Explosive Roots in Level Vector Autoregressive Models and Vector Error Correction Models

Qureshi, Hammad

doi:10.2139/ssrn.2652306

Cited by 1 publication

(1 citation statement)

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“…26 For ten of these cases, the explosive root is in IMR, whereas it is in GDP for the remaining three (India, Indonesia and Paraguay). This may be from the bias associated with least squares in small samples (e.g., Andrews andChen 1994, Qureshi 2008); e.g., Qureshi's (2008) simulations show that least squares can have a high frequency of estimated explosive roots even when the variables are indeed stationary.…”

Section: Core Empirical Granger-causality Resultsmentioning

confidence: 99%

Health and wealth: Short panel Granger causality tests for developing countries

Chen

Clarke

Roy

2013

The Journal of International Trade & Economic Development

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The world has experienced impressive improvements in wealth and health, with, for instance, the world's real GDP per capita having increased by 180% from 1970 to 2007 accompanied by a 50% decline in infant mortality rate. Healthier and wealthier. Are health gains arising from wealth growth? Or, has a healthier population enabled substantial growth in wealth? The answers to these questions have serious policy implications. We contribute to understanding dynamic links between wealth and health by analyzing the relationship between health (as measured by infant mortality rate) and wealth (as measured by GDP per capita) for a panel of 58 developing countries using quinquennial data covering the period 1960 through 2005. We examine for causal rather than associative links between these fundamental macro measures of economic development. The panel enables us to examine for causal links using several methods that differ in how cross-country and temporal heterogeneity is imposed: cross-country homogeneity with temporal heterogeneity and cross-country heterogeneity with temporal homogeneity. Under the latter case, we consider sensitivity to assuming fixed versus random causal coefficients. In addition, we explore robustness of outcomes to level of economic development (as measured by national income) and inclusion of another covariate (education). . Key from these microeconometric applications is that health and wealth clearly affect each other, with some demonstrating how improved health can raise education level, adult labour quantity, and labour productivity, so influencing an individual's wealth (income) via auxiliary factors.However, usually due to the nature of the datasets, most studies explore for associative, rather than causal, relationships between health and wealth; Adams et al. (2003) and Michaud and van Soest (2008) are exceptions, examining for direct causal links between health and wealth, 3 analogous to the notion of Granger (1969) causality in macroeconomics -we denote this as G-causality and G-noncausality.A large literature also uses cross-country data to study the aggregate health-wealth relationship; e.g.

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Section: Core Empirical Granger-causality Resultsmentioning

confidence: 99%