A complete asymptotic series for the autocovariance function of a long memory process

An infinite-order asymptotic expansion is given for the autocovariance function of a general stationary long-memory process with memory parameter d∈(−1/2,1/2)$d\in (-1/2,1/2)$. The class of spectral densities considered includes as a special case the stationary and invertible ARFIMA(p,d,q$p,d,q$) model. The leading term of the expansion is of the order O(1/k^1−2d)$O(1/k^{1-2d})$, where k$k$ is the autocovariance order, consistent with the well known power law decay for such processes, and is shown to be accurate to an error of O(1/k^3−2d)$O(1/k^{3-2d})$. The derivation uses Erdélyi’s [Erdélyi, A., 1956. Asymptotic Expansions. Dover Publications, Inc, New York] expansion for Fourier-type integrals when there are critical points at the boundaries of the range of integration - here the frequencies {0,2π}$\{0,2\pi \}$. Numerical evaluations show that the expansion is accurate even for small k$k$ in cases where the autocovariance sequence decays monotonically, and in other cases for moderate to large k$k$. The approximations are easy to compute across a variety of parameter values and models.     

Inventory Reduction and Productivity Growth: A Comparison of Japanese and US Automotive Sectors

This study assesses the inventory and productivity performance of the Japanese and US automotive industries in recent decades. Within each country we distinguish between vehicle assemblers and parts suppliers. In Japan, assemblers and suppliers made dramatic inventory reductions and productivity gains, particularly during the 1970s. By comparison, we find an unbalanced pattern for the United States: American assembly plants have been streamlined, but parts suppliers have stagnated. In both countries our findings suggest a strong association between inventory reduction and productivity growth © 1997 John Wiley & Sons, Ltd.     

Industry learning environments and the heterogeneity of firm performance

This paper characterizes interindustry heterogeneity in rates of learning‐by‐doing, and examines how industry learning rates are connected with firm performance. Using plant‐level data from the U.S. manufacturing sector, we measure the industry learning rate as the coefficient on cumulative output in a production function. We find that learning rates vary considerably among industries and are higher in industries with greater R&D, advertising, and capital intensity. More importantly, we find that higher rates of learning are associated with wider dispersion of Tobin's q and profitability among firms in the industry. These findings suggest that learning intensity represents an important characteristic of the industry environment that affects the range of firm performance. Copyright © 2009 John Wiley & Sons, Ltd.     

How Budget Deficits Cause Trade Deficits: The Simple Analytics

A supply and demand framework is used to clarify the links between budget deficits, exchange rates, and the trade deficit.     

Production frontier methodologies and efficiency as a performance measure in strategic management research

The measurement of corporate performance is central to strategic management research. A common objective of this research is to identify top performers in an industry and their sources of competitive advantage. Despite this focus on best firms and practices, most researchers utilize statistical methods that identify average effects in a sample, and they assess a single performance dimension while ignoring other relevant dimensions. Emphasis on purely financial measures can overlook the fact that a firm's efficiency in transforming resources has been shown as a major source of competitive advantage. In this article we demonstrate how frontier methodologies, such as Data Envelopment Analysis and the Stochastic Frontier approach, can address these challenges. We provide an illustration based on longitudinal data from U.S. and Japanese automobile producers. Copyright © 2013 John Wiley & Sons, Ltd.     

The learning curve,technology barriers to entry,and competitive survival in the chemical processing industries

This paper evaluates entry and survival rates in a sample of 39 chemical product industries. The analysis focuses on learning-based cost advantages potentially held by incumbent firms. A logit model of entry gives no evidence that entry decisions were sensitive to the cumulative production lead held by incumbents. Entry was facilitated by the fact that for most products, technology was available from a range of sources. A hazard function model reveals that entrant survival rates were unrelated to order of entry or source of process technology. However, survival was adversely affected when the leading incumbent held a large cumulative output advantage or when entrants built plants of sub-optimal scale. Thus, a large incumbent lead in production experience did not deter new entry but did reduce the entrant'S probability of survival.