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This paper empirically investigates the dynamic behavior of non-ferrous metal prices, specifically for copper, aluminum, nickel, and zinc, using a range of continuous-time diffusion models. The study employs maximum likelihood estimation with approximate transition probability density functions to analyze daily price data. Our findings reveal that while price volatility is the dominant factor explaining the dynamics of all four metals, the best-fitting model varies by metal. The GD-GV model, with its generalized volatility function, is found to be the most appropriate for aluminum, whereas the more parsimonious CKLS model provides the best fit for copper, nickel, and zinc. This analysis demonstrates that price dynamics within the non-ferrous metal group are not homogeneous, underscoring the necessity of a flexible, metal-specific modeling approach for accurate price characterization.

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This paper analyzes the influence of factors such as income, age, gender, education level, and residential area on the
political orientation of Seoul citizens using multinomial logistic regression models. The marginal effect of income shows that there is little change across most income brackets, but the probability of being conservative increases sharply in the highest income bracket, while the probabilities of being moderate and liberal decrease. The effect of age indicates that, as expected, the probability of becoming conservative increases and the probability of becoming liberal decreases as age
increases. The impact of education is not linear; graduating from college increases the probability of being liberal and decreases the probability of being conservative. However, obtaining a graduate degree, such as a master's or doctorate, results in an increase in both liberal and conservative probabilities, while the probability of being moderate decreases. It was also found that women are more likely to be liberal than men and show very different patterns in the marginal effects of income, age, and education compared to men.

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This paper applies a Bayesian Variable Selection (BVS) framework to forecast the year-on-year growth rate of China’s newly built housing price index (HPI YoY). Using a broad set of macroeconomic and financial predictors, we implement a hierarchical BVS model with rolling-window estimation and direct multi-horizon forecasting. Out-of-sample performance is evaluated against autoregressive (AR) models, the random walk (RW), and a machine learning benchmark, the Random Forest (RF). The results show that BVS consistently outperforms AR and RW across most horizons in both point and density forecasts, and dominates RF within two years ($h=1$–$18$), while RF is only slightly better at very long horizons ($h=24,30,36$). Horizon-specific predictors provide further insights: lagged HPI and market sentiment (RECI) drive short-run dynamics; RECI, inflation (CPI), and housing credit conditions (HPF, IHLL) matter at medium to long horizons; and demographic fundamentals (PNGR) dominate at long and ultra-long horizons. The forecast results point to a subdued housing market over the next two years, consistent with a structural break around April 2022 and the lasting impact of demographic shifts. Overall, the study demonstrates that BVS not only improves forecasting accuracy but also enhances interpretability, making it a valuable tool for academic research and housing market policy design.

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Since the early 1980s, South Korea’s total fertility rate (TFR) has continuously declined, recently reaching the 0.7 range—the lowest among OECD countries. This study develops an overlapping generations (OLG) general equilibrium model that incorporates married women’s labor supply decisions as well as their choices regarding fertility and childrearing. The model is calibrated to match average labor force participation rates of married women and fertility outcomes over the period 2015–2019. In the baseline economy, women’s fertility decisions replicate the age-specific average number of children and fertility patterns across household income groups observed in the data. When the model parameters are adjusted to reflect the gender wage gap observed in the 1990s, women’s incentives to participate in the labor market decline substantially, leading married women to increase fertility instead of labor supply. Quantitatively, however, the change in the TFR is not large enough to explain the sharp decline in fertility observed over the past three decades. This suggests that the gender wage gap alone cannot fully account for the simultaneous rise in female employment and fall in fertility in South Korea.