deletions | additions       diff --git "a/Paper Replication for \"The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Reply\".tex" "b/Paper Replication for \"The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Reply\".tex"  index e3b3bd4..b40565b 100644  --- "a/Paper Replication for \"The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Reply\".tex"  +++ "b/Paper Replication for \"The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Reply\".tex" 
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One argument made by DG2012 is that "\textit{allowing for local shocks tends to reduce the magnitude of the predicted loss, although this does \textbf{NOT} always come at the expense of reduced statistical precision}" observing that the estimated standard error clustered by state is 3.03 for year fixed effect (column 1a) and 2.29 for USDA region*year fixed effects. Personally I think this argument is tricky from two perspectives: 1) it is the only observation supporting this argument, and 2) we are observing a larger change in the estimated mean, so it is not the case in the coefficient of variation perspective.  \subsubsection{Storage issue}  DG2007 calculated the agricultural profit by subtracting sales revenue by the production cost. Storage was concern since farmers might store their products in good weather years for future sales and do the contrary in bad weather years. As a result, sales did not reveal only current year weather information and would bias our estimation towards zero.  DG2012 address this issue by introducing the lag-one weather variables in the estimation.