Explanation

The correct answer is D because in-sample forecasting ability is indeed a very poor test of model appropriateness and adequacy.

Why D is Correct:

• In-sample forecasting refers to using the same dataset to both develop and test a forecasting model
• This approach leads to overly optimistic results because the model is essentially 'cheating' by using knowledge of the data it's supposed to predict
• It provides a poor indication of how well the model will perform on new, unseen data (out-of-sample data)
• This underscores the importance of using separate datasets for model development and testing

Why Other Options are Incorrect:

A is incorrect - Forecasts are not only possible with time-series data. Cross-sectional and panel data can also be used for forecasting purposes.

B is incorrect - Forecasts do not always improve with more parameters. Adding too many parameters can lead to overfitting, where the model fits the sample data well but performs poorly on new data.

C is incorrect - As the number of variables in a regression equation increases, the risk of over-fitting actually increases, not reduces. This contradicts Occam's razor principle that simpler models are preferable when they explain the same variance.

Key Takeaway:

Proper model validation requires testing on out-of-sample data to ensure the model's predictive power generalizes beyond the training dataset.