Out-of-Sample Forecast Performance as a Test for Nonlinearity in Time Series

This article uses a local-information, near-neighbor forecasting methodology as a prediction test for evidence of a noisy, chaotic data-generating process underlying the Divisia monetary-aggregate series. Using a nonparametric method known to perform well with low-dimensional chaotic processes infected by noise, accompanied by a robust test of forecast performance evaluation, we compare out-of-sample forecasting accuracy from the local-information method to forecasting accuracy from the best fitting global linear model. Our results fail to substantiate previous claims for determinism in the Divisia monetary-aggregate series because the degree of forecast improvement obtained by the local-information method is not consistent with the hypothesis of a low-dimensional attractor underlying the Divisia data.     

Directed Matching and Monetary Exchange

We develop a model of monetary exchange where, as in the random matching literature, agents trade bilaterally and not through centralized markets. Rather than assuming they match exogenously and at random, however, we determine who meets whom as part of the equilibrium. We show how to formalize this process of directed matching in dynamic models with double coincidence problems, and present several examples and applications that illustrate how the approach can be used in monetary theory. Some of our results are similar to those in the random matching literature; others differ significantly.