Predict the first observations of a time series when order of the model is higher
The obvious answer is to use the last 4 data points of your training dataset. Note that there is no harm or bias in doing it. The purpose of breaking the data set into training and test datasets is to estimate and forecast on different datasets.
In your model, the dependent variable is $y_5$ to $y_{183}$. On the other hand, the four explanatory variables are: $y_1-y_{179}$; $y_2-y_{180}$; $y_3-y_{181}$; and $y_4-y_{182}$.
So the test data set for each of the explanatory variable actually starts from $y_{180}$, $y_{181}$, $y_{182}$ and $y_{183}$ respectively.
Therefore, by using the last four data points of your train dataset, you are not introducing any bias in terms of revalidating the model on fitted data.