Tax structure and state economic growth during the Great Recession

Concern about the effect of taxes on economic growth and development in the United States is longstanding. While most studies are concerned with the growth impacts of tax burden, marginal rates, or the impact of a particular tax, there are few works that examine the impact of tax structure in the way it is defined in this work. Here, tax structure is defined as the shares of revenue collected by various taxes. Using a pool of data on the 50 states between 2004 and 2010, this paper explores the relationship between state and local tax structure and growth of real per-capita GDP through the Great Recession centered in 2008. The results are used to generate estimates of the growth impacts of revenue neutral changes in tax shares.     

Estimators of Influence Function

ABSTRACT

This article discusses estimators of influence function with some new counter-examples and tries to uphold their usefulness mathematically as well as through simulation. It is suggested that some estimators of influence function of uniformly Fréchet differentiable functional has more desirable properties.     

Tests for Trend: A Simulation Study

In this study, we use the wavelet analysis to construct a test statistic to test for the existence of a trend in the series. We also propose a new approach for testing the presence of trend based on the periodogram of the data. Since we are also interested in the presence of a long-memory process among the data, we study the properties of our test statistics under different degrees of dependency. We compare the results when using the band periodogram test and the wavelet test with results obtained by applying the ordinary least squares (OLS) method under the same conditions.     

Parameter Estimation for First-Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter

In this article, we propose a new estimate algorithm for the parameters of a first-order Random Coefficient Autoregressive (RCA) Model. This algorithm turns out to be very reliable in estimating the true parameter values of a given model. It combines quasi-maximum likelihood method, the Kalman filter algorithm, and the Powell's method. Simulation results demonstrate that the algorithm is viable and promising.     

Bayesian inference and prediction of the Rayleigh distribution based on ordered ranked set sampling

Based on ordered ranked set sample, Bayesian estimation of the model parameter as well as prediction of the unobserved data from Rayleigh distribution are studied. The Bayes estimates of the parameter involved are obtained using both squared error and asymmetric loss functions. The Bayesian prediction approach is considered for predicting the unobserved lifetimes based on a two-sample prediction problem. A real life dataset and simulation study are used to illustrate our procedures.     

外语能力磨蚀的性别差异初探

对300余名大学生进行了英语阅读和听力测试,并从中随机抽取男女生各50名进行了写作测试,得出如下结论:(1)外语磨蚀程度存在明显的性别差异;(2)男女生在深度阅读、短对话听力以及段落听力方面的磨蚀差异不显著;(3)女生在快速阅读能力方面的磨蚀程度明显超过男生;(4)女生写作能力在切题性、连贯性以及清楚性方面的磨蚀程度显著大于男生,而在语言的正确性方面的磨蚀没有明显超过男生.     

A Genuine Rank‐Dependent Generalization of the Von Neumann‐Morgenstern Expected Utility Theorem

This paper uses “revealed probability trade‐offs” to provide a natural foundation for probability weighting in the famous von Neumann and Morgenstern axiomatic set‐up for expected utility. In particular, it shows that a rank‐dependent preference functional is obtained in this set‐up when the independence axiom is weakened to stochastic dominance and a probability trade‐off consistency condition. In contrast with the existing axiomatizations of rank‐dependent utility, the resulting axioms allow for complete flexibility regarding the outcome space. Consequently, a parameter‐free test/elicitation of rank‐dependent utility becomes possible. The probability‐oriented approach of this paper also provides theoretical foundations for probabilistic attitudes towards risk. It is shown that the preference conditions that characterize the shape of the probability weighting function can be derived from simple probability trade‐off conditions.     

Barriers to green innovation initiatives among manufacturers: the Malaysian case

This study investigated the internal and external barriers to green innovation initiatives among Malaysian manufacturers. Data was gathered through a survey of 153 manufacturing companies in Malaysia. Data was analyzed using the partial least squares technique. Results indicated that the barriers to green products, processes, and systems innovations are different. Issues of environmental resources, attitude and perception, business practices, government support, and customer demand were found to be the barriers to green product innovations, whereas attitude and perception, business practices, poor external partnerships, insufficient information, lack of customer demand, and environmental commercial benefits were determined to be the factors that negatively affect green process innovations. As regards green system innovation, environmental resources, attitude and perception, business practices, technical barriers, government support, and environmental and commercial benefits presented themselves as the internal and external barriers that need to be addressed. The results have important implications for managers of manufacturers that have plans of promoting green products, processes, and system innovations.     

An illustration of the causality relation between government spending and revenue using wavelet analysis on Finnish data

Quarterly data for the period 1960:1 to 1997:2, conventional tests, a bootstrap simulation approach and a multivariate Rao's F-test have been used to investigate if the causality between government spending and revenue in Finland was changed at the beginning of 1990 due to future plans to create the European Monetary Union (EMU). The results indicate that during the period before 1990, the government revenue Granger-caused spending, while the opposite happened after 1990, which agrees better with Barro's tax smoothing hypothesis. However, when using monthly data instead of quarterly data for almost the same sample period, totally different results have been noted. The general conclusion is that the relationship between spending and revenue in Finland is still not completely understood. The ambiguity of these results may well be due to the fact that there are several time scales involved in the relationship, and that the conventional analyses may be inadequate to separate out the time scale structured relationships between these variables. Therefore, to investigate empirically the relation between these variables we attempt to use the wavelets analysis that enables us to separate out different time scales of variation in the data. We find that time scale decomposition is important for analysing these economic variables.     

Towards optimal regression estimation in sample surveys

The Montanari (1987) regression estimator is optimal when the population regression coefficients are known. When the coefficients are estimated, the Montanari estimator is not optimal and can be extremely volatile. Using design‐based arguments, this paper proposes a simpler and better alternative to the Montanari estimator that is also optimal when the population regression coefficients are known. Moreover, it can be easily implemented as it involves standard weighted least squares. The estimator is applicable under single stage stratified sampling with unequal probabilities within each stratum.