tag:blogger.com,1999:blog-19649274.post4782898297889973027..comments2021-05-07T12:19:02.719+05:30Comments on The Leap Blog: The Diwali effect in Delhi air qualityAjay Shahhttp://www.blogger.com/profile/03835842741008200034noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-19649274.post-73770325342429648592016-10-31T23:29:08.102+05:302016-10-31T23:29:08.102+05:30The regression framework by its design has the pot...The regression framework by its design has the potential to produce positive and high betas for days after Diwali due to following two Modeling issues<br /><br />1) climate heteogenety in days after Non Diwali;<br />The regression sample appears to have chosen non Diwali days from entire year ranging from summer to winter. In summer days, PM 25 would surely not have increasing trend similar to winter month. the proportion of summer non Diwali days would anyway be a high proportion of total non Diwali days in the regression model<br /><br />2) homogeneity of Diwali days: <br />the Diwali days within sample would be obviously taken from early winter days and hence pm 25 is anyway expected to increase after Diwali days. Hence positive and high betas for Diwali is somewhat obvious<br /><br />In order to remove the above conclusion bias, a narrowed time framework of the sample could be helpful.<br /><br />In general terms, Diwali anyway appears as negligible culprit given the recent days news when PM 25 had peaked even before Diwali due reasons like crop burning and cold.<br /><br />Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-19649274.post-28066239670056072392016-10-29T20:42:19.638+05:302016-10-29T20:42:19.638+05:301) What is the alpha for these three models:
It i...1) What is the alpha for these three models:<br /><br />It is some time possible that due to high/low alpha in model 2 and 3, the betas for location*diwali will be impacted to maintain the location*diwali averages. Hence the relative increase (w.r.t anand vihar) may appear small but when added with alpha, the final output may increase. <br /><br />2) Use of shifting diwali dates to mitigate cofounding: <br /><br />The primary objective of the analysis is obviously not to predict PM25 after diwali but to the asses the relative change in PM 25 due to diwali. The regression analysis do not explicitly tries to mitigate the mentioned cofounding effect in the data which says that PM 25 will anyway increase due to decreasing temperature after diwali.<br /><br />An easy way to mitigate the cofounding effect is to compare the volatilities of the diwali month with volatility of the same calendar month but in a year in which there was no diwali in the particular month. For example, diwali (2014) was on October 22. In 2015, diwali was on November 11. Hence, if diwali has an impat upon PM 25, volatiliies in November 14 will be lower than volatilities in november 15. The same interpretation will hold for october but in a different way.Anonymousnoreply@blogger.com