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The Dynamic Welfare Cost of Stagnation: An Alternative Measure to the Lucas-Obstfeld Model

Author

Listed:
  • Tatsuyoshi Miyakoshi

    () (Osaka School of International Public Policy (OSIPP), Osaka University)

  • Masakatsu Okubo

    (Graduate School of Systems and Information Engineering,University of Tsukuba)

  • Junji Shimada

    (School of Management, Aoyama Gakuin University)

Abstract

This paper proposes an alternative measure to the Lucas-Obstfeld model to analyze the welfare costs of stagnation, and provides a practical illustration of both the Lucas-Obstfeld model and the alternative model. Compared with the Lucas-Obstfeld model, the alternative model can evaluate: (i) whether the policy was implemented in a timely fashion, (ii) whether the policy cost was expensive compared with the cost of stagnation, and (iii) whether the policy implemented was effective or whether an additional policy is required.

Suggested Citation

  • Tatsuyoshi Miyakoshi & Masakatsu Okubo & Junji Shimada, 2007. "The Dynamic Welfare Cost of Stagnation: An Alternative Measure to the Lucas-Obstfeld Model," Discussion Papers in Economics and Business 07-26, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
  • Handle: RePEc:osk:wpaper:0726
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    More about this item

    Keywords

    Lucas model; dynamic welfare cost; time-varying parameters;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)

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