Monthly price elasticity and possibility of using daily values I am calculating the price elasticity as a starting point to find a theoretical optimal price that would maximize our revenue.
I am looking at 2 years data and to use the price elasticity formula, I am considering monthly values separately (this also because given the strong seasonality we have, i would expect different elasticity per month and I would like to suggest different prices every month).   

To get the monthly Price Elasticity, I calculate for each month Prices and Quantities of the respective year: 

$$
\frac{(Q^{2018} - Q^{2017})/Q^{2017}}{(P^{2018} - P^{2017})/P^{2017}}
$$

Question 1: Is this correct? Using aggregated monthly data is enough? What if I had also 2016 data? How could I put this extra information in the formula? 

Question 2: Could I use daily data to calculate the price elasticity? Could I plot 2 years data and use the linear trend line for the elasticity calculation? 

Thanks in advance, any help is highly appreciated!
What you're trying to approximate is the quantity 

$$
\gamma(t) = \frac{{\rm d}\ln Q(t)}{{\rm d}\ln P(t)}
$$

$\gamma = \gamma(t)$ is a function of time, the better sampling you have of $P$ and $Q$ the better estimation of $\gamma$ you are going to get. Imagine you have one sample every $h$ units of time. From what I can read $h  = 1 ~{\rm month}$, what you can do to measure $\gamma$ at time $t_k$ is calculate

$$
\frac{{\rm d}\ln P(t_k)}{{\rm d}t} \approx \frac{\ln P(t_k) - \ln P(t_k - h)}{h} \tag{1}
$$

with a similar expression for $Q$. But this is really bad in general, here's a better approach

$$
\frac{{\rm d}\ln P(t_k)}{{\rm d}t} \approx \frac{-2\ln P(t_k + 2h) + 8\ln P(t_k + h) - 8\ln P(t_k - h) + \ln P(t_k - 2h)}{12h} \tag{2}
$$

So to give you an idea, if you are measuring the elasticity on ($t_k$ = November 2018) with data measured each month, you will need to know the prices and quantities for ($t_k - 2h = $ September 2018), ($t_k - h = $ October 2018), ($t_k + h = $ December 2018) and ($t_k + 2h = $ January 2019).