In 2010, the debate over valuation of the Renminbi is again heating up and the Chinese currency has returned to appreciating against the dollar But is the Renminbi undervalued？ Drawing on the Balassa-Samuelson （B-S...In 2010, the debate over valuation of the Renminbi is again heating up and the Chinese currency has returned to appreciating against the dollar But is the Renminbi undervalued？ Drawing on the Balassa-Samuelson （B-S） effect on purchasing power parity, we conduct theoretical and empirical analyses of this issue. Theoretical analysis proves that a country＇s real exchange rate will appreciate with rising income, but the currencies of low-income countries tend to be undervalued. In order to avoid biased conclusions resulting from a single dataset or sampling method, we draw on three major publicly-available datasets to examine the B-S effect across 144 economies. Our results indicate that the degree of Renminbi misvaluation is highly dependent on the data source. Synthesizing analyses of diverse datasets, we estimate that the Renminbi was only undervalued by less than 8per cent in 2009. We conclude that China＇s external imbalance most probably results from deep-seated structural imbalances rather than Renminbi undervaluation.展开更多
Structural equation model(SEM) is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. In the traditional SEM, it is often assumed that random errors and exp...Structural equation model(SEM) is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. In the traditional SEM, it is often assumed that random errors and explanatory latent variables follow the normal distribution, and the effect of explanatory latent variables on outcomes can be formulated by a mean regression-type structural equation. But this SEM may be inappropriate in some cases where random errors or latent variables are highly nonnormal. The authors develop a new SEM, called as quantile SEM(QSEM), by allowing for a quantile regression-type structural equation and without distribution assumption of random errors and latent variables. A Bayesian empirical likelihood(BEL) method is developed to simultaneously estimate parameters and latent variables based on the estimating equation method. A hybrid algorithm combining the Gibbs sampler and Metropolis-Hastings algorithm is presented to sample observations required for statistical inference. Latent variables are imputed by the estimated density function and the linear interpolation method. A simulation study and an example are presented to investigate the performance of the proposed methodologies.展开更多
文摘In 2010, the debate over valuation of the Renminbi is again heating up and the Chinese currency has returned to appreciating against the dollar But is the Renminbi undervalued？ Drawing on the Balassa-Samuelson （B-S） effect on purchasing power parity, we conduct theoretical and empirical analyses of this issue. Theoretical analysis proves that a country＇s real exchange rate will appreciate with rising income, but the currencies of low-income countries tend to be undervalued. In order to avoid biased conclusions resulting from a single dataset or sampling method, we draw on three major publicly-available datasets to examine the B-S effect across 144 economies. Our results indicate that the degree of Renminbi misvaluation is highly dependent on the data source. Synthesizing analyses of diverse datasets, we estimate that the Renminbi was only undervalued by less than 8per cent in 2009. We conclude that China＇s external imbalance most probably results from deep-seated structural imbalances rather than Renminbi undervaluation.
基金supported by the National Natural Science Foundation of China under Grant No.11165016
文摘Structural equation model(SEM) is a multivariate analysis tool that has been widely applied to many fields such as biomedical and social sciences. In the traditional SEM, it is often assumed that random errors and explanatory latent variables follow the normal distribution, and the effect of explanatory latent variables on outcomes can be formulated by a mean regression-type structural equation. But this SEM may be inappropriate in some cases where random errors or latent variables are highly nonnormal. The authors develop a new SEM, called as quantile SEM(QSEM), by allowing for a quantile regression-type structural equation and without distribution assumption of random errors and latent variables. A Bayesian empirical likelihood(BEL) method is developed to simultaneously estimate parameters and latent variables based on the estimating equation method. A hybrid algorithm combining the Gibbs sampler and Metropolis-Hastings algorithm is presented to sample observations required for statistical inference. Latent variables are imputed by the estimated density function and the linear interpolation method. A simulation study and an example are presented to investigate the performance of the proposed methodologies.
