• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.

• Journal of Venture Innovation
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• v.5 no.3
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• pp.71-80
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• 2022

The real estate market is an important part of a country's economy and plays a major role in economic growth through the growth of many related industries. Changes in interest rates affect asset prices and have a significant impact on housing prices. This study analyzed housing prices by dividing them into nationwide, local, and Seoul housing prices in order to analyze whether the effect of changes in interest rates on housing prices shows regional differences. The analysis was conducted from the first quarter of 2011 to the fourth quarter of 2021, and was analyzed using the DOLS model. The main analysis results are as follows. First, interest rates were found to have a significant negative effect on national housing prices, and a drop in interest rates significantly increased national housing prices and an increase in interest rates significantly lowered national housing prices. The consumer price index and loan growth rate also had a positive effect on housing prices nationwide, but statistical significance was not high. Second, interest rates had a negative effect on local housing prices, unlike national housing prices, but were not statistically significant. On the other hand, it was found that the consumer price index and loan growth rate had a larger and significant positive effect on local housing prices compared to national housing prices. Finally, it was found that the interest rate had the only significant negative effect on housing prices in Seoul. And this effect was greater and more significant than the effect on national and local housing prices. In the end, it was found that the effect of interest rates on Korean housing prices differs locally. Interest rates have a significant negative effect on national housing prices, and local housing prices, but they are not statistically significant. In addition, the interest rate was found to have the largest and most significant negative effect on housing prices in Seoul. In addition, it was found that there was a difference in the effect of macroeconomic variables on housing prices. This means that there are differences between regions with different factors influencing local and Seoul housing prices, and this point should be considered when drafting and implementing real estate policies.

• Korean small business review
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• v.42 no.4
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• pp.69-102
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• 2020

Shedding new light on the research trend on entrepreneurial ecosystems in the 40-year history of the Asia Pacific Journal of Small Business, this study aims at exploring a potential measurement framework of ecological inputs and outputs in an entrepreneurial ecosystem that promotes entrepreneurship at geographical and spatial levels. As a result of the analysis of research on the entrepreneurial ecosystem in the journal, we found that prior studies emphasized the managerial importance of various ecological factors on the premise of possible causalities between the factors and entrepreneurship. However, empirical research to verify the premised causality has been underexplored yet. This literature gap may lead to unbalanced development of conceptual and case studies that identify requirements for successful entrepreneurial ecosystems based on experiential facts, thereby hindering the generalization of the research results for practical implications. In that there is a growing interest in creating and operating productive entrepreneurial ecosystems as an innovation engine that drives national and regional economic growth, it is necessary to explore and develop the measurement framework for ecological factors that can be used in future empirical research. Hereupon, we apply a conceptual model of 'input-output-outcome-impact' to categorize individual environmental factors identified in prior studies. Based on the model. We operationalize ecological input factors as the financial, intellectual, institutional, and social capitals, and ecological output factors as the establishment-based, innovation-based, and performance-based entrepreneurship. Also, we propose several longitudinal databases that future empirical research can use in analyzing the potential causality between the ecological input and output factors. The proposed framework of entrepreneurial ecosystems, which focuses on measuring ecological input and output factors, has a high application value for future research that analyzes the causality.