15 research outputs found

    톡화정책, 뢀동산 μ‹œμž₯ 및 ꡭ제 자본 흐름에 λŒ€ν•œ λ…Όλ¬Έ

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : μ‚¬νšŒκ³Όν•™λŒ€ν•™ κ²½μ œν•™λΆ€, 2023. 2. μ΄μž¬μ›.This dissertation consists of two articles on monetary poicies and housing markets and one article on international capital flows. Chapter 1 analyzes how the effectiveness of monetary policy shocks are affected by leveraged housing boom period, when housing prices have surged with excessive levels of leverage. Threshold SVAR models are estimated on three small open economies - Norway, Korea, and Canada - by using minimum of the standardized real house price gap and household credit gap as a threshold variable. For all countries, the effects of monetary policy shocks on real house prices and output turn out to be more significant and stronger during the boom regime when the both real house price gap and household credit gap are above the threshold value. Chapter 2 expands the scope of discussion into rental housing markets. In terms of monetary policy transmission mechanisms, the role of homeownership decision channels, where households could decide between mortgaged housing and rental housing, is examined focusing on sticky responses of housing rents to monetary policy shocks. A New Keynesian model incorporated with homeownership channels shows that substitution of mortgaged housing with rental housing after interest rate hikes results in smaller short-term effects of monetary policies but more persistent long-term effects. Rent rigidity, on the other hand, amplfies the short-term effect of monetary policy by suppressing this substitution, but its quantitative effect is limited and temporary. Chapter 3 examines the effectiveness of post-AFC reforms of AFC economies, which had tightened capital controls since the Asian Financial Crisis (AFC) to decrease the volatilities from international capital flow shocks. By classifying ASEAN+3 economies into AFC economies and Non-AFC economies, Bayesian panel VAR models are estimated on three sub-groups: (i) AFC economies in the AFC episodes, (ii) AFC economies in the GFC episodes, and (iii) Non-AFC economies in the GFC episodes. For AFC economies, the negative effects of net capital outflow shocks on real GDP growth rate during the AFC period become weaker during the GFC period. Furthermore, during the GFC episodes, AFC economies are more resilient to net capital outflow shocks than Non-AFC economies. These findings support the effectiveness of post-AFC reforms to strengthen resilience to capital flow shocks in AFC economies.λ³Έ ν•™μœ„λ…Όλ¬Έμ€ 톡화정책 및 뢀동산 μ‹œμž₯에 λŒ€ν•œ 두 개의 μ†Œλ…Όλ¬Έκ³Ό ꡭ제 자본 흐름에 λŒ€ν•œ ν•˜λ‚˜μ˜ μ†Œλ…Όλ¬ΈμœΌλ‘œ 이루어져 μžˆλ‹€. 제1μž₯μ—μ„œλŠ” 뢀동산 가격과 가계 뢀채가 λͺ¨λ‘ μƒμŠΉν•˜λŠ” λ ˆλ²„λ¦¬μ§€ 뢀동산 ν˜Έν™© κ΅­λ©΄ (boom regime)κ³Ό 그렇지 μ•Šμ€ κ΅­λ©΄ (normal regime)μ—μ„œ ν†΅ν™”μ •μ±…μ˜ νš¨κ³Όκ°€ μ–΄λ–€ 차이λ₯Ό κ°–λŠ”μ§€λ₯Ό 비ꡐ뢄석 ν•˜μ˜€λ‹€. 이λ₯Ό μœ„ν•΄ μ‹€μ§ˆ 뢀동산 가격 κ°­κ³Ό 가계 뢀채 갭의 μ΅œμ†Œκ°’μ„ λ¬Έν„± λ³€μˆ˜ (Threshold variable)둜 μ‚¬μš©ν•˜μ—¬ λ…Έλ₯΄μ›¨μ΄, ν•œκ΅­, μΊλ‚˜λ‹€μ˜ 3개 μ†Œκ΅­ 개방 κ²½μ œμ— λŒ€ν•΄ λ¬Έν„± ꡬ쑰적 λ²‘ν„°μžκΈ°νšŒκ·€λͺ¨ν˜• (Threshold SVAR model)을 μΆ”μ •ν•˜μ˜€λ‹€. μΆ”μ • κ²°κ³Ό λͺ¨λ“  κ΅­κ°€μ—μ„œ λ ˆλ²„λ¦¬μ§€ 뢀동산 ν˜Έν™© κ΅­λ©΄ λ™μ•ˆ μ‹€μ§ˆ 뢀동산 가격 및 μƒμ‚°λŸ‰μ— λŒ€ν•œ ν†΅ν™”μ •μ±…μ˜ νš¨κ³Όκ°€ 더 크고 μœ μ˜ν•œ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. 제2μž₯μ—μ„œλŠ” μž„λŒ€ 주택 μ‹œμž₯으둜 λ…Όμ˜λ₯Ό ν™•μž₯ν•˜μ˜€λ‹€. 즉 가계가 금리 변동 이후 λŒ€μΆœμ„ ν†΅ν•œ 주택 λ³΄μœ μ™€ 주택 μž„λŒ€ 쀑 ν•˜λ‚˜λ₯Ό μ„ νƒν•˜λŠ” 것이 κ°€λŠ₯ν•œ 주택 μ†Œμœ  κ²°μ • 채널 (homeownership decision channel)이 톡화정책 전달 κ²½λ‘œμ— λ―ΈμΉ˜λŠ” 영ν–₯을 금리 좩격에 경직적으둜 λ°˜μ‘ν•˜λŠ” 주택 μž„λŒ€λ£Œ (sticky housing rent)λ₯Ό μ€‘μ‹¬μœΌλ‘œ λΆ„μ„ν•˜μ˜€λ‹€. 주택 μ†Œμœ  κ²°μ • 채널을 ν¬ν•¨ν•˜λ„λ‘ ν™•μž₯ν•œ 뉴케인지언 λͺ¨ν˜• (New Keynesian model)을 ν†΅ν•œ 뢄석 κ²°κ³Ό 가계듀이 금리 인상 좩격 이후 λŒ€μΆœμ„ 톡해 κ΅¬μž…ν•œ 주택을 μž„λŒ€ μ£ΌνƒμœΌλ‘œ λŒ€μ²΄ν•˜λŠ” 주택 μ†Œμœ  κ²°μ • 채널은 ν†΅ν™”μ •μ±…μ˜ 단기 효과λ₯Ό μ•½ν™”μ‹œν‚€μ§€λ§Œ μž₯κΈ° νš¨κ³ΌλŠ” κ°•ν™”ν•˜λŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. 반면 비탄λ ₯적 주택 μž„λŒ€λ£ŒλŠ” μž„λŒ€ μ£ΌνƒμœΌλ‘œμ˜ λŒ€μ²΄λ₯Ό λ‹¨κΈ°μ μœΌλ‘œ μ–΅μ œν•˜μ—¬ ν†΅ν™”μ •μ±…μ˜ 단기 효과λ₯Ό κ°•ν™”ν•˜μ˜€μ§€λ§Œ, κ·Έ 영ν–₯은 μΌμ‹œμ μ΄κ³  μ œν•œμ μ΄μ—ˆλ‹€. 제3μž₯μ—μ„œλŠ” μ•„μ‹œμ•„ 금육 μœ„κΈ° κ²½ν—˜ κ΅­κ°€λ“€μ˜ μ™Έν™˜μœ„κΈ° 이후 금육개혁(post-AFC reform)이 ꡭ제 자본 흐름 좩격에 λ”°λ₯Έ 변동성을 μ™„ν™”μ‹œν‚€λŠ”λ° νš¨κ³Όμ μ΄μ—ˆλŠ”μ§€λ₯Ό 싀증 λΆ„μ„ν•˜μ˜€λ‹€. ASEAN+3 ꡭ가듀을 μ•„μ‹œμ•„ 금육 μœ„κΈ°λ₯Ό κ²½ν—˜ν•œ κ΅­κ°€λ“€ (AFC 경제)와 그렇지 μ•Šμ€ κ΅­κ°€λ“€ (λΉ„ AFC 경제)둜 λΆ„λ₯˜ν•œ ν›„, (i) AFC κΈ°κ°„μ˜ AFC 경제, (ii) GFC κΈ°κ°„μ˜ AFC 경제, (iii) GFC κΈ°κ°„μ˜ λΉ„ AFC κ²½μ œλΌλŠ” μ„Έ 개의 μ†Œμ§‘λ‹¨μ— λŒ€ν•΄ λ² μ΄μ§€μ•ˆ νŒ¨λ„ λ²‘ν„°μžκΈ°νšŒκ·€ λͺ¨ν˜• (Bayesian panel VAR model)을 μΆ”μ •ν•˜μ˜€λ‹€. μΆ”μ • κ²°κ³Ό AFC κ²½μ œμ—μ„œ 순자본 유좜 좩격이 κ²½μ œμ„±μž₯λ₯ μ— λ―ΈμΉ˜λŠ” 뢀정적인 영ν–₯은 AFC κΈ°κ°„μ—μ„œ GFC κΈ°κ°„μœΌλ‘œ κ°€λ©΄μ„œ μ•½ν™”λœ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. GFC κΈ°κ°„ λ™μ•ˆ 순자본 유좜 좩격의 경제 μ„±μž₯λ₯ μ— λŒ€ν•œ 뢀정적인 영ν–₯ μ—­μ‹œ AFC κ²½μ œμ—μ„œ λΉ„ AFC κ²½μ œμ— λΉ„ν•΄ 덜 지속적인 κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. μ΄μƒμ˜ κ²°κ³ΌλŠ” μ™Έν™˜μœ„κΈ° 이후 금육 개혁이 ꡭ제 자본 νλ¦„μ˜ 변동성을 μ™„ν™”ν•˜λŠ”λ° νš¨κ³Όμ μ΄μ—ˆμŒμ„ μ‹œμ‚¬ν•œλ‹€.Chapter 1. The effects of monetary policy during leveraged housing booms 1 1.1. Introduction 1 1.2. Data and Empirical Methodologies 6 1.2.1. Data and Empirical Methodologies 6 1.2.2. Reduced-Form Threshold VAR (TVAR) model 11 1.2.3. Structural Identification 14 1.3. Empirical Results 15 1.3.1. Identified Regimes 15 1.3.2. State-Dependent Impulse Response Function 17 1.3.3. Extended models with private consumption and fixed capitals 23 1.4. Robustness Tests 27 1.4.1. Using the household credit gap alone 27 1.4.2. Using the house price gap alone 30 1.4.3. Using the adjusted one-sided HP filter 33 1.5. Conclusion 35 References 38 Appendix 1.A. Data and Sources 40 Appendix 1.B. Model Specification for section 1.4.1 (Household credit gap only) 41 Appendix 1.C. Model Specification for section 1.4.2 (House price gap only) 41 Appendix 1.D. Model Specification for section 1.4.3 (One-sided HP filter) 41 Chapter 2. Homeownership Channels, Rent Stickiness, and Monetary Policy Transmission Mechanisms 42 2.1. Introduction 42 2.2. Data and Empirical Methodologies 47 2.2.1. Data 47 2.2.2. Panel VARX (PVARX) model 50 2.2.3. Responses of Real House Prices and Housing Rents After Monetary Policy Shocks 53 2.2.4. Responses of Nominal House Prices and Housing Rents After Monetary Policy Shocks 55 2.3. The Model and Calibration 56 2.3.1. Unconstrained Households 57 2.3.2. Constrained Households 58 2.3.3. Entrepreneurs and Retailors 60 2.3.4. Housing Supply 62 2.3.5. Interest Rate Rule 64 2.3.6. Equilibrium 64 2.3.7. Calibration 65 2.4. Simulation Results 69 2.4.1. The Role of Rent Stickiness in Different Responses of Housing Rents and House Prices 69 2.4.2. The Role of Homeownership channels in the Monetary Policy Transmission Mechanism 71 2.4.3. The Role of Rent Stickiness in Homeownership Channels 75 2.5. Concluding Remarks 77 References 78 Appendix 2.A. Data and Sources 81 Appendix 2.B. Responses of real housing rents and housing prices under the alternative specification 82 Chapter 3. International Capital Flow Shocks and Economic Crisis in East Asian Countries 83 3.1. Introduction 83 3.2. Related Literature 86 3.3. Data and summary statistics 88 3.4. Cross-country and cross-period difference in the effects of international capital flow shocks for AFC economies 94 3.4.1. Methodology 95 3.4.2. Impulse response to net capital outflow shocks on different sub-groups 98 3.4.3. Impulse response to negative shocks on each component of capital inflows 101 3.5. Conclusion 106 References 109 Appendix 3.A. Data and Sources 111 ꡭ문초둝 113λ°•

    NFT 싀둀와 거래의 κ΄€μ μ—μ„œ

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : λ²•κ³ΌλŒ€ν•™ 법학과(μ§€μ‹μž¬μ‚°μ „κ³΅), 2023. 2. 정상쑰.ꡭ문초둝 λ³Έκ³ μ—μ„œλŠ” μ½”λ‘œλ‚˜19 μ‹œκΈ°μ˜€λ˜ 2020λ…„μœΌλ‘œλΆ€ν„° λΉ„λ‘―ν•˜μ—¬ μ „μ„Έκ³„μ˜ μ‚°μ—… 흐름과 λΆˆκ°€λΆ„μ˜ 관계가 된 NFT와 κ΄€λ ¨ν•˜μ—¬ μ €μž‘κΆŒμ€ 항상 μ΄μŠˆκ°€ λ˜μ—ˆμœΌλ‚˜, 이λ₯Ό λ‹¨μˆœν•œ μ΄μŠˆμ—μ„œ ν™•λŒ€ν•˜μ—¬ μ €μž‘κΆŒ κ΄€μ μ—μ„œ λ°œν–‰μ—μ„œ νŒλ§€κΉŒμ§€μ˜ λ‹¨κ³„λ³„λ‘œ κ²€ν† ν•΄ λ³Ό κΈ°νšŒλŠ” 잘 μ—†μ—ˆλ‹€. NFTλŠ” 기술적으둜 μ΄λ”λ¦¬μ›€μ˜ ERC-721 ν‘œμ€€μ„ 주둜 μ΄μš©ν•˜κ²Œ 되며, μ•”ν˜Έν™” μžμ‚°μœΌλ‘œμ„œ, ꢌ리 증λͺ…μ„œλ‘œμ„œ, 온라인 μž¬ν™”λ‘œμ„œμ˜ 법적 성격을 가지고 μžˆλ‹€. NFT λ°œν–‰μ‹œμ—λŠ” μŠ€λ§ˆνŠΈκ³„μ•½, 메타데이터λ₯Ό μ΄μš©ν•˜μ—¬ λ―ΌνŒ…μ„ μ§„ν–‰ν•˜κ³ , λ‹Ήμ‚¬μžκ°„ ν˜Ήμ€ μ΄μš©μ•½κ΄€μ— 따라 NFT 및 λŒ€μƒ μ €μž‘λ¬Όμ˜ 이용 λ²”μœ„ 등에 λŒ€ν•΄ μ •ν•˜κ²Œ λ˜λŠ”λ°, μ΄λŸ¬ν•œ NFT λ°œν–‰μ— λŒ€ν•œ ν•΄μ™Έ μ‹€λ‘€λ₯Ό λ©”νƒ€λ²„μŠ€, μˆ˜μ§‘ν˜•, μ˜ˆμˆ ν˜•, μ—”ν„°ν…ŒμΈλ¨ΌνŠΈ, κ²Œμž„, μΆœνŒμ—…, λ””νŒŒμ΄ν˜•μ˜ 7개 μœ ν˜•μœΌλ‘œ λ‚˜λˆ„μ–΄ μ‚΄νŽ΄λ³Έλ‹€. 무ꢌ리자 NFT λ―ΌνŒ…μ— λŒ€ν•œ 미ꡭ의 미라λ§₯슀 λŒ€ μΏ μ—”ν‹΄ νƒ€λž€ν‹°λ…Έ μ‚¬κ±΄μ—μ„œμ™€ 같이, 과거의 μ½˜ν…μΈ  μ œμž‘κ³„μ•½μ€ NFTλ₯Ό κ³ λ €ν•˜μ§€ λͺ»ν•˜κ³  μ²΄κ²°λ˜μ—ˆλŠ”λ°”, λ²•μ›μ˜ 해석에 따라 μ°½μž‘λ¬Όμ— λŒ€ν•΄ λ‹Ήμ‚¬μž 쀑 λˆ„κ΅¬μ—κ²Œ μ €μž‘κΆŒ 등이 귀속될지 μ •ν•΄μ§ˆ κ²ƒμœΌλ‘œ 보인닀. λ˜ν•œ, 무ꢌ리자 λ―ΌνŒ…μ‹œ λŒ€μƒ μ €μž‘λ¬Ό λ³΅μ œκ°€ 일어날 수 μžˆλŠ”λ° μ €μž‘κΆŒ μ œν•œ κ·œμ • 적용의 κ°€λŠ₯성에 λŒ€ν•΄μ„œλ„ κ³ λ €ν•΄ λ³Ό 수 μžˆκ² λ‹€. NFTλŠ” μœ ν†΅μ„ 본질적인 μ „μ œλ‘œ ν•˜μ—¬ μƒμ„±λœ κ²½μš°κ°€ λŒ€λΆ€λΆ„μ΄λ‹€. NFT에 μ „ν˜•μ μΈ 민법상 μ†Œμœ κΆŒ 및 μ €μž‘κΆŒ κ°œλ…μ΄ μ μš©λ˜μ§€ μ•ŠλŠ” κ²½μš°κ°€ λ§Žμ€λ°, NFT λ°œν–‰μž, λ³΄μœ μžλ“€μ΄ κ°–λŠ” κΆŒλ¦¬μ— λŒ€ν•΄μ„œλ„ 연ꡬ가 ν•„μš”ν•˜λ‹€. λŒ€μƒ μ €μž‘λ¬Όμ„ 자유둭게 μƒμ—…ν™”ν•¨μœΌλ‘œμ¨ κ°€μΉ˜λ₯Ό μ°½μΆœν•˜λŠ” BAYC 거래 ꡬ쑰 및 κ·Έ 약관도 μ‚΄νŽ΄λ³Ό κ°€μΉ˜κ°€ μžˆλ‹€. κ΅­λ‚΄μ™Έ λŒ€ν‘œμ μΈ NFT λ§ˆμΌ“ν”Œλ ˆμ΄μŠ€μ˜ 약관을 μ‚΄νŽ΄λ³΄λ©° 주둜 μ†Œμœ  관계 및 μ§€μ‹μž¬μ‚°κΆŒμ˜ κΆŒλ¦¬κ΄€κ³„μ— λŒ€ν•΄μ„œ κ·œμœ¨ν•˜κ³  μžˆλŠ” 일반적인 κ·œμ •λ“€μ„ 확인할 수 있고, 이듀 약관은 DMCAλ‚˜ μ €μž‘κΆŒλ²•μƒ μ˜¨λΌμΈμ„œλΉ„μŠ€μ œκ³΅μžμ˜ μ±…μž„ κ·œμ •μ˜ 적용이 κ°€λŠ₯함을 μ „μ œλ‘œ ν•˜κ³  μžˆλ‹€λŠ” 점도 확인할 수 μžˆλ‹€. NFT λ§ˆμΌ“ν”Œλ ˆμ΄μŠ€ μš΄μ˜μžμ—κ²Œ μ˜¨λΌμΈμ„œλΉ„μŠ€μ œκ³΅μžμ˜ 법적 μ±…μž„ 적용이 κ°€λŠ₯ν•œμ§€μ™€ κ΄€λ ¨ν•˜μ—¬μ„œ 쀑ꡭ ν•­μ €μš°μΈν„°λ„·λ²•μ›μ˜ 졜근 νŒκ²°μ„ λΆ„μ„ν•˜κ³  ν•œκ΅­μ—μ„œμ˜ ν™œμš© κ°€λŠ₯성도 생각해 λ³Έλ‹€.제 1 μž₯ μ„œλ‘  1 제 1 절 μ—°κ΅¬μ˜ λͺ©μ  1 제 2 절 μ—°κ΅¬μ˜ λ²”μœ„ 2 제 2 μž₯ NFT의 κ°œλ…κ³Ό μ‹€λ‘€ 3 제 1 절 NFT의 κ°œλ… 및 성격 3 1. NFT의 기술적 κ°œλ… 4 2. NFT의 법적 성격 7 제 2 절 ν•΄μ™Έ NFT λ°œν–‰ 사둀 및 NFT의 μ’…λ₯˜ 12 1. λ©”νƒ€λ²„μŠ€μ™€ NFT 12 2. μˆ˜μ§‘ν˜• NFT 15 3. 예술과 NFT 18 4. μ—”ν„°ν…ŒμΈλ¨ΌνŠΈμ™€ NFT 22 5. κ²Œμž„κ³Ό NFT 27 6. μΆœνŒμ—…κ³„μ—μ„œμ˜ NFT λ°œν–‰ 사둀 31 7. λ””νŒŒμ΄ν˜• NFT 32 제 3 μž₯ NFT λ°œν–‰μœ ν†΅κ³Ό μ €μž‘κΆŒμ˜ 관계 34 제 1 절 λ°œν–‰ κ³Όμ •μ—μ„œμ˜ μ €μž‘κΆŒ 문제 34 1. 슀마트 κ³„μ•½μ˜ 체결 34 2. NFT의 λ°œν–‰ 절차 37 3. NFT λ°œν–‰μ‹œ λ°œν–‰μžμ˜ ꢌ리 38 4. 무ꢌ리자의 λ―ΌνŒ… 40 제 2 절 μœ ν†΅ κ³Όμ •μ—μ„œμ˜ μ €μž‘κΆŒ 문제 46 1. NFT 거래 λ‹Ήμ‚¬μž μ‚¬μ΄μ˜ ꢌ리 문제 46 2. NFT λ°œν–‰μžμ™€ 보유자의 ꢌ리 47 3. NFT ν”Œλž«νΌμ˜ μ•½κ΄€ 규제 적용 λ²”μœ„: NFT λ§ˆμΌ“ν”Œλ ˆμ΄μŠ€μ˜ μ•½κ΄€ 비ꡐ 63 4. NFT 거래 ν”Œλž«νΌμ˜ μ €μž‘κΆŒλ²•μƒ μ˜¨λΌμΈμ„œλΉ„μŠ€μ œκ³΅μž 법적 μ±…μž„ 적용 μ—¬λΆ€ 73 제 4 μž₯ κ²°λ‘  81 μ°Έκ³ λ¬Έν—Œ 83 Abstract 88석

    Timing dependent Trade-offs in monetary policy shocks

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    ν•™μœ„λ…Όλ¬Έ (석사) -- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : μ‚¬νšŒκ³Όν•™λŒ€ν•™ κ²½μ œν•™λΆ€, 2020. 8. κΉ€μ†Œμ˜.This paper estimates a nonlinear SVAR model to study the trade-offs in leaning against the wind in regimes characterized by the Bubble vs Normal housing market. This is achieved by combining TVAR approach and SVAR Identification. I find that in the Bubble regime where house price to income ratio shows an upward trend, leaning against wind policy faces more stronger and prolonged trade-offs between output and house prices. In particular, one year after one percentage point monetary policy shock, the percentage point change in house price growth rate(%p) per percentage loss in output(%) is 3.54 in the Normal regime and 1.53 in the Bubble regime. That is, central banks should take more output loss to curb house prices during house price peaks. Meanwhile, I find that the ratio of house price loss relative to output is 2.58 in a single regime model which does not allow a regime shift. This implies that if one dose not consider the threshold effect in leaning against the wind, there is a serious risk of either under or over estimation.λ³Έκ³ λŠ” 뢀동산 μ‹œμž₯의 버블 μœ λ¬΄μ— λ”°λ₯Έ 역풍정책(Leaning against the wind)의 상좩관계(Trade-off)λ₯Ό λΉ„κ΅ν•˜κΈ° μœ„ν•΄ Bubble vs Normal regime의 λΉ„μ„ ν˜• SVAR λͺ¨ν˜•μ„ μΆ”μ •ν•˜μ˜€λ‹€. ꡬ체적으둜 TVAR λͺ¨ν˜•κ³Ό ꡬ쑰적 식별법 (SVAR Identification)을 κ²°ν•©ν•œ T-SVAR 방법둠을 μ‚¬μš©ν•˜μ˜€μœΌλ©°, μΆ”μ •κ²°κ³Ό μ†Œλ“ λŒ€λΉ„ 뢀동산 가격 λΉ„μœ¨μ΄ 상방 μΆ”μ„Έλ₯Ό λ³΄μ΄λŠ” Bubble regimeμ—μ„œ 역풍정책이 더 κ°•λ ₯ν•˜κ³  지속적인 상좩관계에 μ§λ©΄ν•˜λŠ” κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬λ‹€. ꡬ체적으둜 1%p μ˜ˆμΈ‘ν•˜μ§€ λͺ»ν•œ 톡화정책 좩격에 λŒ€ν•΄ 4λΆ„κΈ° 이후 GDP 1% κ°μ†Œ λŒ€λΉ„ 뢀동산 가격 μ¦κ°€μœ¨ κ°μ†ŒλΆ„(%p)은 Normal regimeμ—μ„œ 3.54, Bubble regimeν•˜μ—μ„œ 1.53으둜 μΆ”μ •λ˜μ—ˆλ‹€. μ΄λŠ” 뢀동산 μ‹œμž₯에 이미 버블이 ν˜•μ„±λœ 이후 μ‹œμ λΆ€ν„°λŠ” 쀑앙은행이 λ™μΌν•œ 뢀동산 가격 ν•˜λ½ 효과λ₯Ό μœ„ν•΄ 더 λ§Žμ€ μ„±μž₯λ₯ μ˜ 희생을 κ°μˆ˜ν•΄μ•Όν•¨μ„ μ‹œμ‚¬ν•œλ‹€. ν•œνŽΈ Regime shift λ₯Ό κ³ λ €ν•˜μ§€ μ•Šμ€ Single regime λͺ¨ν˜•μ—μ„œλŠ” 동 μˆ˜μΉ˜κ°€ 2.58둜 μΆ”μ •λ˜μ—ˆλ‹€. μ΄λŠ” μ—­ν’μ •μ±…μ˜ 효과 및 상좩관계 뢄석에 μžˆμ–΄ 뢀동산 μ‹œμž₯에 λ”°λ₯Έ μ •μ±… 타이밍을 κ³ λ €ν•˜μ§€ μ•ŠλŠ” 경우 κ³Όμ†Œ/κ³ΌλŒ€ν‰κ°€μ˜ μœ„ν—˜μ΄ μ‘΄μž¬ν•¨μ„ μ‹œμ‚¬ν•œλ‹€.1 Introduction 2 2 Literature Review 6 3 Methodology 12 3.1 Threshold Vector Autoregression 12 3.2 SVAR Identification 13 3.2.1 Choice of variables 13 3.2.2 Identification 15 4 Empirical Results 21 4.1 Data Description 21 4.2 Effects of a monetary policy shock 23 5 Robustness Check 29 5.1 Single Regime Model 29 5.2 Cholesky Decomposition 33 6 Conclusion 39 Appendix A 48 Appendix B 49 Appendix C 51Maste

    Fake News and Fact Check News Differences: Focusing on News Usage, Perception, and Literacy in Multi-Media Environments

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    λ‹€μ€‘μ˜ λ―Έλ””μ–΄ ν™˜κ²½μ—μ„œ μˆ˜μš©μžλŠ” κ°€μ§œλ‰΄μŠ€μ™€ 팩트체크λ₯Ό μ‹œκ°„μ˜ 연속선상에 두고 μ—„κ²©νžˆ 뢄리해 순차 적으둜 λ…ΈμΆœν•˜μ§€λŠ” μ•ŠλŠ”λ‹€. 또, λ‰΄μŠ€λ₯Ό λͺ…λ°±νžˆ κ°€μ§œλ‘œ, λͺ…λ°±νžˆ μ‚¬μ‹€λ‘œ κ΅¬λΆ„ν•˜μ§€λ„ μ•ŠλŠ”λ‹€. λ‹€λ§Œ λ‰΄μŠ€ ν”Œ λž«νΌμ— 따라 λ‰΄μŠ€μ™€ κ°€μ§œλ‰΄μŠ€λ₯Ό μƒλŒ€μ μœΌλ‘œ κ΅¬λΆ„ν•˜κ±°λ‚˜, 주된 λ―Έλ””μ–΄ 이용경둜λ₯Ό 톡해 λ‰΄μŠ€μ— λ…ΈμΆœλ  뿐이닀. 이에 λ³Έ μ—°κ΅¬λŠ” λ‹€μ–‘ν•œ λ―Έλ””μ–΄ 채널을 톡해 λ™μ‹œμ  Β· λΉ„λ™μ‹œμ μœΌλ‘œ λ…ΈμΆœλ˜λŠ” κ°€μ§œλ‰΄μŠ€μ™€ 팩트체 크의 집합적 νŒ¨ν„΄μ„ κ³ λ €ν•˜κ³  λ‰΄μŠ€ λ…ΈμΆœ 집합 κ°„μ˜ 차이λ₯Ό μ‚΄νŽ΄λ³Ό ν•„μš”κ°€ μžˆλ‹€κ³  λ³΄μ•˜λ‹€. 뢄석을 μœ„ν•œ 데이터 μˆ˜μ§‘μ€ ν•œκ΅­μ‚¬νšŒκ³Όν•™μ‘°μ‚¬(KAMOS)λ₯Ό 톡해 μ§€λ‚œ 2019λ…„ 5μ›” μ „κ΅­ 만 18μ„Έ 이상 성인남녀 1483λͺ…을 λŒ€μƒμœΌλ‘œ μ„€λ¬Έμ‘°μ‚¬ν•˜μ˜€λ‹€. λΆ„μ„κ²°κ³ΌλŠ” λ‹€μŒκ³Ό κ°™λ‹€. 첫째, μ‚¬λžŒλ“€μ˜ 성별, μ—°λ Ή, ν•™λ ₯, μ •μΉ˜ μ„±ν–₯은 κ°€μ§œλ‰΄μŠ€μ™€ 팩트체크 λ‰΄μŠ€ λ…ΈμΆœμ— 영ν–₯을 λ―ΈμΉ˜λŠ” μ£Όμš”ν•œ 개인적 μ†μ„±μ΄μ—ˆλ‹€. λ‘˜μ§Έ, κ°€μ§œλ‰΄μŠ€μ™€ 팩트체크 λ‰΄μŠ€μ˜ λ…ΈμΆœμ€ 주된 λ―Έλ””μ–΄ μ΄μš©νŒ¨ν„΄κ³Ό κ΄€λ ¨ μžˆμ—ˆλ‹€. μ…‹μ§Έ, κ°€μ§œλ‰΄μŠ€μ™€ 팩트체크 λ‰΄μŠ€ λ…ΈμΆœμ— 따라 λ‰΄μŠ€μ‹ λ’°μ™€ κ°€μ§œλ‰΄μŠ€μ˜ 심각성, 팩트체크의 μœ μš©μ„±μ— λŒ€ν•œ 인식 정도가 λ‹¬λžλ‹€. λ„·μ§Έ, λ‰΄μŠ€ 리터리 μ‹œκ°€ λ†’μ„μˆ˜λ‘ κ°€μ§œλ‰΄μŠ€μ™€ 팩트체크 λ‰΄μŠ€ λ…ΈμΆœμ€ μ¦κ°€ν•˜μ˜€λ‹€. μ—°κ΅¬μ˜ κ²°κ³Όλ₯Ό 톡해 λ‹€μ€‘μ˜ μ±„λ„λ‘œ λ™μ‹œ 적으둜 λ…ΈμΆœλ˜λŠ” κ°€μ§œλ‰΄μŠ€μ™€ 팩트체크의 수용이 μ–΄λ– ν•œ 인식 차이λ₯Ό λ³΄μ΄λŠ”μ§€ μ‚΄νŽ΄λ΄„μœΌλ‘œμ¨ λΉ„νŒμ μΈ λ‰΄μŠ€ 이용λŠ₯λ ₯을 ν•¨μ–‘ν•˜κΈ° μœ„ν•œ κ΅μœ‘μ „λž΅κ³Ό ν•¨κ»˜ μ €λ„λ¦¬μ¦˜μ˜ λ°œμ „ λ°©ν–₯에 λŒ€ν•΄μ„œλ„ λ…Όμ˜ν•  수 μžˆμ„ 것 이닀.N
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