We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either...

To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a...

Chapter 1: Introduction -- Chapter 2: Key Concepts: A Series of Primers -- Chapter 3: Why is the World Always Changing? -- Chapter 4: Making Trends and Breaks Work for us -- Chapter 5: Indicator Saturation Methods -- Chapter 6: Combining Theory and Data -- Chapter 7: Seeing into the Future --...