Dynamic Factor Demands Under Rational Expectations

This paper presents a dynamic model of the industrial demands for structures, equipment, and blue- and white-collar labor. Our approach is consistent with producers holding rational expectations and optimizing dynamically in the presence of adjustment costs, yet it permits generality of functional form regarding the technology. We represent the technology by atranslog input requirement function that specifies the amount of blue-collar labor (a flexible factor) the firm must hire to produce a level of output given its quantities of three quasi-fixed factors that are subject to adjustment costs: non-production (white-collar) workers, equipment, and structures.A complete description of the production structure is obtained by simultaneously estimating the input requirement function and three stochastic Euler equations.We apply an instrumental variable technique to estimate these equations using aggregate data for U.S. manufacturing. We find that as a fraction of total expenditures, adjustment costs are small in total hut large on the margin,and that they differ considerably across quasi-fixed factors. We also present short- and long-run elasticities of factor demands.

Uncertainty, Investment, and Industry Evolution

We study the effects of aggregate and idiosyncratic uncertainty on the entry of firms, total investment, and prices in a competitive industry with irreversible investment. We first use standard dynamic programming methods to determine firms' entry decisions, and we describe the resulting industry equilibrium and its characteristics, emphasizing the effects of different sources of uncertainty. We then show how the conditional distribution of prices can be used as an alternative means of determining and understanding the behavior of firms and the resulting industry equilibrium. Finally, we use four-digit U.S. manufacturing data to examine some implications of the model.

The Excess Co-Movement of Commodity Prices

This paper tests and confirms the existence of a puzzling phenomenon - the prices of largely unrelated raw commodities have a persistent tendency to move together. We show that this comovement of prices is well in excess of anything that can be explained by the common effects of past, current, or expected future values of macroeconomic variables such as inflation, industrial production, interest rates, and exchange rates. These results are a rejection of the standard competitive model of commodity price formation with storage.

Are Imports to Blame?: Attribution of Injury Under the 1974 Trade Act

Under Section 201 of the 1974 Trade Act, a domestic industry can obtain temporary protection against imports by demonstrating before the International Trade Commission that it has been injured, and that imports have been the"substantial cause" of injury --i.e.,"a cause which is important and not less than any other cause." To date, the ITC lacks a coherent framework for selecting a menu of other factors which might be considered as causes of injury, and for weighing the effects of these other factors against those of imports.This paper sets forth a straightforward economic and statistical framework for use in Section 201 cases. This framework is based on the fact that if the domestic industry is competitive, injury can arise from one or more of three broad sources: adverse shifts in market demand, adverse shifts in domestic supply, or increased imports. We show how these sources of injury can be distinguished in theory, and statistically evaluated in practice. As an illustrative example, we apply the framework to the case of the copper industry, which petitioned the ITC for relief in 1984. Although that industry has indeed suffered injury, we show that the "substantial cause" was not imports, but instead increasing costs and decreasing demand.

Uncertainty, investment, and industry evolution

We study the effects of aggregate and idiosyncratic uncertainty on the entry of firms, total investment, and prices in a competitive industry with irreversible investment. We first use standard dynamic programming methods to determine firms' entry decisions, and we describe the resulting industry equilibrium and its characteristics, emphasizing the effects of different sources of uncertainty. We then show how the conditional distribution of prices can be used as an alternative means of determining and understanding the behavior of firms and the resulting industry equilibrium. Finally, we use four-digit U.S. manufacturing data to examine some implications of the model.Supported by the MIT's Center for Energy Policy, and by the National Science Foundation

Climate sensitivity uncertainty : When is good news bad?

Climate change is real and dangerous. Exactly how bad it will get, however, is uncertain. Uncertainty is particularly relevant for estimates of one of the key parameters: equilibrium climate sensitivity—how eventual temperatures will react as atmospheric carbon dioxide concentrations double. Despite significant advances in climate science and increased confidence in the accuracy of the range itself, the “likely” range has been 1.5-4.5°C for over three decades. In 2007, the Intergovernmental Panel on Climate Change (IPCC) narrowed it to 2-4.5°C, only to reverse its decision in 2013, reinstating the prior range. In addition, the 2013 IPCC report removed prior mention of 3°C as the “best estimate.” We interpret the implications of the 2013 IPCC decision to lower the bottom of the range and excise a best estimate. Intuitively, it might seem that a lower bottom would be good news. Here we ask: When might apparently good news about climate sensitivity in fact be bad news in the sense that it lowers societal wellbeing? The lowered bottom value also implies higher uncertainty about the temperature increase, a definite bad. Under reasonable assumptions, both the lowering of the lower bound and the removal of the “best estimate” may well be bad news

Risk-Seeking versus Risk-Avoiding Investments in Noisy Periodic Environments

We study the performance of various agent strategies in an artificial investment scenario. Agents are equipped with a budget, $x(t)$, and at each time step invest a particular fraction, $q(t)$, of their budget. The return on investment (RoI), $r(t)$, is characterized by a periodic function with different types and levels of noise. Risk-avoiding agents choose their fraction $q(t)$ proportional to the expected positive RoI, while risk-seeking agents always choose a maximum value $q_{max}$ if they predict the RoI to be positive ("everything on red"). In addition to these different strategies, agents have different capabilities to predict the future $r(t)$, dependent on their internal complexity. Here, we compare 'zero-intelligent' agents using technical analysis (such as moving least squares) with agents using reinforcement learning or genetic algorithms to predict $r(t)$. The performance of agents is measured by their average budget growth after a certain number of time steps. We present results of extensive computer simulations, which show that, for our given artificial environment, (i) the risk-seeking strategy outperforms the risk-avoiding one, and (ii) the genetic algorithm was able to find this optimal strategy itself, and thus outperforms other prediction approaches considered.Comment: 27 pp. v2 with minor corrections. See http://www.sg.ethz.ch for more inf

Detecting a Currency's Dominance or Dependence using Foreign Exchange Network Trees

In a system containing a large number of interacting stochastic processes, there will typically be many non-zero correlation coefficients. This makes it difficult to either visualize the system's inter-dependencies, or identify its dominant elements. Such a situation arises in Foreign Exchange (FX) which is the world's biggest market. Here we develop a network analysis of these correlations using Minimum Spanning Trees (MSTs). We show that not only do the MSTs provide a meaningful representation of the global FX dynamics, but they also enable one to determine momentarily dominant and dependent currencies. We find that information about a country's geographical ties emerges from the raw exchange-rate data. Most importantly from a trading perspective, we discuss how to infer which currencies are `in play' during a particular period of time

Do Stock Prices Move Together Too Much?

We show that comovements of individual stock prices cannot be justified by economic fundamentals. This finding is a rejection of the present value model of security valuation. Unlike other tests of this model, ours is robust in that it allows for volatility in ex ante rates of return. The only constraint we impose is that investors' utilities are functions of a single consumption index. This implies that changes in discount rates must be related to changes in macroeconomic variables, and hence stock prices of companies in unrelated lines of business should move together only in response to changes in current or expected future macroeconomic conditions. We also show that this constraint implies that any priced factors in the APT model must be related to macroeconomic variables. Hence our results are also a rejection of the APT, so constrained.