I performed a simple linear regression in Stata and in Excel by relating the number of hotel arrivals for entire Switzerland booked by German travelers (Logiernächte or coded as CH_AN_SUM) to the exchange rate of EUR and CHF (coded FX) for 165 months.


Stata:

regress CH_AN_SUM FX

Source | SS df MS Number of obs = 164
-------------+---------------------------------- F(1, 162) = 18.43
Model | 3.1587e+11 1 3.1587e+11 Prob > F = 0.0000
Residual | 2.7761e+12 162 1.7136e+10 R-squared = 0.1022
-------------+---------------------------------- Adj R-squared = 0.0966
Total | 3.0919e+12 163 1.8969e+10 Root MSE = 1.3e+05

------------------------------------------------------------------------------
CH_AN_SUM | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
FX | -217368.3 50629.01 -4.29 0.000 -317346.2 -117390.4
_cons | 933585 67871.59 13.76 0.000 799557.9 1067612

------------------------------------------------------------------------------


Excel:





Why is there such large difference in the coefficients and in R2?

Guess something went wrong, I copied the data from Stata to check any differences in Excel but found nothing. Excel ANOVA also says that there are 165 observations (which is true) but Stata says 164 (However, should not be material)


What I actually need to calculate from that is the price elasticity. It has yet been found somewhere between 0.x to 2.x or something (if FX rate goes up - more expensive - less demand).

In the literature the relationship is usually made in a log log regression model but that did absolutely not work (no results). Maybe this is useful when I want to consider potential differences of regions (cities vs. tourist regions)?

Any help highly appreciated.