are indeed nonstationary then the results of linear causality analysis (conducted in previously described way) may lead to spurious results. 21 A possible solution for this problem is the construction of Vector Error Correction Model (VECM) or application of Toda – Yamamoto (1995) approach. 22 However, the outcomes presented in tables 4-5 ensure that we do not have to consider this problem in our research. Secondly, parametric tests (like Wald test) require some specific modelling assumptions (for example in asymptotic variant the error term should be white noise 23 ). If these assumptions do not hold then the test results may be spurious. Thus, before conducting linear causality analysis we run some diagnostic tests of residual series resulting from VAR models. 24 For VAR model constructed for CCU and RSMI-RDIJA variables the Doornik-Hansen test for multivariate normality (with null hypothesis referring to
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market. Probably for this reason no significant changes of unemployment or inflation rate in Polish economy as effect of declining WSE market index has been noticed for this year. It results from the economic theory that e.g. changes in stock index should have impact on changes in unemployment rate and vice versa. Linear causality test detects in our dataset as a whole a causal unidirectional relation (from stock index to unemployment), while nonlinear does not. Cointegration analysis led us (in line with linear causality test) to establishment of unidirectional long-run causality from changes of the stock market index to changes of the unemployment. Uncertainty of tests results concerning actual causality between these variables may be caused by the fact that in considered time period of more than ten years monthly data must be applied (because lack of more detailed data for industrial production in Poland) and some possible weak intermediate dependencies (causalities) might overlap and/or statistically cancel out.
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As Brock (1991) illustrates, linear Granger-causality tests can have low power uncovering nonlinear causal relations, which could also exist among variables. In addition, the assumption of linear causality may act as a limiting factor when the true relationship could be nonlinear. Furthermore, since linear methods depend on testing the significance of suitable parameters only in a mean equation, causality in any higher order structure cannot be explored Diks and DeGoede (2001). For that reasons, we examine the nonlinear non-Granger causality. Baek and Brock (1992), to emphasize the limitations of the linear assumption, suggest a nonparametric statistical method for detecting nonlinear Granger causality. Whereas, Hiemstra and Jones (1994) extended their work by introducing a modified test statistic for the nonlinear causality. In addition, Diks and Panchenko (2006) (hereafter DP) develop a new nonparametric test statistic for Granger causality which enable us to avoid the problem of over-rejection observed in the frequently used test proposed by Hiemstra and Jones. As a result, in our paper, we apply the nonlinear causality test proposed by Diks and Panchenko (2006) which can be used to detect possible nonlinear causality relationship between two time series. Following the representation by Diks and Panchenko (2006, p. 1649–1657), let a strictly bivariate process f ð X t ; Y t Þ g; and X t;
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Examining the null hypothesis of the opposite direction now, no non-linear causality is running from total useful energy to economic growth, there is sufficient statistical evidence in favor of rejecting the null hypothesis. In particular, regarding the raw differenced series the Hiemstra and Jones (1994) test rejects consistently the null hypothesis at the 0.05 significance level for the various selected lag-lengths, with the only exception to be the first lag-length, where the rejection arises at the 0.1 significance level (see the third column in Panel A). In similar fashion, the Diks and Panchenko (2006) test systematically rejects the null hypothesis but not always at the 0.05 significance level as the Hiemstra and Jones (1994) test does. Specifically, the null hypothesis is rejected at the 0.05 significance level for the third and the fifth lag while the rejection level rises at the 0.1 when the lag-length was set equal to 1, 2 and 4 (see the fourth column in Panel A ). Finally, for the delinearized series and under the same null hypothesis, the two non-linear and non-parametric causality tests provide adequate evidence towards the rejection of the null hypothesis. The third column in panel B shows that the Hiemstra and Jones (1994) test rejects consistently the null hypothesis at the 0.1 significance level for the different implemented lag-lengths, with the only exception to occur in the final lag, where the rejection is at the 0.05 significance level. Similar is the causality inference when the Diks and Panchenko (2006) test is performed. Despite the removal of any linear component for both series, the null hypothesis is still rejected at the 0.05 significance level, when the lag-lengths were set equal to 1, and 5, while the rejection level rises at the 0.1 when the lag-length was equal to 3. The only two
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At the beginning, linear Granger causality (the Wald variant) was tested separately in the bivariate and five-variate VAR models. The numbers of lags were determined using the Schwarz information criterion, additionally taking into account that the residuals of the selected VAR model should not have any significant cross-correlations. The results are given in Table 2. As can be seen, we either detected a lack of linear causality relationships or the existence of only weak ones between grain and livestock returns. Nonlinear Granger causality between the commodi- ties was verified using the H-J and D-P tests (both tests were applied to gain more reliable and defini- tive results) in three steps. In the first step, the tests were applied to the raw data, i.e., seasonally adjusted log returns. These results indicate whether causal relationships exist, but do not elucidate the nature of these relations. For this reason, two other steps were considered in the research. In the second step, the tests were applied for the residuals from the VAR models. Since the rejection of the null hypothesis for such residuals means that the detected causality is nonlinear, this filtering enables control of the linear interdependencies among the commodities. In the third step we further filtered the residuals from the VAR models using the BEKK (1,1) models and used the H-J and D-P tests for the standardised residuals Table 1. Contemporaneous correlation coefficients for
One must remember that the definition of Granger causality applied in this paper was intentionally formulated for stationary time series. Taking into account the results of previous empirical (Granger and Newbold, 1974) and theoretical (Phillips, 1986) deliberations, one may state that if the time series under study are indeed nonstationary then the outcomes of typical linear causality tests may lead to misleading conclusions. Thus, first we examined all time series for stationarity and identified their orders of integration. We did our best to carry out this part of the research with the greatest possible precision, since all further computations strongly depend on this stage.
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The linear causality framework is widely adopted in the behavioral finance literature when evaluating the predictive content that sentiment may have upon stock returns. The salient feature of this study is the fact that the analysis is carried out, employing an extended dataset, within a non-linear framework. The non-linear causality tests implemented are the well established H&J test and the D&P test. The advantage of the D&P test over the H&J is that it corrects for the observed over-rejection of the null hypothesis. Our empirical find- ings reveal that there is reasonable statistical evidence to support that sentiment embodies significant predictive power with respect to stock returns.
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Our non-linear Granger-causality framework is used to dis- entangle the effect of past climate variability on global veg- etation dynamics. To this end, climate data sets of obser- vational nature – mostly based on satellite and in situ ob- servations – have been assembled to construct time series (see Sect. 3.3) that are then used to predict NDVI anoma- lies following the linear and non-linear causality frameworks described in Sect. 2. Data sets have been selected from the current pool of satellite and in situ observations on the basis of meeting a series of spatiotemporal requirements: (a) expected relevance of the variable for driving vegeta- tion dynamics, (b) multidecadal record and global coverage available, and (c) adequate spatial and temporal resolution. The selected data sets can be classified into three different categories: water availability (including precipitation, snow water equivalent, and soil moisture data sets), temperature (both for the land surface and the near-surface atmosphere), and radiation (considering different radiative fluxes indepen- dently). Rather than using a single data set for each variable, we have collected all data sets meeting the above require- ments. This has led to a total of 21 different data sets which are listed in Table 1. They span the study period 1981–2010 at the global scale and have been converted to a common monthly temporal resolution and 1 ◦ × 1 ◦ latitude–longitude
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The short-run interaction between economic growth and renewable energy consumption is unidirectional without any feedback. In other words, causality findings suggest that only renewable energy consumption affects economic growth, finding that is consistent with the growth hypothesis. Otherwise, the evolution of economic activities across the countries in the sub-Saharan region has an impact on the conservation share of renewable energy in the short-run. In addition, there is no direct or indirect short-run causal link between health expenditures and renewable energy consumption, a finding that has not been previously investigated in the literature. These finding point out the role that renewable energy can play in the health footprint of sub-Saharan population, given that the region is characterized by a wealth of renewables sources unexploited. Moreover, access to health care can be improved and turn to be more reliable through renewable energy systems. If the countries in the area exploit their share of natural resources, then this can be substantially beneficial for their savings levels, allowing them to significantly reduce air pollution levels and then, to improve the quality of the health conditions of their citizens. In the sub-Saharan African countries, energy challenges impact extremely on the global performance of the region’s social and economic indicators. In fact, the region’s relatively poor health indicators can be greatly
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Numerous empirical studies have been steered on the association between international tourism and economic growth. But their empirical findings are mixed. For instance, Balaguer and Cantavella-Jorda (2002) studied the effect of tourism on economic growth in Spain by using quarterly time series data from 1975- 1997. They employed Johansen cointegration and Granger causality test to validate the connection between tourism and economic growth. Their findings showed that there is a long run relationship between tourism and economic growth and the causality runs from tourism to economic growth. Similarly, Grillon (2013) analyzed the dynamics between international tourism and economic growth in Dominican economy during the period 1991-2012. He employed ARDL bounds test to co- integration proposes by Pesaran et al. (2001) and the results confirmed the existence of a long-run equilibrium relationship between tourist arrivals and overall economic growth. Besides, the granger pairwise causality tests show causality running from tourist arrivals to aggregate output expansion. Moreover, Ohlan, (2017) studied the link between tourism and economic growth in India by over the period of 1960-2014. To investigate the relationship between the two variables he used the newly- developed Bayer and Hanck combined cointegration test and the result indicated that tourism and economic growth are cointegrated. In addition, the Granger-causation test indicated the tourism leads economic growth. Shih and Do (2016) also found a favorable impact of tourism on economic growth over the period of 1995-2013 by using a Granger causality test and Rolling Window regression method. The findings revealed that tourism has played a key role in driving economic growth in Vietnam economy.
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Abstract: Futures markets are mainly used as a tool for price discovery and for risk management on the spot markets and enable diversification for international portfolio investments. With this study we aim (1) to investigate the causality relationship between futures markets and spot market and (2) to examine the causality relationship between futures markets and spot markets in different countries. We are interested in both the futures markets - spot market relations and the interactions between the markets at international level. For variables we used the the BIST30 spot index and BIST30 futures contract representing the Borsa Istanbul market and the Dow-Jones 30 index and Dow-Jones 30 futures contract, which are the most important indices representing the US markets. Daily closing price data for the period between 2nd January, 2009 and 18th June, 2018 were analyzed using correlation, unit root test, causality test and regression equations. The results of the study show that the futures markets continue their price discovery role for both the spot markets and futures markets and are influential on other futures and spot markets at international level. These findings are important for investors wanting to invest in Turkey and in similarly considered emerging market economies. It will help investors take informed decisions by providing them with a more efficient price estimations utilizing the futures markets.
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The nexus between volatility of stock return and trading volume has been based on the models to reaching market of information and modeling distribution of stock prices (Çukur et al., 2012). These are sequential arrival information and mixed distribution hypothesis. Sequential arrival information hypothesis is put forward first time by Copel (1976) and then developed by Jennings and Starks et al. (1981). This hypothesis forecast a positive causality between the two variables because of containing information for explanation to current trading volume of past period prices and current trading prices of past period values on trading volume (Yılancı and Bozoklu, 2014). Model based on asymmetric information approach, all market participants weren’t detected the informations simultaneously from new market. Moreover, this perception process refers to a sequential process that followed. Therefore, according to the successive information hypothesis, absolute lagged returns have the power to predict today's trading volume. On the other hand, the opposite situation may be possible. This situation has been developed by Mixed Distribution Hypothesis (Clark, 1973; Epps and Epps, 1976). The model also assumes that new information led to a change in the price reach simultaneously for market participants.
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Not only oil price changes, but also the exchange rates are one of the key factors that affect the agri- cultural commodity prices. By using the Granger cau- sality analysis, Chen et al. (2008) examine Australia, Canada, Chile, New Zealand and South Africa in their study and state that the exchange rates can be used to forecast future commodity prices. Harri et al. (2009) use the VAR model, covering the monthly period from 2000 to 2008 and find out that while there is a co-integration relationship between corn, cotton, soybeans and crude oil prices and exchange rates, the same result is not valid for wheat. Frank and Garcia (2010) analyse the relationship among the crude oil prices, exchange rates and the agricultural commodity prices. The authors divide their sample into two groups. For the weekly data from 1998 to 2006 and from 2006 to 2009, they use the VAR and VEC models, respectively. The results indicate that between 2006–2009 periods the effects of oil price and exchange rates on the agricultural commodity prices are greater than the 1998–2006 period. By using the Granger causality technique, covering the quarterly period from 1969 to 2008, Gilbert (2010) addresses that the world GDP growth, the monetary expansion, oil price and the dollar exchange rate have a causal impact on the agricultural commodity prices.
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The study aimed at investigating the relationship between energy consumption at aggregate and disaggregate levels i.e., oil, coal, gas and electricity in different sectors (commercial, agriculture, industry, power and transport) of the economy with the economic growth in Pakistan. Annual time series data for the time period ranging from 1972 to 2014 has been used in this study. Autoregressive distributed lag bound testing approach for cointegration and to find the relationship between variables Granger causality test is applied. The results of the study showed that there exists a long run relationship between the dependent variable (economic growth) and independent variables (aggregate and disaggregate oil, coal, gas and electricity consumption in different sectors). It is also found that there exists a Neutrality Hypothesis between aggregate and disaggregate oil consumption and Conservation Hypothesis is found in aggregate and disaggregate coal, gas and electricity consumption. This study recommends that government should increase job opportunities in industrial sector where oil is used for production, shift their burden to cheap available resource from coal and transfer the units of electricity to industrial sector so that economic growth of Pakistan can be enhanced.
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manufacturing output utilizing the Divisa index. Aqeel and Butt 56 apply the Hsiao Granger causality to examine the association between disaggregate energy consumption and economic growth. Their results show that both energy (petroleum) and electricity consumption cause economic growth. Jamil and Ahmad 57 employ the trivariate model to examine the relationship between electricity consumption, economic growth, and electricity prices. They find that economic growth is positively linked with electricity consumption at the aggregate and disaggregate levels, and electricity demand increases private expenditures in the residential sector. Jamil and Ahmad 58 reinvestigate the major contributing factors of electricity demand function and find that economic growth has a positive impact on electricity consumption at both the aggregate and disaggregate levels. They note that energy and non-energy (capital and labor) variables play a vital role in stimulating manufacturing output.
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The notion that causation may run in the opposite direction, that is from economic growth to human resources (i.e. to education), is also relatively common. Investment in capital stock may result in sufficient economic growth providing the surplus which is necessary for further investment in the education sector. Many economists (e.g. Easterly et al. 1994, Caselli 1999) claim that demand for highly qualified staff is stimulated by investment in capital stock and new technologies. Moreover, some studies support the thesis that human resources and new technologies are complementary. In some papers it is pointed out that higher education supports the tendency towards the reduction in the current earnings in favour of higher future economic growth. The analysis of causal links between human resources and economic growth has been the subject of numerous contributions. De Meulemeester and Rochat (1995) performed an analysis of causality between higher education and economic growth in six countries. This research included Sweden, the United Kingdom, Japan, France, Italy and Australia and was conducted over different time periods. The causality running from higher education to economic growth was established in the case of Sweden, the United Kingdom, Japan and France while for Italy and Australia no causality was reported. For all analyzed countries the null hypothesis of no cointegration could not be rejected.
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The oft-made assumption that a movement of a part of a human body is the effect of one or more mental states derives, I think, from the causal theory of action, according to which actions are events caused by mental states. (No need for details here!). Many suppose that such a theory is established once it is recognized that a person may have done something she meant to because she thought such-and-such and wanted so-and-so; and then, on the strength of the fact that this because implicates causality, they assume that beliefs and desires stand to actions as causes to effects (with decisions or intentions maybe intervening). 13 An example will help to bring out what is problematic about the
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The study of Kollias (1991) included a multiple regression analysis in order to test if there is an arms race between Turkey and Greece. He hypothesized that when suitable variables are introduced, it is possible to capture the way that Greek military expenditure is influenced by the Turkish threat. He claimed that these variables have to allow for the strategic environment within which decisions are made by Greek military authorities. He found evidence that because of differences in size and quantitative of military disadvantage, Greece tries to offset this by gaining a qualitative advantage over Turkey. Kollias & Paleologou (2002) examined if there is a Greek-Turkish arms race for the period of 1950-1999. They applied the causality methodology employed by Hendry & Ericsson (1982). Their empirical results support the view that there is an arms race between the two countries, since there is a bi-directional causality between military spending of these countries. However, they implied that military spending is a function of many determinants (economic constraints, alliance membership, external and internal concerns), which can have an impact on the level of military spending.
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relation to the EKC hypothesis. Most of previous studies have relied on parametric and semiparametric specifications of this relationship and have presumed a priori that the causality runs from economic growth to environmental quality. However, there is not any theoretical justification for this assumption. Other studies assume a linear relationship between growth and carbon emissions without considering structural breaks and other forms of nonlinearity. Rules, regulations (e.g., incentives and penalties), programs and international agreements on energy issues have changed over the last few decades and have affected participation and compliance, thereby introducing abrupt and smooth breaks in this relationship (Barrett and Stavins, 2003; Price, 2005). This consideration
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Detection of cause-effects relationships among variable has been one of the fundamental questions of most part of natural or social sciences, including Economics. The bibliometric study of Hoover (2004, p.4) is very illustrative: 70% of the articles in the JSTOR archives published in 2001, contain words ‘in a causal family (“cause”, “causes”, “causal”, “causally” or “causality”)’. The percentage increases up to the 80% if the search is restricted only to econometric articles. It is clear that causality is one of the leading topics in mainstream Economics and Econometrics. In contrast, the Index of Lesage and Pace (2009) textbook on Spatial Econometrics contains almost 1,000 headwords, none of which is related to the Hoover’s causal family. Exactly the same can be said with respect to other textbooks published in the field such as Paelinck and Klaassen (1979), Anselin (1988), Upton and Fingleton (1985), Anselin and Florax (1995), Tiefelsdorf (2000), Griffith (2003), Anselin, Florax and Rey (2004), Getis, Mur and Zoller (2004) or Arbia (2006). This silence is striking and hardly justifiable.
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