Recent research using establishment and firm level data has raised a variety of conceptual and measurement questions regarding our understanding of aggregate productivity growth. 1 Several key, related findings are of interest. First, there is large scale, ongoing reallocation of outputs

This study investigates whether market-wide liquidity is a state variable important for asset pricing. We find that expected stock returns are related cross-sectionally to the sensitivities of returns to fluctuations in aggregate liquidity. Our monthly liquidity measure, an average of individual

to be used in a nearest-neighbor framework. Making use of noisy optical flow measurements is the key challenge, which is addressed by treating optical flow not as precise pixel displacements, but rather as a spatial pattern of noisy measurements which are carefully smoothed and aggregated to form our spatio

the late 1970s and early 1980s. Methodologically, I approach this question in two steps. In the first step, I estimate a production function to obtain a measure of plant productivity. I estimate the production function semiparametrically to correct for the presence of selection and simultaneity biases

Modern economies experience substantial fluctuations in aggregate output and employment. In recessions, employment falls and unemployment rises. In the years immediately after a recession, the labor market is slackunemployment remains high and the vacancy rate and other measures of employer

We propose an optimization approach to flow control where the objective is to maximize the aggregate source utility over their transmission rates. We view network links and sources as processors of a distributed computation system to solve the dual problem using gradient projection algorithm

, and that aggregating streams of such traffic typically intensifies the self-similarity (“burstiness”) instead of smoothing it. Our conclusions are supported by a rigorous statistical analysis of hundreds of millions of high quality Ethernet traffic measurements collected between 1989 and 1992, coupled with a

We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. While

be measured more or less straightforwardly at a eiven site, but it is difficult to aggregate these changes regionally and globallv. In contrast to analvses of human al-A11 organisms modify their environment, reasonably well quantified; all are ongoing, teraiion of the global carbon cycle, we and humans

performance measures with the inherently scalar way in which EAs reward individual performance, i.e., number of offspring. In this review, current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a