Capital Market, Severity of Business Cycle, and Probability of an Economic Downturn

Tharavanij, Piyapas (2007): Capital Market, Severity of Business Cycle, and Probability of an Economic Downturn.

Abstract

This paper investigates the relationships of capital market, severity of economic contraction, and probability of an economic downturn. The finding supports a theoretical prediction that countries with more advanced capital markets would face less severe business cycle output contraction, and a lower chance of an economic downturn. The results hold even after controlling for other relevant variables, country specific effects, and state dependences. However, the marginal effects are small. Results are generated using panel estimation technique with panel data from 44 countries covering the years 1975 through 2004.

• Item Type: MPRA Paper
• Institution: Monash University, Dept. of Economics
• Language: English
• Keywords: business cycle; capital market; financial development; financial structure; panel data; market-based; bank-based
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy; G - Financial Economics > G0 - General > G00 - General; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C35 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions; G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C34 - Truncated and Censored Models ; Switching Regression Models

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