A comparison of two business cycle dating methods

We study the suggestion that Markov switching (MS) models should be used to determine cyclical turning points. A Kalman filter approximation is used to derive the dating rules implicit in such models. We compare these with dating rules in an algorithm that provides a good approximation to the chronology determined by the NBER. We find that there is very little that is attractive in the MS approach when compared with this algorithm. The most important difference relates to robustness. The MS approach depends on the validity of that statistical model. Our approach is valid in a wider range of circumstances.

Schreider, Judith, interviewed by Desiree Shayer on January 21, 2010: Audio Recording

Some themes discussed are: • Colby—admissions (2-3) • Colby—dorms (3-4) • Colby—social life (4) • Marriage (4) • Colby—professors (7) • Colby—Dean Runnals (12) • Food—kosher (5) • Dating—rules at Colby (4-5, 6) • Dating—townies (6) • Military service—(8) • Occupation—furniture (8) • Occupation—education (10)

Schreider, Judith, interviewed by Desiree Shayer on January 21, 2010: Audio Part Two

Some themes discussed are: • Colby—admissions (2-3) • Colby—dorms (3-4) • Colby—social life (4) • Marriage (4) • Colby—professors (7) • Colby—Dean Runnals (12) • Food—kosher (5) • Dating—rules at Colby (4-5, 6) • Dating—townies (6) • Military service—(8) • Occupation—furniture (8) • Occupation—education (10)

Schreider, Judith, interviewed by Desiree Shayer on January 21, 2010: Transcript

Some themes discussed are: • Colby—admissions (2-3) • Colby—dorms (3-4) • Colby—social life (4) • Marriage (4) • Colby—professors (7) • Colby—Dean Runnals (12) • Food—kosher (5) • Dating—rules at Colby (4-5, 6) • Dating—townies (6) • Military service—(8) • Occupation—furniture (8) • Occupation—education (10)

A Reliable Basis for Approximate Association Rules

For most of the work done in developing association rule mining, the primary focus has been on the efficiency of the approach and to a lesser extent the quality of the derived rules has been emphasized. Often for a dataset, a huge number of rules can be derived, but many of them can be redundant to other rules and thus are useless in practice. The extremely large number of rules makes it difficult for the end users to comprehend and therefore effectively use the discovered rules and thus significantly reduces the effectiveness of rule mining algorithms. If the extracted knowledge can’t be effectively used in solving real world problems, the effort of extracting the knowledge is worth little. This is a serious problem but not yet solved satisfactorily. In this paper, we propose a concise representation called Reliable Approximate basis for representing non-redundant approximate association rules. We prove that the redundancy elimination based on the proposed basis does not reduce the belief to the extracted rules. We also prove that all approximate association rules can be deduced from the Reliable Approximate basis. Therefore the basis is a lossless representation of approximate association rules.