Data from Constructing Historical Euro-Zone Data, Economic Journal, 2001, 111:F102-F121. Quarterly, 1979q1 to 1999q4.

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...

Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a...

The new-Keynesian Phillips curve (NKPC) includes expected future inflation to explain current inflation. Such models are estimated by replacing the expected value by the future outcome, using Instrumental Variables or Generalized Method of Momentsmethods. However, the underlying theory does not...

This paper reviews the need for powerful facilities in econometrics, focusing on concrete problems which arise in financial economics and in macroeconomics. We argue that the profession is being held back by the lack of easy to use generic software which is able to exploit the availability of...

Parallel computation has a long history in econometric computing, but is not at all wide spread. We believe that a major impediment is the labour cost of coding for parallel architectures. Moreover, programs for specific hardware often become obsolete quite quickly. Our approach is to take a...

Many economic models (such as the new-Keynesian Phillips curve, NKPC) include expected future values, often estimated after replacing the expected value by the actual future outcome, using Instrumental Variables or Generalized Method of Moments. Although crises, breaks and regime shifts are...

High dimensional general unrestricted models (GUMs) may include important individual determinants, many small relevant effects, and irrelevant variables. Automatic model selection procedures can handle more candidate variables than observations, allowing substantial dimension reduction from GUMs...

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...