This research paper intends to redefine and extend the concept of ‘optimal li-quidity’ discussed in Han and Lee (2012). For this purpose, we have distinguished between liquidity held by households and liquidity held by firms following Levhari and Patinkin (1968) and Yoo and Pyo (1986). Han and Lee (2012) have revised the ‘money-in-utility’ model by Walsh (2012) and derived the relationship between liquidity and consumption. In the present paper, we have extended Han and Lee (2012) to a ‘money-in-utility-and-production’ model. We have specified a DSGE model in which liquidity serves for both household utility and production input and have conducted the impulse-response analysis. The impulse-responses of most of important variables from the shock of TFP increase are consistent with the results of Bhattacharjee and Thoenissen (2007). On the other hand, the policy interest rate shows a hump-shaped impulse-response, which is consistent with the impulse response of monetary expansion in the cash-in-advance model. In addition, the increase in money supply has produced a kind of crowding-out effect reducing the share of liquidity held by firms. The main policy implication of our model is that not only the absolute level of optimal liquidity but also the relative distribution of the liquidity between households and firms are important determinant for economic growth and stability. In order to validate this proposi-tion, we have conducted a panel regression analysis and have empirically verified the proposition that the relatively higher share of liquidity held by firms would contribute to both GDP growth and its stability.  

 CONTENTS

 Executive Summary

 I. Introduction

 II. A Model of Optimal Liquidity and Consumption-Investment Decision
 1. Representative Firm
 2. Representative Consumer
 3. Credit Bank
 4. Government
 5. A Liquidity Growth Rule
 6. Solution and Calibration
 7. Impulse Responses

 III. Empirical Implications from the OECD Flow-of-funds Data
 1. Summary Statistics from Selected OECD Countries’ Data (1995-2012)
 2. A Regression Analysis

 IV. Summary and Conclusion

 References

 Appendix