"Prediction interval" for a slope of a model fitted to new data Problem statement:
I am using a simple linear regression with outcome $y$ and single predictor $x$ of the form:
$$
y_i \sim N(\mu_i, \sigma) \\
\mu_i = \beta_0 + \beta_1x_i
$$
I assume fitting $\beta_0, \beta_1$ with maximum likelihoood (although if other method works better for the purpose of this question, I can use that). Then, I observe a new set of outcomes $\bar{y}$ for a (known) new set of predictors $\bar{x}$ which I assume come from the same process. I then assume a regression is fitted independently to $\bar{x}, \bar{y}$ obtaining $\bar{\beta}_0, \bar{\beta}_1$.
Is there an analytical way to use the original fit to get something like a "prediction interval" for $\bar\beta_1$? I am looking for an interval with the expected coverage properties for $\bar\beta_1$ - the estimate of the slope from $\bar{x}, \bar{y}$. I assume this is going to be noticeably wider than the confidence interval as there's the additional uncertainty arising from sampling $\bar{y}$ and constructing the estimate based on those samples.
I could obviously use simulations to obtain that interval, but I am curious if there is a more elegant way.
Background:
I am attempting to do something akin to cross validation for testing the extent that each of a set of possible summaries is "reproducible" - having $N$ replicates, intuitively, a summary is good, if summarising $N - K$ replicates of the experiment lets me predict the summary the remaining $K$ replicates well. One of the summaries I am experimenting with is a slope from a regression model, so I'd like to develop the expected prediction interval if the model was actually capturing the data well.
Code example:
The following code shows the problem and solution with simulations in R.
In each simulation, I am trying to use the linear fit to $x,y$ to construct an interval that would contain $\bar{\beta}_1$ in 95% of cases. I use the confidence interval and then I use simulations to construct a "prediction" interval.
x <- rep(0:5, times = 4)
xbar <- rep(0:5, times = 2)

N_sims <- 100

#Pre-allocate
y <- matrix(NA_real_, nrow = N_sims, ncol = length(x))
ybar <- matrix(NA_real_, nrow = N_sims, ncol = length(xbar))
b1 <- numeric(N_sims)
b1ci <- matrix(NA_real_, nrow = N_sims, ncol = 2)
b1bar <- numeric(N_sims)

b1pred_sim <- matrix(NA_real_, nrow = N_sims, ncol = 2)

for(i in 1:N_sims) {
  # Choose the true values randomly
  # The results are quite similar whether I choose
  # those separately for each simulation or 
  # once at the begining of the script
  true_b0 <- 1 + rnorm(1)
  true_b1 <- 0.5 + rnorm(1)
  true_sigma <- 1 + rlnorm(1)

  
  y_ <- rnorm(length(x), true_b0 + x * true_b1, sd = true_sigma)
  y[i,] <- y_
  fit <- lm(y_ ~ x)
  b1[i] <- coef(fit)["x"]
  b1ci[i,] <- confint(fit)["x",]
  
  ## Calculate the prediction interval with simulations
  N_inner_sims <- 1000
  sims_b1bar <- numeric(N_inner_sims)
  for(j in 1:N_inner_sims) {
    coef_sim_norm <- mvtnorm::rmvnorm(1, coef(fit), vcov(fit))
    # Transform to T distribution
    dof <- length(x) - 2
    coef_sim <- coef_sim_norm / sqrt(rchisq(2, dof) / dof)
    
    ysim <- rnorm(length(xbar), coef_sim[1] + xbar * coef_sim[2], sd = sigma(fit))
    fitsim <- lm(ysim ~ xbar)
    sims_b1bar[j] <- coef(fitsim)["xbar"]
  }
  b1pred_sim[i,] <- quantile(sims_b1bar, c(0.025,0.975))
  
  ## Actually fit the new data
  ybar_ <- rnorm(length(xbar), true_b0 + xbar * true_b1, sd = true_sigma)
  ybar[i,] <- ybar_
  fitbar <- lm(ybar_ ~ xbar)
  b1bar[i] <- coef(fitbar)["xbar"]
}

cat("Coverage CI: ", mean(b1bar >= b1ci[,1] & b1bar <= b1ci[,2]))
cat("Coverage prediction - sims: ", 
  mean(b1bar >= b1pred_sim[,1] & b1bar <= b1pred_sim[,2]))

Due to the simulations this is quite slow, but a typical result is something like:
Coverage CI:  0.81
Coverage prediction - sims:  0.96

so CI is too narrow while the simulations appear (at least approxiamtely) well calibrated. So the question is if I can get something like b1pred_sim analytically. Note that b1pred_sim is just appropriately widened CI:

 A: Thanks to Jarle Tufto who provided an outline of the main idea in the comments. All errors are my own.
We can realize that if $\sigma$ was known, the distribution of the MLE of the slope is $\frac{\sigma^2}{\sum (x_i - m)^2}$ where $m$ is the observed mean of $x$ (taken from Wiki )
This means that the difference of the two MLEs is also going to be normally distributed. We however do not know $\sigma$, and plugging in the estimate of sigma from the first part, the resulting quantity is $t$-distributed with $n−2$ degrees of freedom, where $n$ is the sample size of the observed data.
So the prediction interval will be derived from t distribution centered at the MLE from the first batch of data and with standard error of $\sqrt{s^2 + \frac{\hat{\sigma}^2}{\sum (\bar{x}_i - \bar{m})^2}}$ where $\hat\sigma$ is the estimate of $\sigma$ from the first regression, $s$ is the standard error from the first regression and $\bar{m}$ is the mean of $\bar{x}$.
Adapting the code example from the question:


x <- rep(0:5, times = 4)
xbar <- rep(0:5, times = 2)

N_sims <- 10000

#Pre-allocate
y <- matrix(NA_real_, nrow = N_sims, ncol = length(x))
ybar <- matrix(NA_real_, nrow = N_sims, ncol = length(xbar))
b1 <- numeric(N_sims)
b1ci <- matrix(NA_real_, nrow = N_sims, ncol = 2)
b1bar <- numeric(N_sims)

b1pred <- matrix(NA_real_, nrow = N_sims, ncol = 2)

for(i in 1:N_sims) {
  # Choose the true values randomly
  # The results are quite similar whether I choose
  # those separately for each simulation or
  # once at the begining of the script
  true_b0 <- 1 + rnorm(1)
  true_b1 <- 0.5 + rnorm(1)
  true_sigma <- 1 + rlnorm(1)


  y_ <- rnorm(length(x), true_b0 + x * true_b1, sd = true_sigma)
  y[i,] <- y_
  fit <- lm(y_ ~ x)
  b1[i] <- coef(fit)["x"]
  b1ci[i,] <- confint(fit)["x",]

  # Calculate the prediction interval for b1bar
  b1bar_var <- sigma(fit)^2 / sum((xbar - mean(xbar))^2)
  b1_var <- vcov(fit)["x", "x"]
  diff_se <- sqrt(b1bar_var + b1_var)
  b1pred[i, ] <- b1[i] + qt(c(0.025,0.975), df = length(x) - 2) * diff_se

  ## Actually fit the new data
  ybar_ <- rnorm(length(xbar), true_b0 + xbar * true_b1, sd = true_sigma)
  ybar[i,] <- ybar_
  fitbar <- lm(ybar_ ~ xbar)
  b1bar[i] <- coef(fitbar)["xbar"]
}

cat("Coverage prediction: ", mean(b1bar >= b1pred[,1] & b1bar <= b1pred[,2]))

Shows the expected coverage behaviour.
