Causal relationships between economic policy uncertainty and housing market returns in China and India : evidence from linear and nonlinear panel and time series models

In this paper, we modify the multivariate nonlinear causality test to be panel nonlinear causality test and we apply these and other existing related tests to examine the causal relationship between growth in economic policy uncertainty (EPU) and real housing returns in China and India using quarterly data from 2003:01 to 2012:04. Both panel linear and nonlinear Granger causality tests suggest the existence of only linear and nonlinear unidirectional causality relationships from growth in EPU to real housing returns in both China and India, and bivariate linear Granger causality tests suggest the existence of only linear unidirectional causality relationship from growth in EPU to real housing returns only in China. However, nonlinear bivariate Granger causality tests conclude the existence of nonlinear bidirectional causality relationships between growth in EPU and real housing returns in both China and India and cross bivariate linear and nonlinear Granger causality tests discover that there is only a linear causality relationship from Indian growth in EPU to Chinese housing returns. The results confirm the relevance of EPU data to better understand and predict the future behaviour of housing market returns in these countries.Ministerio de Economia y Competitividad [ECO2014-55496-R]; Hang Seng Management College, University of Pretoria; Asia University; Lingnan University; Research Grants Council (RGC) of Hong Kong [UGC/IDS14/15, 12500915, 134036]http://www.degruyter.com/view/j/snde2019-04-01hj2018Economic

Do both demand-following and supply-leading theories hold true in developing countries?

To overcome the limitations of the traditional approach which uses linear causality to examine whether the supply-leading and demand-following theories hold. As certain countries will be found not to follow the theory by using the traditional approach, this paper first suggests using all the proxies of financial development and economic growth as well as both multivariate and bivariate linear and nonlinear causality tests to analyze the relationship between financial development and economic growth. The multivariate nonlinear test not only takes into consideration both dependent and joint effects among variables, but is also able to detect a multivariate nonlinear deterministic process that cannot be detected by using any linear causality test. We find five more countries in which the supply-leading hypothesis and/or demand-following hypothesis hold true than with the traditional approach. However, there is still one country, Pakistan, for which no linear or nonlinear causality is found between its financial development and economic growth. To overcome this limitation, this paper suggests including cointegration in the analysis. This leads us to conclude that either supply-leading or demand-following hypotheses or both hold for all countries without any exception. There will be some types of relationships between economic growth and financial development in any country such that either they move together or economic growth causes financial development or financial development causes economic growth without any exception. The finding in our paper is may be useful for governments, politicians, and other international institutions in their decision making process for the development of the countries and reducing poverty

Do both demand-following and supply-leading theories hold true in developing countries?

To overcome the limitations of the traditional approach which uses linear causality to examine whether the supply-leading and demand-following theories hold. As certain countries will be found not to follow the theory by using the traditional approach, this paper first suggests using all the proxies of financial development and economic growth as well as both multivariate and bivariate linear and nonlinear causality tests to analyze the relationship between financial development and economic growth. The multivariate nonlinear test not only takes into consideration both dependent and joint effects among variables, but is also able to detect a multivariate nonlinear deterministic process that cannot be detected by using any linear causality test. We find five more countries in which the supply-leading hypothesis and/or demand-following hypothesis hold true than with the traditional approach. However, there is still one country, Pakistan, for which no linear or nonlinear causality is found between its financial development and economic growth. To overcome this limitation, this paper suggests including cointegration in the analysis. This leads us to conclude that either supply-leading or demand-following hypotheses or both hold for all countries without any exception. There will be some types of relationships between economic growth and financial development in any country such that either they move together or economic growth causes financial development or financial development causes economic growth without any exception. The finding in our paper is may be useful for governments, politicians, and other international institutions in their decision making process for the development of the countries and reducing poverty

Detecting and quantifying causal associations in large nonlinear time series datasets

Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields

Capital Inflows, Inflation and Exchange Rate Volatility : An Investigation for Linear and Nonlinear Causal Linkages

Since the early 1990s, there is an upsurge in foreign capital flows to developing economies, particularly into emerging markets. One view argues that capital inflows do help to increase efficiency, a better allocation of capital and to fill up the investment-saving gap. Adherents to that view advise countries to launch capital account liberalisation. In this study, we investigate the effects of capital inflows on domestic price level, monetary expansion and exchange rate volatility. To proceed with this, linear and nonlinear cointegration and Granger causality tests are applied in a bi-variate as well as in multivariate framework. The key message of the analysis is that there is a significant inflationary impact of capital inflows, in particular during the last 7 years. The finding suggest that there is a need to manage the capital inflows in such a way that they should neither create an inflationary pressure in the economy nor fuel the exchange rate volatility.Capital Inflows, Inflationary Pressures, exchange rate volatility, Monetary Expansion, Nonlinear Dynamics

Capital Inflows, Inflation and Exchange Rate Volatility: An Investigation for Linear and Nonlinear Causal Linkages

Since the early 1990s, there is an upsurge in foreign capital flows to developing economies, particularly into emerging markets. One view argues that capital inflows do help to increase efficiency, a better allocation of capital and to fill up the investment-saving gap. Adherents to that view advise countries to launch capital account liberalisation. In this study, we investigate the effects of capital inflows on domestic price level, monetary expansion and exchange rate volatility. To proceed with this, linear and nonlinear cointegration and Granger causality tests are applied in a bi-variate as well as in multivariate framework. The key message of the analysis is that there is a significant inflationary impact of capital inflows, in particular during the last 7 years. The finding suggest that there is a need to manage the capital inflows in such a way that they should neither create an inflationary pressure in the economy nor fuel the exchange rate volatility.Capital Inflows, Inflationary Pressures, Exchange Rate Volatility, Monetary Expansion, Nonlinear Dynamics

Nonlinear causality testing with stepwise multivariate filtering

This study explores the direction and nature of causal linkages among six currencies denoted relative to United States dollar (USD), namely Euro (EUR), Great Britain Pound (GBP), Japanese Yen (JPY), Swiss Frank (CHF), Australian Dollar (AUD) and Canadian Dollar (CAD). These are the most liquid and widely traded currency pairs in the world and make up about 90% of total Forex trading worldwide. The data covers the period 3/20/1987-11/14/2007, including the Asian crisis, the dot-com bubble and the period just before the outbreak of the US subprime crisis. The objective of the paper is to test for the existence of both linear and nonlinear causal relationships among these currency markets. The modified Baek-Brock test for nonlinear non-causality is applied on the currency return time series as well as the linear Granger test. Further to the classical pairwise analysis causality testing is conducted in a multivariate formulation, to correct for the effects of the other variables. A new stepwise multivariate filtering approach is implemented. To check if any of the observed causality is strictly nonlinear, the nonlinear causal relationships of VAR/VECM filtered residuals are also examined. Finally, the hypothesis of nonlinear non-causality is investigated after controlling for conditional heteroskedasticity in the data using GARCH-BEKK, CCC-GARCH and DCC-GARCH models. Significant nonlinear causal linkages persisted even after multivariate GARCH filtering. This indicates that if nonlinear effects are accounted for, neither FX market leads or lags the other consistently and currency returns may exhibit statistically significant higher-order moments and asymmetries.nonparametric Granger causality; filtering; multivariate GARCH models; spillovers

Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests

This paper investigates the relationship between stock market volatility and the business cycle in four major economies, namely the US, Canada, Japan and the UK. We employ both linear and nonlinear bivariate causality tests and we further conduct a multivariate analysis to explore possible spillover effects across countries. Our results suggest that there is a bidirectional causal relationship between stock market volatility and the business cycle within each country and additionally reveal that the recent financial crisis plays an important role in this context. Finally, we identify a significant impact of the US on the remaining markets