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[Abstract] Exporting exposes firms to foreign buyers and rivals, providing knowledge that makes innovation more effective. How much does this mechanism raise innovation efficiency, and how does it affect long‑run growth? I develop and estimate an endogenous growth model in which foreign‑market exposure enhances firms’ innovation efficiency—the exporting–innovation efficiency (EIE) channel—while broader product scope dilutes managerial attention and lowers marginal returns. The model predicts that innovation efficiency rises with export intensity but declines with product scope and firm size. Using firm‑level data on Chinese manufacturers, I document patterns consistent with these predictions and estimate that export exposure increases the effective knowledge available for innovation by ~9% at the product level. Although firm‑level gains are modest, the aggregate effect is economically meaningful: eliminating the EIE channel reduces the long‑run growth rate by about 1.3 percentage points (around 10 percent of observed growth). With the EIE channel active, trade liberalization disproportionately benefits firms with broad export portfolios, boosting their innovation efficiency and growth and accelerating the exit of low‑productivity firms. The result is a more right‑skewed distribution of productivity and greater market concentration.

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[Abstract] This paper examines the welfare implications of innovation policies in the context of multinational production (MP) and international trade, focusing on the optimal allocation of labor between innovation and production sectors. It explores how such policies are transmitted across countries via the home market effect and terms of trade effect. By accounting for differences in market size and technology levels, the analysis highlights the strategic interactions between nations and their implications for national welfare. The results demonstrate that optimal subsidies for innovation and production depend critically on a country's relative market size and technological position. Additionally, the study analyzes subsidy competition under the Nash equilibrium, revealing its influence on wages, knowledge dependency, and trade patterns. 

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[Abstract] This paper applies the realized exponential generalized autoregressive conditional heteroskedasticity (REGARCH) model to analyze the Nikkei 225 index from 2010 to 2017, utilizing realized variance (RV) and realized range-based volatility (RRV) as high-frequency measures of volatility. The findings show that REGARCH models outperform standard GARCH family models in both in-sample fitting and out-of-sample forecasting, driven by the dynamic information embedded in high-frequency realized measures. Incorporating multiple realized measures within a joint REGARCH framework further enhances model performance. Notably, RRV demonstrates superior predictive power compared to RV, as evidenced by improvements in forecast accuracy metrics. Moreover, the forecasting results remain robust under both rolling-window and recursive evaluation schemes.