The Response of Prices, Sales, and Output to Temporary Changes in Demand

We determine empirically how the Big Three automakers accommodate shocks to demand. They have the capability to change prices, alter labor inputs through temporary layoffs and overtime, or adjust inventories. These adjustments are interrelated, non-convex, and dynamic in nature. Combining weekly plant-level data on production schedules and output with monthly data on sales and transaction prices, we estimate a dynamic profit-maximization model of the firm. Using impulse response functions, we demonstrate that when an automaker is hit with a demand shock sales respond immediately, prices respond gradually, and production responds only after a delay. The size of the immediate sales response is linear in the size of the shock, but the delayed production response is non-convex in the size of the shock. For sufficiently large shocks the cumulative production response over the product cycle is an order of magnitude larger than the cumulative price response. We examine two recent demand shocks: the Ford Explorer/Firestone tire recall of 2000, and the September 11, 2001 terrorist attacks.

The Response of Prices, Sales, and Output to Temporary Changes in Demand

We determine empirically how the Big Three automakers accommodate shocks to demand. They have the capability to change prices, alter labor inputs through temporary layoffs and overtime, or adjust inventories. These adjustments are interrelated, non-convex, and dynamic in nature. Combining weekly plant-level data on production schedules and output with monthly data on sales and transaction prices, we estimate a dynamic profit-maximization model of the firm. Using impulse response functions, we demonstrate that when an automaker is hit with a demand shock sales respond immediately, prices respond gradually, and production responds only after a delay. The size of the immediate sales response is linear in the size of the shock, but the delayed production response is non-convex in the size of the shock. For sufficiently large shocks the cumulative production response over the product cycle is an order of magnitude larger than the cumulative price response. We examine two recent demand shocks: the Ford Explorer/Firestone tire recall of 2000, and the September 11, 2001 terrorist attacks.automobile pricing, inventories, revenue management, indirect inference

Prices, Production and Inventories over the Automotive Model Year

This paper studies the within-model-year pricing and production of new automobiles. Using new monthly data on U.S. transaction prices, we document that for the typical new vehicle, prices fall over the model year at a 9.2 percent annual rate. Concurrently, both sales and inventories are hump shaped. To explain these time series, we formulate a market equilibrium model for new automobiles in which inventory and pricing decisions are made simultaneously. On the demand side, we use micro-level data to estimate time-varying aggregate demand curves for each vehicle. On the supply side, we solve a dynamic programming model of an automaker that, while able to produce only one vintage of a product at a time, may accumulate inventories and consequently sell multiple vintages of the same product simultaneously. The profit maximizing pricing and production strategies under a build-to-stock inventory policy imply declining prices and hump-shaped sales and inventories of the magnitudes observed in the data. Further, roughly half of the price decline is driven by inventory control considerations, as opposed to decreasing demand.

Biogenesis of Y RNA–derived small RNAs

Small non-coding RNAs (sRNAs) constitute a significant portion of the transcriptome in eukaryotes. Many of these sRNAs regulate gene expression. Next-generation sequencing (NGS) has revealed a plethora of previously uncharacterised sRNAs with potential biological function, a number of which originate from longer RNAs. Here, the biogenesis of sRNAs derived from the non-coding Y RNAs (YsRNAs) was characterised as a model for understanding this emerging class of sRNA fragments. Y RNAs are highly conserved, 100 nt long molecules involved in DNA replication which bind to the autoimmune proteins Ro60 and La. YsRNAs are produced in cells undergoing apoptosis. Here, it was demonstrated that YsRNAs are generated from the 5’ and 3’ ends of all four Y RNAs in stressed and unstressed cells. Furthermore, production of these fragments was observed in both cancerous and non-cancerous cells. Although YsRNAs have been proposed to have gene silencing activity, experiments done here found that YsRNAs do not enter the microRNA pathway and are not generated by the gene silencing-related protein Dicer. Furthermore, experiments established that the enzyme which produces fragments from tRNAs, angiogenin, was also not responsible for YsRNA generation. Using mammalian cultured cells along with gene knockout and RNA interference (RNAi) technology, it was determined that RNase L contributed to YsRNA generation. Furthermore, the Y RNA binding protein Ro60 was shown to be essential for YsRNA production through a model of RNase protection. Analysis of deep sequencing data in Ro60 knockout cells revealed that many other sRNAs are also dependent on Ro60. Finally, a ‘high definition’ (HD) protocol to improve NGS detection of sRNAs was tested. The HD protocol was found to be better at detecting sRNAs than current methods. This will facilitate more efficient detection of novel sRNAs in the future

Market Value and Patent Citations: A First Look

As patent data become more available in machine-readable form, an increasing number of researchers have begun to use measures based on patents and their citations as indicators of technological output and information flow. This paper explores the economic meaning of these citation-based patent measures using the financial market valuation of the firms that own the patents. Using a new and comprehensive dataset containing over 4800 U. S. Manufacturing firms and their patenting activity for the past 30 years, we explore the contributions of R&D spending, patents, and citation-weighted patents to measures of Tobin's Q for the firms. We find that citation-weighted patent stocks are more highly correlated with market value than patent stocks themselves and that this fact is due mainly to the high valuation placed on firms that hold very highly cited patents. We also find that self-citations are worth about twice as much as ordinary citations, especially to smaller firms.

Market Value and Patent Citations: A First Look

As patent data become more available in machine-readable form, an increasing number of researchers have begun to use measures based on patents and their citations as indicators of technological output and information flow. This paper explores the economic meaning of these citation-based patent measures using the financial market valuation of the firms that own the patents. Using a new and comprehensive dataset containing over 4800 U. S. Manufacturing firms and their patenting activity for the past 30 years, we explore the contributions of R&D spending, patents, and citation-weighted patents to measures of Tobin's Q for the firms. We find that citation-weighted patent stocks are more highly correlated with market value than patent stocks themselves and that this fact is due mainly to the high valuation placed on firms that hold very highly cited patents.

The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools

This paper describes the database on U.S. patents that we have developed over the past decade, with the goal of making it widely accessible for research. We present main trends in U. S. patenting over the last 30 years, including a variety of original measures constructed with citation data, such as backward and forward citation lags, indices of 'originality' and 'generality', self-citations, etc. Many of these measures exhibit interesting differences across the six main technological categories that we have developed (comprising Computers and Communications, Drugs and Medical, Electrical and Electronics, Chemical, Mechanical and Others), differences that call for further research. To stimulate such research, the entire database about 3 million patents and 16 million citations is now available on the NBER website. We discuss key issues that arise in the use of patent citations data, and suggest ways of addressing them. In particular, significant changes over time in the rate of patenting and in the number of citations made, as well as the inevitable truncation of the data, make it very hard to use the raw number of citations received by different patents directly in a meaningful way. To remedy this problem we suggest two alternative approaches: the fixed-effects approach involves scaling citations by the average citation count for a group of patents to which the patent of interest belongs; the quasi-structural approach attempts to distinguish the multiple effects on citation rates via econometric estimation.