Department of Economics

Conventional wisdom claims that the Law of One Price (LOP) fails in commodity markets, commodity borders are wide and Purchasing Power Parity (PPP) fails. But the evidence supporting those claims comes primarily from retail markets where price differentials do not represent risk-free profits. As we show, prices from a wide range of auction markets strongly support the LOP, reject wide borders and do not reject PPP. In addition, recognizing the difference between retail and auction markets helps explain several puzzles associated with exchange rates. The Keynesian paradigm dominates macroeconomics. We question that dominance for two reasons: (1) by reviving PPP we reject Liquidity Preference and support Loanable funds and (2) we reject the standard Keynesian assumption that commodity markets clear slowly and asset markets clear rapidly. Whether commodity or asset, retail markets clear slowly. Whether commodity or asset, auction markets clear rapidly.

Exchange-rate economics is filled with puzzles.  The asset approach has failed and without it most open-economy models are built on sand.  Conventional wisdom rejects the Law of One Price and views Purchasing Power Parity as useful at best in the long run.  We show for the first time how recognizing differences between retail, wholesale and auction markets, and recognizing that trade involves time in transit, helps solve the puzzles and provides a theory of exchange rates using auction markets for assets and commodities.  We also restore the Law of One Price and Purchasing Power Parity to the status of “not rejected”.    

Exchange-rate economics is filled with puzzles. The asset approach has failed. Purchasing Power Parity is useful at best in the long run. There is no clear link between exchange rates and fundamentals. With no empirically supported theory for exchange rates, open-economy macro models are built on sand. This paper shows for the first time how recognizing differences between retail, wholesale and auction markets helps solve the puzzles, provides a theory of exchange rates based on auction markets for assets and commodities, and suggests a link between fundamentals and exchange rates.

Teh Forward-Bias Puzzle, failure of uncovered interest parity and related puzzles suggest that there is a fundamental failure in internatonal financial markets.  Many theories attempt to explain this bias and failure.  But none of them has been widely accepted; at least partly because they are not consistent with the related puzzles.  The model of monetary policy in Table 6 explains the Forward-Bias Puzzle and the UIP failure without appealing to risk premia or information failures.  It also explains, or is at least consistent with, the related puzzles.  Finally it suggests that we need to change the way we think about UIP.

Uncovered interest parity is widely used in open economy macroeconomics.  But the evidence rejects UIP and implies forward bias.  There are many suggested explanations for the failure of UIP and forward bias, but none are widely accepted, at least partially because none appear to explain the related puzzles discussed below.  This paper shows how sterilized “leaning against the wind” and a combination of inflationary and liquidity effects of open market operations can explain forward bias and the failure of UIP even when expectations are rational.  They also appear to be able to explain the related puzzles.

Download rates of academic journals have joined citation rates as commonly used measures of research influence. But in what ways and to what extent do the two measures differ? This paper examines six years of download data for more than five thousand journals subscribed to by the University of California system. While down- load rates of journals are highly correlated with citation rates, the average ratio of downloads to citations varies substantially among academic disciplines. We find that, typically, the ratio of a journal’s downloads to citations depends positively on its im- pact factor. Surprisingly, we find that, controlling for citation rates, number of articles, academic discipline and year of download, there remains a “publisher effect,” with some publishers recording significantly more downloads than would be predicted from char- acteristics of their journals. Download statistics are recorded and supplied to libraries by journal publishers, often subject to confidentiality clauses. If libraries use download statistics to evaluate journals, they may want to account for publisher bias in these statistics.