by Richard Hardy
Last Updated October 19, 2019 12:19 PM - source

Suppose one estimates a linear time series model $$ y_t=\beta_0+\beta_1 x_{t-1}+\varepsilon_t $$ and finds that $\hat\beta_1>0$ and the $p$-value associated with $\hat\beta_1$ is lower than the chosen significance level. Can one say, without any caveats, that

$x$

statistically significantly predicts$y$?

Similarly, can one say

$x$

positively predicts$y$?

My **main concern** is that the claim about prediction is based on in-sample analysis without stating the implicit assumptions that are required to make a conclusion about out-of-sample results from in-sample results. Another, **minor concern** is that the claim is based on a significance test of $\beta_1$ rather than a measure of change in prediction errors of $y$ when $x_1$ is added to the model. However, the latter is probably not a problem as the significance of $\beta_1$ probably implies the prediction errors of $y$ will be decreased by use of $x_1$ in the model.

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