• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.11
• /
• pp.6565-6575
• /
• 2014

Hamilton's Markov-switching model [5] was extended to the simultaneous equations model. A framework for an instrumental variable interpretation of full information maximum likelihood (FIML) by Hausman [4] can be used to deal with the problem of simultaneous equations based on the Hamilton filter [5]. A comparison of the proposed FIML Markov-switching model with the LIML Markov-switching models [1,2,3] revealed the LIML Markov-switching models to be a special case of the proposed FIML Markov-switching model, where all but the first equation were just identified. Moreover, the proposed Markov-switching model is a general form in simultaneous equations and covers a broad class of models that could not be handled previously. Excess sensitivity of marginal propensity to consume with big shocks, such as housing bubble bursts in 2008, can be determined by applying the proposed model to Campbell and Mankiw's consumption function [6], and allowing for the possibility of structural breaks in the sensitivity of consumption growth to income growth.

• Survey Research
• /
• v.9 no.2
• /
• pp.1-27
• /
• 2008

Problems of incomplete data are pervasive in statistical analysis. In particular, incomplete data have been an important challenge in repeated measures studies. The objective of this study is to give a brief introduction to missing data mechanisms and conventional/recent missing data methods and to assess the performance of various missing data methods under ignorable and non-ignorable missingness mechanisms. Given the inadequate attention to longitudinal studies with missing data, this study applied recent advances in missing data methods to repeated measures models and investigated the performance of various missing data methods, such as FIML (Full Information Maximum Likelihood Estimation) and MICE(Multivariate Imputation by Chained Equations), under MCAR, MAR, and MNAR mechanisms. Overall, the results showed that listwise deletion and mean imputation performed poorly compared to other recommended missing data procedures. The better performance of EM, FIML, and MICE was more noticeable under MAR compared to MCAR. With the non-ignorable missing data, this study showed that missing data methods did not perform well. In particular, this problem was noticeable in slope-related estimates. Therefore, this study suggests that if missing data are suspected to be non-ignorable, developmental research may underestimate true rates of change over the life course. This study also suggests that bias from non-ignorable missing data can be substantially reduced by considering rich information from variables related to missingness.

• Journal of Technology Innovation
• /
• v.22 no.3
• /
• pp.261-286
• /
• 2014

Recently, project performance and management of government R&D program investing huge amount of budget is being focused. In this study, I built a model for estimating the performance creating paths of the government R&D program, then I analyzed the attributes of performances created by industries. I used simultaneous equations with FIML and examined the IT industry and CT industry of industrial technology innovation program using survey on R&D performance conducted by KEIT. On the analysis result, all the companies create the innovation performance through the government grants. However, the one who commercialize patents is SMEs in IT industry, and conglomerates in CT industry. SMEs in IT industry, which has characteristic of complex product, need extra self innovation activities even the government supports them. For improving the commercialization performance and technology development of SMEs in CT industry, it is effective when conducting cooperative research. These findings give us the implication that it is needed to consider different factors by industrial and enterprise characteristic when planning the public policy and government tasks.