Encompassing Tests When No Model Is Encompassing

This paper considers regression-based tests for encompassing, when none of the models under consideration encompasses all the other models. For both in- and out-of-sample applications, I derive asymptotic distributions and propose feasible procedures to construct confidence intervals and test statistics. Procedures that are asymptotically valid under the null of encompassing (e.g., Davidson and MacKinnon (1981)) can have large asymptotic and finite sample distortions. Simulations indicate that the proposed procedures can work well in samples of size typically available, though the divergence between actual and nominal confidence interval coverage sometimes is large.

Diffusion index-based inflation forecasts for the euro area

Diffusion indexes based on dynamic factors have recently been advocated by Stock and Watson (1998), and further used to perform forecasting tests by the same authors on US data. This technique is explored for the euro area using a multi-country data set and a broad array of variables, in order to test the inflation forecasting performance of extracted factors at the aggregate euro area level. First, a description of factors extracted from different data sets is performed using a number of different approaches. Conclusions reached are that nominal phenomena in the original variables might be well captured in-sample using the factor approach. Out-of-sample tests have more ambiguous interpretation, as factors seem to be good leading indicators of inflation, but the comparative advantage of the factors is less clear. Nevertheless, alternative indicators such as unemployment or money growth do not outperform them JEL Classification: C53, E31, E37

A method for inferring hierarchical dynamics in stochastic processes

Complex systems may often be characterized by their hierarchical dynamics. In this paper do we present a method and an operational algorithm that automatically infer this property in a broad range of systems; discrete stochastic processes. The main idea is to systematically explore the set of projections from the state space of a process to smaller state spaces, and to determine which of the projections that impose Markovian dynamics on the coarser level. These projections, which we call Markov projections, then constitute the hierarchical dynamics of the system. The algorithm operates on time series or other statistics, so a priori knowledge of the intrinsic workings of a system is not required in order to determine its hierarchical dynamics. We illustrate the method by applying it to two simple processes; a finite state automaton and an iterated map.Comment: 16 pages, 12 figure

Learning Timbre Analogies from Unlabelled Data by Multivariate Tree Regression

This is the Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the Journal of New Music Research, November 2011, copyright Taylor & Francis. The published article is available online at http://www.tandfonline.com/10.1080/09298215.2011.596938