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I've been going back and forth with how I should work to find an event effect. would be so grateful for some clarification.

I have daily time series of exchange rates for different countries ( 1 for each country). the period is from jan1, 2018 to dec 31, 2019. I want to know if a certain event that had multiple announcements during this period had an effect on each of the countries. ( I am taking the news that happened during this period 65 in total) and I want to analyse if the event had an effect on each country's exchange rate. this is the market model and my variables would be ER= (dependent)exchange rate, D= dummy, SP= stock market return

this is the market model, SP is the stock market return

my question is, should i have 1 dummy variable for the whole regression with all 1 when a news happen and zeros when it did not and analyse that, as in my data frame would look like

all obsv have either a 1 or a zero

and so on for 546 observations( business days) which contains 65 dummys. so total would be 481 observations with 0 and 65observations with a 1 during this period of jan1, 2018 to dec312019 when there was a news announcement.

I should then just run an OLS regression of the market model and the dummy=1 would tell me the effect of the event on the FX.

or should I have had to reduce the time window, to, for example, 6 months and analysed each news announcement separately, of course now focusing on less news announcement ( 1 every 6 months), and adding a dummy=1 to days around the event date (-1,+1). so in a 6months daily observations, i would only have 3 dummys=1 everything else would be zero for example:

smaller event window

or am i completely wrong with my both approaches?

thank you so much!

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    $\begingroup$ I’m voting to close this question because it is not a quant finance question but a stat/econometric one $\endgroup$ – lehalle Jun 2 '20 at 5:58

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