Modelling Breaks and Clusters in the Steady States of Macroeconomic Variables

Macroeconomists working with multivariate models typically face uncertainty over which (if any) of their variables have long run steady states which are subject to breaks. Furthermore, the nature of the break process is often unknown. In this paper, we draw on methods from the Bayesian clustering literature to develop an econometric methodology which: i) finds groups of variables which have the same number of breaks; and ii) determines the nature of the break process within each group. We present an application involving a five-variate steady-state VAR.mixtures of normals, steady state VARs, Bayesian

Time Varying Dimension Models

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this paper proposes several Time Varying dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving US in?ation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.mixture model, model change, Bayesian

Oracle and Multiple Robustness Properties of Survey Calibration Estimator in Missing Response Problem

In the presence of missing response, reweighting the complete case subsample by the inverse of nonmissing probability is both intuitive and easy to implement. However, inverse probability weighting is not efficient in general and is not robust against misspecification of the missing probability model. Calibration was developed by survey statisticians for improving efficiency of inverse probability weighting estimators when population totals of auxiliary variables are known and when inclusion probability is known by design. In missing data problem we can calibrate auxiliary variables in the complete case subsample to the full sample. However, the inclusion probability is unknown in general and need to be estimated in missing data problems and it is unclear whether calibration is robust against misspecification of the missing probability model. It is also unclear how efficient calibration is for general missing data problem. This paper answers these two questions and presents two rather unexpected results. First, when the missing data probability is correctly specified and multiple working outcome regression models are posited, calibration enjoys an oracle property where the same semiparametric efficiency bound is attained as if the true outcome model is known in advance. Second, when the missing mechanism is misspecified, calibration can still be a consistent estimator when any one of the outcome regression model is correctly specified. This is a multiple robustness property more general than double robustness considered the missing data literature. We provide connections of a wide class of calibration estimator constructed based on generalized empirical likelihood to many existing estimators in biostatistics, econometrics and survey sampling and perform simulation studies to study the finite sample properties of calibration estimators

A New Model of Trend Inflation

This paper introduces a new model of trend (or underlying) inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. The bounds placed on trend inflation mean that standard econometric methods for estimating linear Gaussian state space models cannot be used and we develop a posterior simulation algorithm for estimating the bounded trend inflation model. In an empirical exercise with CPI inflation we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model.

Structure of cytochrome a3-Cua3 couple in cytochrome c oxidase as revealed by nitric oxide binding studies

The addition of NO to oxidized cytochrome c oxidase (ferrocytochrome c:oxygen oxidoreductase, EC 1.9.3.1) causes the appearance of a high-spin heme electron paramagnetic resonance (EPR) signal due to cytochrome a3. This suggests that NO coordinates to Cu{a3}+2 and breaks the antiferromagnetic couple by forming a cytochrome a3+3-Cu{a3}+2-NO complex. The intensity of the high-spin cytochrome a3 signal depends on the method of preparation of the enzyme and maximally accounts for 58% of one heme. The effect of N3- on the cytochrome a3+3-Cu{a3}+2-NO complex is to reduce cytochrome a3 to the ferrous state, and this is followed by formation of a new complex that exhibits EPR signals characteristic of a triplet species. On the basis of optical and EPR results, a NO bridge between cytochrome a3+2 and Cu{a3}+2 is proposed-i.e., cytochrome a3+2-NO-Cu{a3}+2. The half-field transition observed at g = 4.34 in the EPR spectrum of this triplet species exhibits resolved copper hyperfine splittings with |A{}| = 0.020 cm-1, indicating that the Cu{a3}+2 in the cytochrome a3+2-NO-Cu{a3}+2 complex is similar to a type 2 copper site

Modification and Improvement of Empirical Likelihood for Missing Response Problem

An empirical likelihood (EL) estimator was proposed by Qin and Zhang (2007) for a missing response problem under a missing at random assumption. They showed by simulation studies that the finite sample performance of EL estimator is better than some existing estimators. However, the empirical likelihood estimator does not have a uniformly smaller asymptotic variance than other estimators in general. We consider several modifications to the empirical likelihood estimator and show that the proposed estimator dominates the empirical likelihood estimator and several other existing estimators in terms of asymptotic efficiencies. The proposed estimator also attains the minimum asymptotic variance among estimators having influence functions in a certain class