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Methods used to infer causal relations from data rather than knowledge of mechanisms are most helpful and exploited only if the theoretical background is insufficient or experimentation impossible. The review of literature shows that when an investigator has no prior knowledge of the researched phenomenon, no result of the Grangercausality test has any epistemic utility due to different possible interpretations. (1) Rejecting the null in one of the tests can be interpreted as either a true causal relation, opposite direction of the true causation, instant causality, time series cointegration, not frequent enough sampling, etc. (2) Bi-directional Granger causality can be read either as instant causality or common cause fallacy. (3) Non-rejection of both nulls possibly means either indirect or nonlinear causality, or no causal relation.

Purpose
This paper aims to analyze forecasting problems from the perspective of information extraction. Circumstances are studied under which the forecast of an economic variable from one domain (country, industry, market segment) should rely on information regarding the same type of variable from another domain even if the two variables are not causally linked. It is shown that Granger causality linking variables from different domains is the rule and should be exploited for forecasting.


Design/methodology/approach
This paper applies information economics, in particular the study of rational information extraction, to shed light on the debate on causality and forecasting.


Findings
It is shown that the rational generalization of information across domains can lead to effects that are hard to square with economic intuition but worth considering for forecasting. Information from one domain is shown to affect that from another domain if there is at least one common factor affecting both domains, which is not (or not yet) observed when a forecast has to be made. The analysis suggests the theoretical possibility that the direction of such effects across domains can be counter-intuitive. In time-series econometrics, such effects will show up in estimated coefficients with the "wrong" sign.


Practical implications
This study helps forecasters by indicating a wider set of variables relevant for prediction. The analysis offers a theoretical basis for using lagged values from the type of variable to be forecast but from another domain. For example, when forecasting the bond risk spread in one country, introducing in the time-series model the lagged value of the risk spread from another country is suggested. Two empirical examples illustrate this principle for specifying models for prediction. The application to risk spreads and inflation rates illustrates the principles of the approach suggested here which is widely applicable.


Originality/value
The present study builds on a probability theoretic analysis to inform the specification of time-series forecasting models.

ABSTRACT: Countries devote a lot of time and effort to develop economically, but they nevertheless struggle to attain that growth consistently. Research into economic growth should have provided some guidance as to which actions these countries should adopt, but multiple studies using different methods and datasets generally have led to an array of outcomes that contradict one another. However, Granger causality analysis can provide better insight into the relationship between some key variables and economic growth to give these countries more direction. Granger causality results establish a stronger connection between variables, even though they do not depict true causation. We therefore use a Vector Auto Regression method with Generalized Method of Moments estimation and then Granger-causality to discover how commonly used variables in the literature individually impact economic growth and in what way for a selection of OECD and non-OECD countries. We choose government expenditure, foreign direct investment (FDI), education (as a proxy for human capital), population growth, urbanization, and trade openness as our variables of interest. We also utilize updated datasets from the World Development Indicators (WDI) ranging from the years 2000-2016, based on the availability of the data. We find that FDI and population growth do Granger-cause economic development for OECD countries but that government expenditures, education, urbanization, and trade openness do not show such Granger causality. However economic growth Granger-causes government expenditures, FDI, and education for these OECD countries. For the non-OECD countries, we find that FDI, population growth, and trade openness Granger-cause growth but Granger causality does not occur from government expenditure, education, and urbanization to economic development. Yet similar to the OECD countries, economic growth does Granger-cause FDI and trade openness for the non-OECD countries as well. The overall results indicate that all countries should encourage the expansion of foreign direct investment to grow their economy, which then in turn can lead to additional investment as well as more resources available for the country as a whole. We also suggest further studies should focus on the economic development of individual countries rather than regions, particularly in terms of how technology can drive growth and impact these other variables.

The relationship between military spending and economic inequality is not well documented within the empirical literature, while numerous studies have uncovered the linkages between military spending and other macroeconomic variables, such as economic growth, unemployment, purchasing power parity, black market premium, poverty and investment. The purpose of this article is to examine the causal relationship between military spending and inequality using BVC and SIPRI data across 58 countries from 1987 to 1999. Panel unit root tests indicate that two inequality measures (Theil and EHII) under consideration are likely to be non-stationary. The authors' work addresses the adverse implications of modeling with non-stationary variables, since this omission casts serious doubt on the reliability of the relationship between military spending and inequality. The recently developed panel Granger non-causality tests provide no evidence to support the causal relationship in either direction between the military spending and the change in economic inequality. The results are consistently robust to alternative data sources for military spending, to alternative definitions of the inequality measures, to the log transformation of the military spending, to the deletion of some data points, and to the division of OECD and non-OECD countries. Finally, the impulse responses and variance decompositions based on the panel vector autoregressive regression model are consistent with the findings from Granger non-causality tests.