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This methodological paper presents a class of stochastic processes with appealing properties for theoretical or empirical work in finance and macroeconomics, the "linearity-generating" class. Its key property is that it yields simple exact closed-form expressions for stocks and bonds, with an arbitrary number of factors. It operates in discrete and continuous time. It has a number of economic modeling applications. These include macroeconomic situations with changing trend growth rates, or stochastic probability of disaster, asset pricing with stochastic risk premia or stochastic dividend growth rates, and yield curve analysis that allows flexibility and transparency. Many research questions may be addressed more simply and in closed form by using the linearity-generating class.
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Despite the availability of more sophisticated methods, a popular way to estimate a Pareto exponent is still to run an OLS regression: log(Rank)=a-b log(Size), and take b as an estimate of the Pareto exponent. The reason for this popularity is arguably the simplicity and robustness of this method. Unfortunately, this procedure is strongly biased in small samples. We provide a simple practical remedy for this bias, and propose that, if one wants to use an OLS regression, one should use the Rank-1/2, and run log(Rank-1/2)=a-b log(Size). The shift of 1/2 is optimal, and reduces the bias to a leading order. The standard error on the Pareto exponent zeta is not the OLS standard error, but is asymptotically (2/n)^(1/2) zeta. Numerical results demonstrate the advantage of the proposed approach over the standard OLS estimation procedures and indicate that it performs well under dependent heavy-tailed processes exhibiting deviations from power laws. The estimation procedures considered are illustrated using an empirical application to Zipf's law for the U.S. city size distribution.
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This paper presents a unified framework for understanding the determinants of both CEO incentives and total pay levels in competitive market equilibrium. It embeds a modified principal-agent problem into a talent assignment model to endogenize both elements of compensation. The model's closed form solutions yield testable predictions for how incentives should vary across firms under optimal contracting. In particular, our calibrations show that the negative relationship between the CEO's effective equity stake and firm size is quantitatively consistent with efficiency and need not reflect rent extraction. Our model and data both also imply that the dollar change in wealth for a percentage change in firm value, scaled by annual pay, is independent of firm size. This may render it an attractive incentive measure as it is comparable between firms and over time. The theory also predicts a positive relationship between pay volatility and firm volatility, and that risk and effort affect total pay along the cross-section but not in the aggregate. Finally, we demonstrate that incentive compensation is effective at solving large agency problems, such as selecting corporate strategy, but smaller issues such as perk consumption are best addressed through direct monitoring.
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In cross-sectional data sets from ten credit markets, we find that middle-aged adults borrow at lower interest rates and pay fewer fees relative to younger and older adults. Fee and interest payments are minimized around age 53. The measured effects are not explained by observed risk characteristics. We discuss several leading factors that may contribute to these effects, including age-related changes in experience and cognitive function, selection effects, and cohort effects.
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