverted “U-shaped” paths of income inequality and real GDP per capita for large sample of two group countries including developed and developing countries, respectively. Also, this paper analyzes the nonlinear relationship between inflation and income inequality in two groups. Furthermore, this paper investigates the short- and long-run relationship between variables. The results are important for policy makers.

The purpose of this paper is to investigate the dynamic relationship among Gold, Crude oil, Indian Rupee-US Dollar and Stock market-Sensex (gold, oil, dollar and stock market (GODS)) in the pre-crisis, the crisis and the post-crisis periods in the Indian context.

The authors use Johansen’s cointegration technique, Vector Error Correction Model (VECM), Vector Auto Regression, VEC Granger Causality/Block Exogeneity Wald Test, and Granger Causality and Toda Yamamoto modified Granger causality to study long-run relationship and causality.

Johansen’s cointegration test results indicate that there is a long-run equilibrium relationship among the variables in the pre-crisis and the crisis periods but not in post-crisis period. VECM results report that none of four models of the variables show long-run causality in the pre-crisis period. During the crisis period, both crude oil and Sensex models show long-run causality. However, in some cases, results indicate short-run causality. The authors find one-way causality from USD and Sensex to crude oil, and from gold and Sensex to USD. Thus, the authors conclude that the relationship among GODS is dynamic across global financial crisis.

The research findings of this study are vital to the large group of stakeholders and participants of gold, crude oil, US dollar and stock market in emerging economies like India. The results are useful to importers, exporters, government, policy makers, corporate houses, retail investors, portfolio managers, commodity traders, treasury and fund managers, other commercial traders, etc.

This study is one of its kinds as it investigates the relationship among GODS in India in different sub-periods like before, during and after the global financial crisis of 2008. None of the studies compare phase-wise relationship among GODS in the Indian context. The study contributes to the economic theory and the body of knowledge. It highlights the need to revisit the economic theory to explain the interplay mechanism among GODS.

This paper seeks to study the impact of bio‐fuel policies on oil and food futures prices from December 6, 2004 to August 1, 2008.

The daily closing prices of brent crude oil, light sweet crude oil, corn, wheat, soybeans, and rough rice futures from December 6, 2004 to August 1, 2008 are used in this research. The vector error correction model is applied in order to study the impact of bio‐fuel policies on oil and agricultural futures prices.

Unit root and cointegration tests show that the brent crude oil, light sweet crude oil, wheat, corn, soybeans, and rough rice futures are stationary and have a long‐run equilibrium relationship. Granger causality tests of the four periods shows that the causality relationship between oil futures and food futures changes over time. The first period result shows many Granger causes on several variables at a 5 percent significance level. The second period has more Granger causes at the 5 percent significance level. However, the Granger causality relationships become fewer and fewer in the third and fourth period.

This is the first paper to study the impact of the four major bio‐fuel policies of Brazil, the European Union, and the USA.

Since the famous tapering talk of Bernanke, US Dollar (USD) made a significant appreciation on emerging market local currencies. When the stock indices are adjusted to USD, a negative relationship is usually the case. USD index is a natural candidate for measurement of these effects. It is seen that some emerging stock indices exhibit negative causality with USD index in Granger sense. Moreover, the authors take into account rolling correlations of USD index and the relevant stock indices and examine them on the investment horizon beginning from tapering talk. The authors deduce that Granger causality test and correlation results are coherent with each other which sheds light to the famous discussion whether causality implies correlation or vice versa.

This study examines the causal relationship between exports and economic growth in sub-Saharan African (SSA) countries during the period 1980 to 2017. The study also examines whether the causality between these two macroeconomic variables depends on the countries' stage of development as proxied by their per capita income.

The study uses a panel cointegration test and panel Granger-causality model to examine the link between exports and growth. The study also incorporates external debt as an intermittent variable in a bivariate setting between exports and economic growth, thereby creating a dynamic multivariate panel Granger-causality model.

Although the study found the existence of a long-run relationship between exports and economic growth, the study failed to find any export-led growth response in both low-income and middle-income countries. Instead, the study found evidence of a bidirectional causality and a neutrality response in middle-income and low-income countries, respectively. The study, therefore, concludes that the benefits of an export-led growth hypothesis may have been oversold, and that the strategy may not be desirable to some low-income developing countries.

These findings have important policy implications as they indicate that the causality between exports and economic growth in SSA countries varies with the countries' stage of development. Consistent with the contemporary literature, the study cautions low-income SSA countries against over-relying on an export-led growth strategy to achieve a sustained growth path as no causality between exports and economic growth has been found to exist in those countries. Instead, such countries should consider pursuing new growth strategies by building the domestic demand side of their economies alongside their export promotion strategies in order to expand the real sector of their economies. For middle-income countries, the study recommends that both export promotion strategies and pro-growth policies should be intensified as economic growth and exports have been found to reinforce each other in those countries.

Unlike the previous studies, the current study disaggregated the full sample of SSA countries into two subsets – one comprising of low-income countries and the other consisting of middle-income countries. In addition, the study uses a multivariate Granger-causality model in order to address the emission-of-variable bias. To our knowledge, this may be the first study of its kind in recent years to examine in detail the causal relationship between exports and economic growth in SSA countries using an ECM-based multivariate panel Granger-causality model.

本研究旨在探討在1980年至2017年期間撒哈拉以南非洲國家的出口、與其經濟增長之間的因果關係，亦探討這兩個宏觀經濟變量之間的因果關係、會否取決於有關國家所處以人均收入來衡量的發展階段。

本研究雖然發現出口與經濟增長存有一個長期性關係，唯未能於低收入國家或中等收入國家、找到任何出口帶動的增長反應。研究反而找到證據，證實中等收入國家為一雙向性因果關係反應，而低收入國家則為一中立性反應。因此，研究的結論是：出口必能帶動經濟增長這假設被過度吹噓，而且，對部份低收入發展中國家而言，實施以出口帶動經濟增長的策略或許是沒有用的。

本研究的結果在政策方面有其重要意義。這是因為研究結果顯示、於撒哈拉以南非洲國家、出口與經濟增長之間的因果關係，會因有關國家所處的發展階段而有所變更。與當代文獻一樣，本研究提醒低收入的撒哈拉以南非洲國家，不要過度依賴以出口帶動增長的策略來謀求踏上持續增長之路，這是因為在這些國家，出口與經濟增長之間的因果關係仍未確立。他們反而應考慮推行新增長經濟策略，方法是在實施推動出口的策略的同時，也要建立其經濟的國內需求面，以擴大其經濟實業部門。就中等收入國家而言，本研究建議他們應增強推動出口的策略及強化促進增長的政策，這是因為在這些國家裏，經濟增長及出口已被證實會互為增強。

有別於過去的研究，本研究把撒哈拉以南非洲國家的整體樣本分解為兩個子集：一個包括低收入國家，另一個則包括中等收入國家。而且、研究使用了多變量面板格蘭傑因果關係模型、以處理遺漏變數偏差的問題。據我們了解，這大概是近年首個同類研究、以基於歐洲共同市場多變量面板格蘭傑因果關係模型、來詳細探討於撒哈拉以南非洲國家、出口與經濟增長之間的因果關係。

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k -fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post- sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on GC between inflation and unemployment rates.