Forecasting with no prior knowledge - Bayesian vs Frequentist I have a basic question about Bayesian statistics. 
Lets say that I want to make forecasts of a certain response variable, based on explanatory variables and lagged responses variables, while I have no knowledge apart from the data. This means that under a Bayesian approach, I would specify a diffuse prior to obtain the posterior distribution of my parameters.
In this topic (Why I should use Bayesian inference with uninformative prior?) I read the following sentence: ''if you are interested only in point estimates, then it (frequentist and bayesian) is basically the same''.
I would now like to ask: Does forecasting in a 'Bayesian way', when using a diffuse prior, has any advantages over just using MLE to obtain estimates for your parameters and making forecasts in 'frequentist way'?
I think that for obtaining a point estimate of your parameter, a Bayesian approach does not really have any advantages over a frequentist approach, but I was wondering if this also was the case from a forecasting perspective. 
 A: 
I think that for obtaining a point estimate of your parameter, a Bayesian approach does not really have any advantages over a frequentist approach

I would strongly oppose to that. Take the case of a coin flipped three times and we are looking for a point estimate of the probability $\theta_{head}$ of a "Heads" result. Let there be three flips with the result of 2 Heads and 1 Tails. A Frequentist approach with no prior information will result in a point estimate of $\theta_{heads} = 2/3$ - which in a case of coin flips is just nuts. No reasonable person would deduct from this experiment, that the coin will likely show heads in 2 out of three flips to come. Prior information is worth a lot when making point estimates in case of scarce data. Missing the chance of adding prior information is a big downside of Frequentist statistics. (Obviously, not having headaches to define a prior and not having to defend you choice of prior is a big advantage of Frequentism, but that'S not the point here.)

I think that for obtaining a point estimate of your parameter, a Bayesian approach does not really have any advantages over a frequentist approach, but I was wondering if this also was the case from a forecasting perspective

I think that this question is missing an important point. You should ask: "Is it reasonable, to do forecasting/prediction based on point estimates?" A Bayesian would strongly argue, that you should do prediction based on the whole posterior distribution of possible coefficients because forecasting/prediction based on point estimates disregards all information about how imprecise the point estimate is.
