Structural changes, common stochastic trends and unit roots in panel data

In this paper we propose a new test statistic that considers multiple structural breaks to analyse the non-stationarity of a panel data set. The methodology is based on the common factor analysis in an attempt to allow for some sort of dependence across the individuals. Thus allowing for multiple structural breaks in the â€Panel Analysis of Non-stationarity in Idiosyncratic and Common components†(PANIC) methodology increases the degree of heterogeneity when assessing the stochastic properties of the panel data setmultiple structural breaks, common factors, panel data unit root tests, principal components

COMPLETE FLEXIBILITY SYSTEMS AND THE STATIONARITY OF U.S. MEAT DEMANDS

A Rotterdam demand model is used to detect evidence of structural change in beef, pork, and chicken demands. The demand model is partially inverted prior to estimation to account for meat supply fixity. Estimation uses a likelihood maximization routine applied to 1950 through 1985 annual data. The results suggest severe disruption in the meat markets in the 1970s. A comparison of the 1980s and the 1960s elasticity structures reveals that income and cross-price elasticities are nearly the same but direct price elasticities are lower and are trending toward even more inelasticity. Implications for pricing and risk management are discussed.Demand and Price Analysis, Livestock Production/Industries,

What can one learn about Self-Organized Criticality from Dynamical Systems theory ?

We develop a dynamical system approach for the Zhang's model of Self-Organized Criticality, for which the dynamics can be described either in terms of Iterated Function Systems, or as a piecewise hyperbolic dynamical system of skew-product type. In this setting we describe the SOC attractor, and discuss its fractal structure. We show how the Lyapunov exponents, the Hausdorff dimensions, and the system size are related to the probability distribution of the avalanche size, via the Ledrappier-Young formula.Comment: 23 pages, 8 figures. to appear in Jour. of Stat. Phy

Unit Root Testing in a Central Bank

Central bank economists have to understand and forecast macroeconomic time series. A serious problem that they face is that those series are often trended or a.ected by persistent innovations to the process. To try to get round this problem, or at least to understand its possible e.ects, it is common to test whether series are stationary. These tests are often called unit-root tests.1 In this handbook we discuss such testing. A model-builder should use appropriate econometric techniques. In order to choose between alternative estimators, the model-builder needs to think carefully about the relevant theory and the available data. But economic theory is rarely unambiguous in its implications for the data generating process. Subjecting the data to pre-estimation testing can help to gauge the relevance of different theories and possible data problems.Unit, Root, Testing, Central Bank