経済学部学会研究会について
法政大学経済学部学会では定期的に研究会を開催しています。
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開催予定および最近開催した研究会
【第1回開催】日時：2026年06月06日（土）15:30～17:00（1時間半程度）
| 場所 | 市ヶ谷キャンパス ボアソナードタワー25階・会議室C |
|---|---|
| 報告題目 | A Unified Framework for Equilibrium Selection in DSGE Models |
| 報告者 | 岡野 光洋 氏 所属：大阪学院大学経済学部 |
| 使用言語 | 日本語 |
| 要旨 | This paper characterizes DSGE models as fixed-point selection devices for self-referential economic specifications. We formalize this structure as (S,T,Π): specification, self-referential operator, and equilibrium selector. The framework applies to any DSGE model through compositional pipelines where specifications are transformed, fixed points computed, and equilibria selected. We provide formal results and computational implementation for linear rational-expectations systems, reinterpreting Blanchard-Kahn conditions as a specific selection operator and verifying that standard solution methods (such as QZ decomposition and OccBin) realize this operation. We show that alternative selectors (minimal-variance, fiscal anchoring) become available under indeterminacy, revealing selection as a policy choice rather than a mathematical necessity. Our framework reveals the formal structure underlying DSGE solution methods, enabling programmatic verification and systematic comparison of selection rules. |
| 研究会担当 | 倪 彬, 坪田 建明 |
| 照会先 | 宮﨑 憲治（miya_ken[at]hosei.ac.jp;[at]を@に変換して下さい） |
【第2回開催】日時：2026年06月30日（火）17:00～18:30（1時間半程度）
| 場所 | 市ヶ谷キャンパス ボアソナードタワー19階・会議室D |
|---|---|
| 報告題目 | Artificial Intelligence and the Supply of Skills: Evidence from College Major Admissions in China |
| 報告者 | Carl Lin (Associate Professor) 所属：Faculty of Economics, Bucknell University (USA) |
| 使用言語 | 英語 |
| 要旨 | How does artificial intelligence (AI) reshape the supply of skills before individuals enter the labor market? We study how AI influences college major admissions in China, where a centralized system allows shifts in educational demand to be directly observed. Using a novel measure of major-level AI exposure, we find that increases in exposure lead to declines in both enrollment and admission thresholds in affected majors. A one-standard-deviation increase reduces enrollment by 0.8 percent and lowers minimum admission scores and rank thresholds by 0.13 and 0.05 standard deviations, respectively. Moving from the 25th to the 75th percentile of exposure decreases enrollment by approximately 1.2 students per admission cell, lowers admission scores by 0.06 standard deviations, and reduces rank thresholds by 2.7 percentile points. While these effects are modest at an annual frequency, they accumulate over time, implying a gradual but economically meaningful reallocation of students across fields of study rather than discrete shifts. The responses are concentrated in non-elite institutions and in majors with immediate task exposure, whereas top-tier universities and fields with stronger complementarities exhibit more muted adjustments. Additional evidence suggests that these patterns operate through changes in employment expectations and information attention. |
| 研究会担当 | 倪 彬, 坪田 建明 |
| 照会先 | 馬 欣欣（xxma[at]hosei.ac.jp;[at]を@に変換して下さい） |
