Stock-Returns and Inflation in a Principal-Agent Economy

We study a monetary in which final goods sell on spot markets, while labor and dividends sell through contracts. Firms and workers confuse absolute and relative price changes: A positive price-level shock makes sellers think they are producing better goods than they really are. They split this apparent windfall with workers who get a higher real wage. Hence, unexpected inflation shifts real income from firms (the principals) to workers (the agents) and thereby lowers stock-returns.MONEY SUPPLY ; PRICES ; STOCKS

The IT Revolution and the Stock Market.

A new technology or product is often developed by the single entrepreneur. Whether he reaches the initial public offering stage or is acquired by a listed firm, it takes time for the innovator to add value to the stock market. Indeed, the innovation may, at first, reduce the market's value because some firms--usually large or old--will cling to old technologies that have lost their momentum. This paper argues that (a) the market declined in the late 1960s because it felt that the old technologies either had lost their momentum or would give way to IT, and that (b) IT innovators boosted the stock market's value only in the 1980s. If the stock market provides a forecast of future events, then the recent dramatic upswing represents a rosy estimate about growth in future profits for the economy. This translates into a forecast of higher output and productivity growth, holding other things equal (such as capital's share of income).INFORMATION TECHNOLOGY ; STOCK MARKET

Vintage Capital and Equality

If machines are indivisible, a vintage capital model nust give rise to income inequality. If new machines are always better than old ones and if society cannot provide everyone with a new machine all of the time, inequality will result. I explore this mechanism in detail.CAPITAL ; MACHINES

The IT Revolution and the Stock Market.

Technological progress comes in waves. The birth of information technology (IT) may herald the start of a Third Industrial Revolution. This paper argues that (a) the market declined in the late 1960s because it felt that the old technologies either had lost their momentum or would give way to IT, and that (b) IT innovators boosted the stock market's value only in the 1980s.STOCK MARKET ; INFORMATION ; TECHNOLOGY

Maximum likelihood estimation of cloud height from multi-angle satellite imagery

We develop a new estimation technique for recovering depth-of-field from multiple stereo images. Depth-of-field is estimated by determining the shift in image location resulting from different camera viewpoints. When this shift is not divisible by pixel width, the multiple stereo images can be combined to form a super-resolution image. By modeling this super-resolution image as a realization of a random field, one can view the recovery of depth as a likelihood estimation problem. We apply these modeling techniques to the recovery of cloud height from multiple viewing angles provided by the MISR instrument on the Terra Satellite. Our efforts are focused on a two layer cloud ensemble where both layers are relatively planar, the bottom layer is optically thick and textured, and the top layer is optically thin. Our results demonstrate that with relative ease, we get comparable estimates to the M2 stereo matcher which is the same algorithm used in the current MISR standard product (details can be found in [IEEE Transactions on Geoscience and Remote Sensing 40 (2002) 1547--1559]). Moreover, our techniques provide the possibility of modeling all of the MISR data in a unified way for cloud height estimation. Research is underway to extend this framework for fast, quality global estimates of cloud height.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS243 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A low-order decomposition of turbulent channel flow via resolvent analysis and convex optimization

We combine resolvent-mode decomposition with techniques from convex optimization to optimally approximate velocity spectra in a turbulent channel. The velocity is expressed as a weighted sum of resolvent modes that are dynamically significant, non-empirical, and scalable with Reynolds number. To optimally represent DNS data at friction Reynolds number $2003$, we determine the weights of resolvent modes as the solution of a convex optimization problem. Using only $12$ modes per wall-parallel wavenumber pair and temporal frequency, we obtain close agreement with DNS-spectra, reducing the wall-normal and temporal resolutions used in the simulation by three orders of magnitude