THE INTERNATIONAL TRANSMISSION OF VOLATILITY SHOCKS: AN EMPIRICAL ANALYSIS

This paper proposes an empirical model which can be used to estimate the international transmission of volatility shocks. Using this model we estimate that a one standard deviation increase in the volatility of the shock to US real GDP leads to a decline in UK GDP of 1% relative to trend and a 0.7% increase in UK CPI relative to trend at the two‐year horizon. Using a nonlinear open‐economy DSGE model, we find that these empirical estimates are consistent with the response to a perturbation to the volatility of foreign “supply” type shocks, while an increase in the volatility of demand shocks has a negligible impact.     

ECONOMIES OF SCALE AND THE SIZE OF EXPORTERS

How important are economies of scale in exporting? We argue that firm size cannot be the main determinant of export status if a model is to be consistent with the observed number and size of exporters. Instead, we need a lot of variation independent of firm size to reconcile the model with the data. We show that the augmented model also has markedly different implications regarding the margin of adjustment in the event of a trade liberalization: most of the adjustment is through the intensive margin and productivity gains due to reallocation are halved.     

Estimation of eigenvalues of the scale matrix of the multivariate f distribution

Let F have the multivariate F distribution with a scale matrix Δ. In this paper, the problem of estimating the eigenvalues of the scale matrix Δ is considered. New class of estimators are obtained which dominate the best linear estimator of the form cF. Simulation study is also carried out to compare the performance of these estimators.     

The pitfall of instrumental variables in big data: What the rule of thumb can't give you

ABSTRACT

Background: Instrumental variables (IVs) have become much easier to find in the “Big data era” which has increased the number of applications of the Two-Stage Least Squares model (TSLS). With the increased availability of IVs, the possibility that these IVs are weak has increased. Prior work has suggested a ‘rule of thumb’ that IVs with a first stage F statistic at least ten will avoid a relative bias in point estimates greater than 10%. We investigated whether or not this threshold was also an efficient guarantee of low false rejection rates of the null hypothesis test in TSLS applications with many IVs.

Objective: To test how the ‘rule of thumb’ for weak instruments performs in predicting low false rejection rates in the TSLS model when the number of IVs is large.

Method: We used a Monte Carlo approach to create 28 original data sets for different models with the number of IVs varying from 3 to 30. For each model, we generated 2000 observations for each iteration and conducted 50,000 iterations to reach convergence in rejection rates. The point estimate was set to 0, and probabilities of rejecting this hypothesis were recorded for each model as a measurement of false rejection rate. The relationship between the endogenous variable and IVs was carefully adjusted to let the F statistics for the first stage model equal ten, thus simulating the ‘rule of thumb.’

Results: We found that the false rejection rates (type I errors) increased when the number of IVs in the TSLS model increased while holding the F statistics for the first stage model equal to 10. The false rejection rate exceeds 10% when TLSL has 24 IVs and exceed 15% when TLSL has 30 IVs.

Conclusion: When more instrumental variables were applied in the model, the ‘rule of thumb’ was no longer an efficient guarantee for good performance in hypothesis testing. A more restricted margin for F statistics is recommended to replace the ‘rule of thumb,’ especially when the number of instrumental variables is large.     

Statistical aspects of linkage analysis in interlaboratory studies

This paper investigates statistical issues that arise in interlaboratory studies known as Key Comparisons when one has to link several comparisons to or through existing studies. An approach to the analysis of such a data is proposed using Gaussian distributions with heterogeneous variances. We develop conditions for the set of sufficient statistics to be complete and for the uniqueness of uniformly minimum variance unbiased estimators (UMVUE) of the contrast parametric functions. New procedures are derived for estimating these functions with estimates of their uncertainty. These estimates lead to associated confidence intervals for the laboratories (or studies) contrasts. Several examples demonstrate statistical inference for contrasts based on linkage through the pilot laboratories. Monte Carlo simulation results on performance of approximate confidence intervals are also reported.     

A Note on Screening Regression Equations

Consider developing a regression model in a context where substantive theory is weak. To focus on an extreme case, suppose that in fact there is no relationship between the dependent variable and the explanatory variables. Even so, if there are many explanatory variables, the R ² will be high. If explanatory variables with small t statistics are dropped and the equation refitted, the R ² will stay high and the overall F will become highly significant. This is demonstrated by simulation and by asymptotic calculation.     

新时期党内民主的实践探索

党的十六大以来，以胡锦涛同志为总书记的党中央，大力推进党内民主建设，并进行了一系列的新探索。这些新探索包括：党的定期报告工作制度稳步推进、党员民主权利保障取得历史性成就、党内监督机制取得重大进展、党纪工作卑得重大成果、党务公开逐步推行、党代会制度建设有了长足进步、党内选举取得新的突破等等。     

16至18世纪中国婢女现象论析

16至18世纪国家对蓄婢行为的控制削弱,蓄婢现象突出。其主要表现为:婢女数量增多,分布广泛,蓄婢成风。立足于社会史和女性史的角度对此现象进行考察可发现,除了灾荒、官府的横征暴敛、高利贷盘剥和清初奴隶制残余向内地扩展以外,扭曲的男尊女卑观念、奢侈的社会风气以及从事婢女交易的专业团伙和中介人的存在也是形成婢女现象的根源。对婢女现象的再考察有助于我们更好地理解下层女性的生存状态。