Innovative methods for modeling of scale invariant processes

This paper presents some innovative methods for modeling discrete scale invariant (DSI) processes and evaluation of corresponding parameters. For the case where the absolute values of the increments of DSI processes are in general increasing, we consider some moving sample variance of the increments and present some heuristic algorithm to characterize successive scale intervals. This enables us to estimate scale parameter of such DSI processes. To present some superior structure for the modeling of DSI processes, we consider the possibility that the variations inside the prescribed scale intervals show some further self-similar behavior. Such consideration enables us to provide more efficient estimators for Hurst parameters. We also present two competitive estimation methods for the Hurst parameters of self-similar processes with stationary increments and prove their efficiency. Using simulated samples of some simple fractional Brownian motion, we show that our estimators of Hurst parameter are more efficient as compared with the celebrated methods of convex rearrangement and quadratic variation. Finally we apply the proposed methods to evaluate DSI behavior of the S&P500 indices in some period.     

Identification of Nonseparable Triangular Models With Discrete Instruments

We study the identification through instruments of a nonseparable function that relates a continuous outcome to a continuous endogenous variable. Using group and dynamical systems theories, we show that full identification can be achieved under strong exogeneity of the instrument and a dual monotonicity condition, even if the instrument is discrete. When identified, the model is also testable. Our results therefore highlight the identifying power of strong exogeneity when combined with monotonicity restrictions.     

CREDIT CONSTRAINTS AND THE CYCLICALITY OF R&D INVESTMENT: EVIDENCE FROM FRANCE

We use a French firm‐level data set containing 13,000 firms over the period 1994–2004 to analyze the relationship between credit constraints and firms’ R&D behavior over the business cycle. Our main results can be summarized as follows: (i) R&D investment is countercyclical without credit constraints, but it becomes procyclical as firms face tighter credit constraints; (ii) this result is only observed for firms in sectors that depend more heavily upon external finance, or that are characterized by a low degree of asset tangibility; (iii) in more credit‐constrained firms, R&D investment plummets during recessions but does not increase proportionally during upturns.     

TRANSFERABLE CONTROL

In this paper, we introduce the notion of transferable control, defined as a situation where one party (the principal, say) can transfer control to another party (the agent) but cannot commit herself to do so. One theoretical foundation for this notion builds on the distinction between formal and real authority introduced by Aghion and Tirole, in which the actual exercise of authority may require noncontractible information, absent which formal control rights are vacuous. We use this notion to study the extent to which control transfers may allow an agent to reveal information regarding his ability or willingness to cooperate with the principal in the future. We show that the distinction between contractible and transferable control can drastically influence how learning takes place: with contractible control, information about the agent can often be acquired through revelation mechanisms that involve communication and message‐contingent control allocations; in contrast, when control is transferable but not contractible, it can be optimal to transfer control unconditionally and learn instead from the way in which the agent exercises control. (JEL: C70, C72, D23, L2)