FDI Determinants: Case of Romania

Even if the FDI is important for all host countries, for those in the process of transition to a market economy the FDI presence is critical under many respects. Not all transition countries benefited from the very beginning from the FDI presence. Several determinant factors explain the differences. Romania was lagging behind regarding the interest of foreign investors during the first 9–10 years of transition. The situation has improved greatly. The aim of this paper is to identify the main factors determining the evolution in the FDI/GDP (%) as proxy for the FDI evolution. To this end, we used the method of factors analyses. The four resulted determinant factors are: Market size and potential, Reform progress, Business liberalization, and Labor cost. A linear regression model expresses the connections between dependent variable and the four determinant factors. The paper concludes with certain policy implications.

高校教师情绪劳动策略量表本土化修订

目的：对CUKUR（2009）编制的高校教师情绪劳动策略量表进行本土化修订,以形成适合中国文化背景的高校教师情绪劳动量表。方法：在专家评定和开放式调查的基础上形成了20个题目的初始量表,对581名被试的数据采用探索性因素分析和验证性因素分析的方法确定量表结构。结果：高校教师情绪劳动量表由4个维度19个题目构成,验证性因素分析结果表明量表具有较好的结构效度（X^2／df=1．18,NNFI=0．98,CFI=0．98,RMSEA-0．03）,各维度的CronbacWs口系数介于0．78～0．88之间。结论：高校教师情绪劳动量表的信度和效度都达到了心理测量学的要求,可以用于进一步的研究。     

城市化与产业发展的匹配机制研究--基于比较优势的理论视角

城市化与产业发展是相互促进、互为因果螺旋上升的关系,城市是产业发展的载体,城市化的推进引致巨大的消费需求和投资需求,而产业是城市化的动力和支撑。城市化进程可以看成是资源、基础设施、制度和环境等一系列要素禀赋的动态变化,并在此基础上形成比较优势,吸引特定产业的投资和促进产业演进,在一个静态时点上,城市化与产业发展处于一般均衡状态,二者之间存在一种相互匹配关系。因此,在我国新型城镇化建设中要树立大、中、小不同规模城市协同发展的理念,在分析城市要素动态变化基础上,实施科学有效的产业政策。     

Estimation of parameters of inverse Weibull distribution and application to multi-component stress-strength model

The problem of estimation of the parameters of two-parameter inverse Weibull distributions has been considered. We establish existence and uniqueness of the maximum likelihood estimators of the scale and shape parameters. We derive Bayes estimators of the parameters under the entropy loss function. Hierarchical Bayes estimator, equivariant estimator and a class of minimax estimators are derived when shape parameter is known. Ordered Bayes estimators using information about second population are also derived. We investigate the reliability of multi-component stress-strength model using classical and Bayesian approaches. Risk comparison of the classical and Bayes estimators is done using Monte Carlo simulations. Applications of the proposed estimators are shown using real data sets.     

Noninformative priors for linear combinations of normal means with unequal variances

For normal populations with unequal variances, we develop matching priors and reference priors for a linear combination of the means. Here, we find three second-order matching priors: a highest posterior density (HPD) matching prior, a cumulative distribution function (CDF) matching prior, and a likelihood ratio (LR) matching prior. Furthermore, we show that the reference priors are all first-order matching priors, but that they do not satisfy the second-order matching criterion that establishes the symmetry and the unimodality of the posterior under the developed priors. The results of a simulation indicate that the second-order matching prior outperforms the reference priors in terms of matching the target coverage probabilities, in a frequentist sense. Finally, we compare the Bayesian credible intervals based on the developed priors with the confidence intervals derived from real data.     

Bayesian testing of restrictions on vector autoregressive models

In this study, we propose a prior on restricted Vector Autoregressive (VAR) models. The prior setting permits efficient Markov Chain Monte Carlo (MCMC) sampling from the posterior of the VAR parameters and estimation of the Bayes factor. Numerical simulations show that when the sample size is small, the Bayes factor is more effective in selecting the correct model than the commonly used Schwarz criterion. We conduct Bayesian hypothesis testing of VAR models on the macroeconomic, state-, and sector-specific effects of employment growth.     

The umvue and bayes estimate of reliability of mixed failure time distribution

We study the reliability estimates of the non-standard mixture of degenerate (degenerated at zero) and exponential distributions. The Uniformly Minimum Variance Unbiased Estimator (UMVUE) and Bayes estimator of the reliability for some selective prior when the mixing proportion is known and unknown are derived. The Bayes risk is computed for each Bayes estimator of the reliability. A simulated study is carried out to assess the performance of the estimators alongwith the true and Maximum Likelihood Estimate (MLE) of the reliability. An example from Vannman (1991) is also discussed at the end of the paper.     

Is categorization of random data necessary for parallel analysis on Likert-type data?

Random eigenvalues are the key elements in parallel analysis. When analyzing Likert-type data, is it necessary to convert the continuous random data to discrete type before estimating eigenvalues? The study compared the random eigenvalues obtained from continuous and categorized random data from two popular computer programs to be used as the basis for comparison in conducting parallel analysis on Likert-type data. Results indicated that categorized random data gave eigenvalues and number of factors similar to those obtained from continuous random data. It is suggested that when conducting parallel analysis on Likert-type data by the two programs, the conversion is unnecessary.     

On the simple step-stress model for two-parameter exponential distribution

In this paper, we consider the simple step-stress model for a two-parameter exponential distribution, when both the parameters are unknown and the data are Type-II censored. It is assumed that under two different stress levels, the scale parameter only changes but the location parameter remains unchanged. It is observed that the maximum likelihood estimators do not always exist. We obtain the maximum likelihood estimates of the unknown parameters whenever they exist. We provide the exact conditional distributions of the maximum likelihood estimators of the scale parameters. Since the construction of the exact confidence intervals is very difficult from the conditional distributions, we propose to use the observed Fisher Information matrix for this purpose. We have suggested to use the bootstrap method for constructing confidence intervals. Bayes estimates and associated credible intervals are obtained using the importance sampling technique. Extensive simulations are performed to compare the performances of the different confidence and credible intervals in terms of their coverage percentages and average lengths. The performances of the bootstrap confidence intervals are quite satisfactory even for small sample sizes.