The Center and the Periphery: The Globalization of Financial Turmoil

This paper studies how financial turbulence in emerging market countries can spread across borders. We construct indices of financial globalization' and evaluate the repercussions of turmoil in three emerging markets, which experienced financial crises in the late 1990s: Brazil, Russia, and Thailand. Our findings indicate that financial turbulence in these countries only spreads globally when they affect asset markets in one or more of the world's financial centers. Otherwise, spillovers are confined to countries in the same region. We also find that fragility in institutions in the financial centers is at the core of global spillovers while economic and monetary policy news contributes to regional spillovers.

The center and the periphery: The globalization of financial turmoil

This paper studies how financial turbulence in emerging market countries can spread across borders. We construct indices of “financial globalization” and evaluate the repercussions of turmoil in three emerging markets that experienced financial crises in the late 1990s: Brazil, Russia, and Thailand. Our findings indicate that financial turbulence in these countries only spreads globally when they affect asset markets in one or more of the world’s financial centers. Otherwise, spillovers are confined to countries in the same region. Also, episodes of worldwide globalization of turmoil are mostly episodes of synchronized crashes while regional turbulence include both joint crashes and rallies.financial crises contagion stock prices exchange rate crashes turbulence interest rate spreads default

Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data

We propose an estimation approach to analyse correlated functional data which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model. Estimation is based on dimension reduction via functional principal component analysis and on mixed model methodology. Our procedure allows the decomposition of the variability in the data as well as the estimation of mean effects of interest and borrows strength across curves. Confidence bands for mean effects can be constructed conditional on estimated principal components. We provide R-code implementing our approach. The method is motivated by and applied to data from speech production research

"A Generalized SSAR Model and Predictive Distribution with an Application to VaR"

The asymmetrical movements between the downward and upward phases of the sample paths of time series have been sometimes observed. By generalizing the SSAR (simultaneous switching autoregressive) models, we introduce a class of nonlinear time series models having the asymmetrical sample paths in the upward and downward phases. We show that the class of generalized SSAR models is useful for estimating the asymmetrical predictive distribution given the present and past information. Applications to the prediction based on the predictive median and the estimation of the VaR (value at risk) in financial risk management are discussed.

"A New Light from Old Wisdoms : Alternative Estimation Methods of Simultaneous Equations with Possibly Many Instruments"

We compare four dffierent estimation methods for a coefficient of a linear structural equation with instrumental variables. As the classical methods we consider the limited information maximum likelihood (LIML) estimator and the two-stage least squares (TSLS) estimator, and as the semi-parametric estimation methods we consider the maximum emirical likelihood (MEL) estimator and the generalized method of moments (GMM) (or the estimating equation) estimator. We prove several theorems on the asymptotic optimality of the LIML estimator when the number of instruments is large, which are new as well as old, and we relate them to the results in some recent studies. Tables and figures of the distribution functions of four estimators are given for enough values of the parameters to cover most of interest. We have found that the LIML estimator has good performance when the number of instruments is large, that is, the micro-econometric models with many instruments in the terminology of recent econometric literature.

Some robust design strategies for percentile estimation in binary response models

For the problem of percentile estimation of a quantal response curve, we determine multi-objective designs which are robust with respect to misspecifications of the model assumptions. We propose a maximin approach based on efficiencies and provide designs that are simultaneously efficient with respect to the particular choice of various parameter regions and link functions. Furthermore, we deal with the problems of designing model and percentile robust experiments and give various examples of such designs, which are calculated numerically. --Binary response model,robust optimal design,c-efficiency,percentile estimation,multi-objective designs

"On Finite Sample Distributions of the Empirical Likelihood Estimator and the GMM Estimator"

The distributions of the Maximum Empirical Likelihood (MEL) estimator and the Generalized Method o Moments (GMM) estimator for the coe cient o one endogenous variable in a linear structural equation are evaluated numerically.Tables and gures are given for enough values of the parameters to cover most of interest.Comparisons of the distributions of the MEL estimator and the GMM estimator with their asymptotic expansions are made.

Not a Hollowing Out, a Stretching: Trends in U.S. Nonmetro Wage Income Distribution, 1961-2003

Much of the U.S. labor economics literature asserts that U.S. wage income inequality increased in the last half of the 20th century. These papers point to two trends: 1) the increasing dispersion in U.S. wage incomes, and 2) the rapid growth in the relative frequency of large wage incomes of fixed size in constant dollar terms. A subset of the labor economics literature interprets these trends as a hollowing out of the wage income distribution. A hollowing out would yield fewer middling wage incomes. Since nonmetro wage incomes have, historically, been smaller than metro wage incomes, a hollowing out might disproportionately displace nonmetro wage incomes into the left mode of the hollowed out distribution, that of small wage incomes. However, there was no hollowing out of the nonmetro wage income distribution between 1961 and 2003. While trends #1 and #2 exist in U.S. nonmetro wage income data, they are aspects of the stretching of the distribution of nonmetro wage incomes to the right over larger wage incomes as all its percentiles increased between 1961 and 2003. This stretching means that all nonmetro wage income percentiles increase simultaneously with greater proportional growth in the smaller percentiles. The literature focused on the greater absolute gains of the larger percentiles and took them as evidence of growing inequality. This paper shows for nonmetro wage incomes in the U.S. that those gains are but one aspect of the stretching of the distribution and that other aspects of this transformation might as easily be taken as evidence of growing equality.distribution dynamics; hollowed out distribution; inequality; nonmetropolitan; wage income; wage inequality