The analyzed results indicated that a weak positive 0.233 but highly significant (0.000) correlation existed between the variables. The data for monthly average temperature ☌ and precipitation (mm) were collected. Copulas are powerful tools to model the joint distribution of two or more variables simultaneously by preserving their dependence structure. In this study, copula analysis was used to eventually fill the void of the negligence of interdependence of two climate variables (temperature and precipitation). Finally, the effects of temperature and precipitation on a single aggregate measure, agriculture value addition, are separately investigated. Third, it extends previous studies by involving agriculture value addition. First, empirical settings introduce an innovative geographical instrument, Second, it includes a wider set of control variables in the analysis. This paper contributes to the growing literature in at least four aspects. In designing effective policies and strategies, policymakers should focus not only on crop production but also on other agricultural activities such as livestock production and fisheries, in addition to national and international socio-economic and geopolitical dynamics. The study provides strong implications for policymakers to confront climate change, which is an impending danger to agriculture. Surprisingly, the magnitude of the coefficient on temperature is mild, at least 20 per cent, as compared to previous studies, which may be because of the use of the instrumental variable (IV), which is also supported by an alternative robust measure when estimated across different regions. The study finds a negative relationship between temperature and agriculture. The inclusion of some control variables is supposed to reduce the omitted variable bias. The identification and F -statistic tests are used to choose and exclude the instrument. This study introduces a geographical instrument, longitude and latitude, for temperature to assess the impact of climate change on agriculture by estimating regression using IV-two-stage least squares method over annual panel data for 60 countries for the period of 1999-2011. This paper aims to investigate the relationships between climate change and agriculture and test the potential reverse causality and endogeneity of climatic variables to agriculture. The empirical literature on climate change and agriculture does not adequately address the issue of potential endogeneity between climatic variables and agriculture, which makes their estimates unreliable.
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