Question regarding estimation of signal tone parameters (frequency, amplitude, and phase) using Macleods algorithm
I'm not sure about Macleod, but generally windowing is not good for that style of estimator. See this paper for details about similar estimators.

Generally the more data, the better for all parameters. Provided you use an estimator without bias, it should do better with more data.

It depends on what  you mean by "resolution".  

If you mean the standard deviation of the estimate, then that will be governed (at higher SNRs) by the Cramer-Rao lower bound for frequency estimate $\hat{f}_0$, which is:
$$
{\rm var}(\hat{f}_0) \ge \frac{12}{(2\pi)^2 \eta N(N^2-1)}
$$
where the signal model is
$$
x[n] = A \cos(2\pi f_0 n + \phi) + w[n]
$$
the amplitude is $A$ and phase $\phi$ (which are also both unknown), the number of samples is $N$, $\eta = A^2/(2\sigma^2)$ is the SNR and $w[n]$ is white, Gaussian noise of variance $\sigma^2$.

See equations (3.41) of Kay's Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory.