Time preference and labour migration in an OLG model with land and capital

This paper constructs a two-country migration model in the lines of Galor (1986), in which the world population consists of individuals of two types who have different time preferences. Production uses three inputs: mobile labour, immobile capital and land. It is shown that both countries are necessarily inhabited by agents of both types and exhibit equal density of population and equal interest rate at the steady state equilibrium of the integrated economy. The steady state welfare implications of international labour migration are studied.This paper has been written during Philippe Michel's visit at CORE, September 1994. Earlier drafts of this paper have been presented at the meeting Journées d'étude générations imbriquées (Marseille, November 24–26, 1994), at the ESPE conference (Lisbon, June 1–3, 1995) and at the EEA annual meeting (Prag, September 1–3, 1995). Comments by participants are gratefully acknowledged. We are also grateful to Stéphane Déo, Kamhon Kan, Pierre Pestieau, Christoph Schmidt and two anonymous referees for helpful comments. The usual disclaimer applies.Responsible editor: Alessandro Cigno     

用结构参数F预测饱和烷烃水的溶解度和辛醇/水分配系数

用结构参数F预测了46种饱和烷烃水的溶解度和辛醇／水分配系数,预测结果与文献值十分接近．     

Macroeconomic impacts of Chinese currency appreciation on China and the Rest of World: A global CGE analysis

This paper analyzes the ex-ante short-term impacts of the Chinese RMB appreciation on the Chinese and world economy, using a novel approach of modeling nominal exchange rate adjustment in the GTAP, a global CGE model. Scenario results show that Chinese economy will be affected negatively, with lower real GDP, lower employment rates, and a decline in the trade surplus. Chinese currency appreciation has positive impact on the GDP of the major countries and regions, although by a small margin. With higher Chinese exchange rate, trade balance for other trading partner countries improves with the exception of the U.S.     

The Interpretation of R 2 in Autoregressive-Moving Average Time Series Models

It is demonstrated that factors needed to conduct tests and form confidence intervals for the ratio of two normal variances can be found using one of the new desk calculators which compute F probabilities.     

Log-linear models for mutations in the HIV genome

We discuss a general application of categorical data analysis to mutations along the HIV genome. We consider a multidimensional table for several positions at the same time. Due to the complexity of the multidimensional table, we may collapse it by pooling some categories. However, the association between the remaining variables may not be the same as before collapsing. We discuss the collapsibility of tables and the change in the meaning of parameters after collapsing categories. We also address this problem with a log-linear model. We present a parameterization with the consensus output as the reference cell as is appropriate to explain genomic mutations in HIV. We also consider five null hypotheses and some classical methods to address them. We illustrate methods for six positions along the HIV genome, through consideration of all triples of positions.     

Limit theory of generalized order statistics

Generalized order statistics (gos) were introduced by Kamps [1995. A Concept of Generalized Order Statistics. Teubner, Stuttgart] to unify several models of ordered random variables (rv's), e.g., (ordinary) order statistics (oos), records, sequential order statistics (sos). In a wide subclass of gos that includes oos and sos, the possible limit distribution functions (df's) of the maximum gos are obtained in Nasri-Roudsari [1996. Extreme value theory of generalized order statistics. J. Statist. Plann. Inference 55, 281–297]. In this paper, for this subclass, necessary and sufficient conditions of weak convergence, as well as the form of the possible limit df's of extreme, intermediate and central gos are derived. These results are extended to a wider subclass.     

Stochastic volatility modelling in continuous time with general marginal distributions: Inference,prediction and model selection

We compare results for stochastic volatility models where the underlying volatility process having generalized inverse Gaussian (GIG) and tempered stable marginal laws. We use a continuous time stochastic volatility model where the volatility follows an Ornstein–Uhlenbeck stochastic differential equation driven by a Lévy process. A model for long-range dependence is also considered, its merit and practical relevance discussed. We find that the full GIG and a special case, the inverse gamma, marginal distributions accurately fit real data. Inference is carried out in a Bayesian framework, with computation using Markov chain Monte Carlo (MCMC). We develop an MCMC algorithm that can be used for a general marginal model.     

Single use conservative confidence regions in multivariate controlled calibration

We consider a multivariate linear model for multivariate controlled calibration, and construct some conservative confidence regions, which are nonempty and invariant under nonsingular transformations. The computation of our confidence region is easier compared to some of the existing procedures. We illustrate the results using two examples. The simulation results show the closeness of the coverage probability of our confidence regions to the assumed confidence level.     

A p-value for testing the equivalence of the variances of a bivariate normal distribution

A p-value is developed for testing the equivalence of the variances of a bivariate normal distribution. The unknown correlation coefficient is a nuisance parameter in the problem. If the correlation is known, the proposed p-value provides an exact test. For large samples, the p-value can be computed by replacing the unknown correlation by the sample correlation, and the resulting test is quite satisfactory. For small samples, it is proposed to compute the p-value by replacing the unknown correlation by a scalar multiple of the sample correlation. However, a single scalar is not satisfactory, and it is proposed to use different scalars depending on the magnitude of the sample correlation coefficient. In order to implement this approach, tables are obtained providing sub-intervals for the sample correlation coefficient, and the scalars to be used if the sample correlation coefficient belongs to a particular sub-interval. Once such tables are available, the proposed p-value is quite easy to compute since it has an explicit analytic expression. Numerical results on the type I error probability and power are reported on the performance of such a test, and the proposed p-value test is also compared to another test based on a rejection region. The results are illustrated with two examples: an example dealing with the comparability of two measuring devices, and an example dealing with the assessment of bioequivalence.     

Semiparametric analysis of long-range dependence in nonlinear regression

This paper establishes consistency and asymptotic distribution theory for the least squares estimate of a vector parameter of non-linear regression with long-range dependent noise. A covariance-based estimate of the memory parameter is proposed. The consistency of the estimate is established.