Bridge the gap between network-based inference method and global ranking method in personal recommendation

In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the global ranking method is the limit of applying network-based inference method with infinity times.Comment: 13 pages, 3 figure

Expectation Propagation on the Maximum of Correlated Normal Variables

Many inference problems involving questions of optimality ask for the maximum or the minimum of a finite set of unknown quantities. This technical report derives the first two posterior moments of the maximum of two correlated Gaussian variables and the first two posterior moments of the two generating variables (corresponding to Gaussian approximations minimizing relative entropy). It is shown how this can be used to build a heuristic approximation to the maximum relationship over a finite set of Gaussian variables, allowing approximate inference by Expectation Propagation on such quantities.Comment: 11 pages, 7 figure

Inflation, Exchange Rates and PPP in a Multivariate Panel Cointegration Model

New multivariate panel cointegration methods are used to analyze nominal exchange rates and prices in the four major economic powers in Europe, France, Germany, Italy and Great Britain for the post- Bretton Woods period. We test for PPP and find that the theoretical PPP relationship does not hold but there is a similar (1,-1.5,0.9 instead of 1,-1,1) relationship which is common for the investigated countries. Parametric bootstrap inference is used to deal with badly small sample sized tests.Long-run purchasing power parity, multivariate cointegration analysis, bootstrap inference.

Does the IV estimator establish causality? Re-examining Chinese fertility-growth relationship

The instrumental variable (IV) estimator in a cross-sectional or panel regression model is often taken to provide valid causal inference from contemporaneous correlations. In this exercise we point out that the IV estimator, like the OLS estimator, cannot be used effectively for causal inference without the aid of non-sample information. We present three possible cases (lack of identification, accounting identities, and temporal aggregation) where IV estimates could lead to misleading causal inference. In other words, a non-zero IV estimate does not necessarily indicate a causal effect nor does the causal direction. In this light, we re-examine the relationship between Chinese provincial birth rates and economic growth. This exercise highlights the potential pitfalls of using too much temporal averaging to compile the data for cross sectional and panel regressions and the importance of estimating both (x on y and y on x) regressions to avoid misleading causal inferences. The GMM-SYS results from dynamic panel regressions based on five-year averages show a strong negative relationship running both ways, from births to growth and growth to births. This outcome, however, changes to a more meaningful one-way relationship from births to growth if the panel analysis is carried out with the annual data. Although falling birth rates in China have enhanced the country’s growth performance, it is difficult to attribute this effect solely to the one-child policy implemented after 1978.IV estimator and causality inference, identification, accounting identities, temporal aggregation, spurious causality, Chinese provincial growth and fertility relationship.