My dissertation sheds light on the relationship between the shadow value of capital, the market value per capital stock, and investment. When the asset market is efficient, firms’ market value per capital stock, average q, represents the shadow value of capital, marginal q or fundamental q. Average q, however, is observed as different movements from fundamental q because of i) investment adjustment specifications and goods market structure, and ii) investors’ speculative behavior. My first paper emphasizes the importance of adjustment costs and studies the interactions between average q, fundamental q, and investment. The second paper explores the relationship through the structural vector autoregressive model at the macro level. The paper allows market speculation and constructs fundamental q with discounted profit rate along with average q.
The first paper evaluates the importance of investment installment costs in a sticky price model by comparing two different adjustment cost specifications; one depends on the investment-to-capital stock ratio, and the other depends on investment growth. The two adjustment cost specifications are considered, since the former has been adopted in the empirical literature such as such as Hayashi (1982) and the latter has been adopted in the theoretical literature such as Chirinko and Fazzari (1994). There is a stronger positive asset price (or average q) response to a positive technology shock when the adjustment cost depends on investment growth. In addition, the investment growth specification generates a hump shaped response of investment and a semi-hump shaped response of output.
As indicated in Hayashi (1982), higher fundamental q leads to higher investment purchases. Higher shadow value of capital means that additional capital stock creates net profits, enabling firms to increase investment purchases. An efficient asset market implies a close positive relation between average q and fundamental q, and thus higher average q leads to higher investment purchases. Previous literature has focused on average q and investment at the micro level with a single-equation regression model, and the result was not satisfactory. I have conducted empirical research to answer whether investment is sensitive to fundamental q or average q through comparison of impulse responses to a technology shock. In addition, the extent to which technology shocks explain average q fluctuations is studied through forecast error variance decomposition.
My empirical paper has applied the structural vector autoregressive model with the restriction that only technology shocks can alter labor productivity in the long run. Impulse responses to technology shocks indicate that there exists a positive interaction between investment and average q. On the other hand, fundamental q is influenced by investment but not in an adverse direction; fundamental q follows investment growth rate. Furthermore, without having average q in the equation, fundamental q alone cannot be a significant explanatory variable to predict investment. Positive technology shocks are expected to raise firms’ profits, output, and investment. The variance decomposition results suggest that technology shocks account for larger portions of output and investment when average q is used without fundamental q. When fundamental q is included in the estimation, the portion of investment fluctuations caused by technology shocks shrink significantly, which confirms that fundamental q cannot explain investment fluctuations.