Capital mobility in Latin American and Caribbean countries: new evidence from dynamic common correlated effects panel data modeling

This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel data techniques.This is the first study on capital mobility in Latin American and Caribbean countries to employ the recently developed panel data procedure of the dynamic common correlated effects modeling technique of Chudik and Pesaran(J Econ 188:393–420,2015)and the error-correction testing of Gengenbach,Urbain,and Westerlund(Panel error correction testing with global stochastic trends,2008,J Appl Econ 31:982–1004,2016).These approaches address the serious panel data econometric issues of crosssection dependence,slope heterogeneity,nonstationarity,and endogeneity in a multifactor error-structure framework.The empirical findings of this study reveal a low average(mean)savings–retention coefficient for the panel as a whole and for most individual countries,as well as indicating a cointegration relationship between saving and investment ratios.The results indicate that there is a relatively high degree of capital mobility in the Latin American and Caribbean countries in the short run,while the long-run solvency condition is maintained,which is due to reduced frictions in goods and services markets causing increase competition.Increased capital mobility in these countries can promote economic growth and hasten the process of globalization by creating a conducive economic environment for FDI in these countries.

Panel data models with cross-sectional dependence: a selective review

In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.

The Impact of Restricted Measures on Price Discovery in Stock Index Futures:Evidence from CSI 500 Stock Index Futures

This paper compares the impact of restricted measures on CSI 500 stock index futures market and its underlying spot market.It uses vector error correction(VECM)model and common factor analysis method to study the differences between the two markets before and after the restricted measures was implemented.This paper analyzes the price discovery function through three aspects,i.e.,response to new information,price ratio of new information,and price discovery contribution degree of two markets.Based on empirical results,it is clear that group one in the period of April 17th to September 2nd has an obvious price discovery function.However,group two in the period of September 7th to December 31th does not have.The result shows that stock index futures do have price discovery function to some extent.However,due to the impact of restrictive policies,the spot market price contribution may exceed the futures market in some special time periods,which implies that the price discovery function of CSI 500 stock index futures market is not stable.

Regime Dependent Sensitivity of Country Exchange Traded Funds to Common Risk Factors

If common factors jointly affect country stock markets, it is an indica- tion of global stock market integration. Common factors may affect some markets more/less than other markets, an indication of the degree of global stock market in- tegration/segmentation. In this paper, we study the integration of global stock mar- kets based on the returns on exchange traded funds （ETFs） for the US, Canada, UK, Germany, France, Italy, Australia and Japan. The relationship between country ETF returns and common risk factors may be time-varying across countries, and that favors a regime switching （RS） factor model for the dynamics of the country ETF returns. A RS factor model for the relationship between country ETF returns and common risk factors is fitted to daily data for the period from May 31, 2000 to March 31, 2014. We use the data to test a hierarchy of hypotheses on country ETF returns： （1） common factor exposure across all country ETFs and all regimes; （2） common factor exposure across some country ETFs and all regimes, and （3） common factor exposure across some country ETFs and some regimes. The RS factor model for ETF returns fits the data well and the common factors have variable effects across countries and over regimes

A simple construction of CRT-based ideal secret sharing scheme and its security extension based on common factor

Secret sharing(SS)is part of the essential techniques in cryptography but still faces many challenges in efficiency and security.Currently,SS schemes based on the Chinese Remainder Theorem(CRT)are either low in the information rate or complicated in construction.To solve the above problems,1)a simple construction of an ideal(t,n)-SS scheme is proposed based on CRT for a polynomial ring.Compared with Ning’s scheme,it is much more efficient in generating n pairwise coprime modular polynomials during the scheme construction phase.Moreover,Shamir’s scheme is also a special case of our scheme.To further improve the security,2)a common-factor-based(t,n)-SS scheme is proposed in which all shareholders share a common polynomial factor.It enables both the verification of received shares and the establishment of a secure channel among shareholders during the reconstruction phase.As a result,the scheme is resistant to eavesdropping and modification attacks by outside adversaries.

Estimation of Partially Linear Panel Data Models with Cross-Sectional Dependence

This paper studies the estimation of the partially linear panel data models,allowing for cross-sectional dependence through a common factors structure.This semiparametric additive partial linear framework,including both linear and nonlinear additive components,is more flexible compared to linear models,and is preferred to a fully nonparametric regression because of the‘curse of dimensionality’.The consistency and asymptotic normality of the proposed estimators are established for the case where both cross-sectional dimension and temporal dimension go to infinity.The theoretical findings are further supported for small samples via a Monte Carlo study.The results suggest that the proposed method is robust to a wide variety of data generation processes.

INDUSTRIAL PRODUCTION IN GERMANY AND AUSTRIA:A CASE STUDY IN STRUCTURAL TIME SERIES MODELLING

Industrial production series are volatile and often cyclical. Time series models can be used toestablish certain stylized facts, such as trends and cycles, which may be present in these series. Incertain situations, it is also possible that common factors, which may have an interesting interpretation,can be detected in production series. Series from two neighboring countries with close economicrelationships, such as Germany and Austria, are especially likely to exhibit such joint stylized facts.

Krigings over space and time based on latent low-dimensional structures

We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximation methods,the fundamental difference is that the low-dimensional structure is completely unknown in our setting,which is learned from the data collected irregularly over space but regularly in time.Furthermore,a graph Laplacian is incorporated in the learning in order to take the advantage of the continuity over space,and a new aggregation method via randomly partitioning space is introduced to improve the efficiency.We do not impose any stationarity conditions over space either,as the learning is facilitated by the stationarity in time.Krigings over space and time are carried out based on the learned low-dimensional structure,which is scalable to the cases when the data are taken over a large number of locations and/or over a long time period.Asymptotic properties of the proposed methods are established.An illustration with both simulated and real data sets is also reported.

Comparing gene expression profiles of Kashin-Beck and Keshan diseases occurring within the same endemic areas of China

In this study, differentially expressed genes in peripheral blood from patients with Kashin-Beck disease and Keshan disease were compared to further investigate the etiology and pathogenesis of both diseases, which occur in a common endemic area of China. Twenty Kashin-Beck disease patients and 12 healthy controls, and 16 Keshan disease patients and 16 healthy controls, were grouped into four pairs. Patients and controls were selected from common endemic areas for the two diseases. Total RNA was isolated from peripheral blood mononuclear cells from all patients and controls, and gene expression profiles analyzed by oligonucleotide microarrays. Sixteen genes differentially expressed in both Kashin-Beck disease and Keshan disease (versus controls) were identified, and comprised nine genes showing synchronous and seven asynchronous expression. The Comparative Toxicogenomics Database shows that expression and biological function of these genes can be affected by multiple environmental factors, including mycotoxin and selenium content, potential environmental risk factors for the two diseases. Thus, these shared differentially expressed genes may contribute to the distinct organ lesions, caused by common environmental risk factors of Kashin-Beck disease and Keshan disease.