Research Database

Over the period 1982–2006, the U.S. Bureau of Economic Analysis (BEA) estimates the return on investments of foreign subsidiaries of U.S. multinational companies averaged 9.4 percent per year after taxes while U.S. subsidiaries of foreign multinationals earned on average only 3.2 percent. We estimate the importance of two factors that distort BEA returns: technology capital and plant-specific intangible capital. Technology capital is accumulated know-how from intangible investments in R&D, brands, and organizations that can be used in foreign and domestic locations. Technology capital used abroad generates profits for foreign subsidiaries with no foreign direct investment. Plant-specific intangible capital in foreign subsidiaries is expensed abroad, lowering current profits on foreign direct investment (FDI) and increasing future profits. We develop a multicountry general equilibrium model with an essential role for FDI and apply the same methodology as the BEA to construct economic statistics for the model economy. We estimate that mismeasurement of intangible investments accounts for over 60 percent of the difference in BEA returns.

This paper describes a newly constructed data set of all U.S. state banks from 1782 to 1861. It contains the names and locations of all banks and branches that went into business and an estimate of when each operated. The compilation is based on reported balance sheets, listings in banknote reporters, and secondary sources. Based on these data, the paper presents a count of the number of banks and branches in business by state. I argue that my series are superior to previously existing ones for reasons of consistency, accuracy, and timing. The paper contains examples to support this argument.

This paper investigates—in the context of a simple example—the accuracy of an econometric technique recently proposed by Kydland and Prescott. We consider a hypothetical econometrician who has a large sample of data, which is known to be generated as a solution to an infinite horizon, stochastic optimization problem. The form of the optimization problem is known to the econometrician. However, the values of some of the parameters need to be estimated. The optimization problem—presented in a recent paper by Long and Plosser—is not linear quadratic. Nevertheless, its closed form solution is known, although not to the hypothetical econometrician of this paper. The econometrician uses Kydland and Prescott’s method to estimate the unknown structural parameters. Kydland and Prescott’s approach involves replacing the given stochastic optimization problem by another which approximates it. The approximate problem is a element of the class of linear quadratic problems, whose solution is well-known—even to the hypothetical econometrician of this paper. After examining the probability limits of the econometrician’s estimators under “reasonable” specifications of model parameters, we conclude that the Kydland and Prescott method works well in the example considered. It is left to future research to determine the extent to which the results obtained for the example in this paper applies to a broader class of models.

An overlapping generations model is developed that contains labor markets in which adverse selection problems arise. As a response to these problems, quantity rationing of labor occurs. In addition, the model is capable of generating (a) random employment and prices despite the absence of underlying uncertainty in equilibrium; (b) a statistical (nondegenerate) Phillips curve; (c) procyclical movements in productivity; (d) correlations between aggregate demand and unemployment (and output); (e) an absence of correlation between unemployment (employment) and real wages. In addition, the Phillips curve obtained typically has the "correct" slope. Finally, the model reconciles the theoretical importance and observed unimportance of intertemporal substitution effects, and explains why price level stability may be a poor policy objective.

Earlier researchers have found either no relation or a positive relation between the conditional expected return and the conditional variance of the monthly excess return on stocks when they used the standard GARCH-M model. This is in contrast to the negative relation found when other approaches were used to model conditional variance. We show that the difference in the estimated relation arises because the standard GARCH-M model is misspecified. When the standard model is modified allow for (i) the presence for seasonal patterns in volatility, (ii) positive and negative innovations to returns to having different impacts on conditional volatility, and (iii) nominal interest rates to affect conditional variance, we once again find support for a negative relation. Using the modified GARCH-M model, we also show that there is little evidence to support the traditional view that conditional volatility is highly persistent. Also, positive unanticipated returns result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility of a similar magnitude. Hence the time series properties of the monthly excess return on stocks appear to be substantially different from that of the daily excess return on stocks.