Spatial market equilibria with flow-dependent supply and demand: The single commodity case

Existence and uniqueness of spatial price equilibria are analyzed for a single commodity market network in which supply and demand levels are allowed to depend on commodity flows as well as market prices. This framework makes possible the explicit incorporation of flow-dependent shipment costs into market supply and demand functions. Moreover, since many quantity signals are also expressible as flow-dependent variables, it is possible to incorporate a number of recent models of supply and demand behavior which allow for quantity signals as well as price signals. In this context, the main result of the paper is to establish the existence of spatial price equilibria for such market networks under quite general conditions. In addition, certain uniqueness results are established for the case of arc-generated networks.     

The Value Relevance of Earnings and Cash Flows: Empirical Evidence for Japan

The Japanese equity market is one of the largest in the world. In recent years, fund managers worldwide have substantially increased their exposure to the Japanese capital markets. In spite of the Japanese capital market's rapid growth and its increasing importance in the international financial world, there has been limited empirical evidence linking security returns to earnings and cash flows. This study extends the growing empirical literature on the association of earnings and cash flows with security returns by using a Japanese dataset consisting of 6,662 firm-year observations for the period 1984–93. We hypothesize that (i) earnings and cash flows are jointly associated with stock returns, and (ii) the association between cash flows (earnings) and security returns increases (decreases) when earnings are transitory. This study provides empirical evidence (i) that cash flows (earnings) have information content beyond earnings (cash flows) in explaining security returns, and (ii) that cash flows (earnings) play a more (less) important role in the marketplace when earnings are transitory. Moreover, results show that the explanatory power of our Japanese models is similar to the evidence provided in prior US studies, indicating that Japanese investors utilize earnings and cash flows in their pricing of equities as their US counterparts.     

Commodity prices and the CPI: Cointegration, information, and signal extraction

This paper provides theoretical underpinnings for the commodity price/aggregate price relationship, discusses the conditions under which commodity prices are useful information variables for monetary policy, and provides empirical results which suggest why commodity prices have not been very useful for forecasting.     

Statistical Causality and Strategic Behavior in Industrial Markets

This paper proposes a simple test of the leader-follower model of strategic behavior. This test relates the temporal notions of leadership central to such models to the empirical methods of statistical causality. This test is performed using data from the US softwood plywood industry of the last three decades. Others have productively explored the spatial pricing practices of this industry by applying a leader-follower model. Similarly, we find that a leader-follower model explains well the temporal relations between key strategic variables (prices) in the industry. We conclude that the leader-follower model imposes meaningful restrictions on observable time-series data and that statistical causality is a useful method for testing these restrictions.     

Contaminant,Commodity and Fuel: A Multi-sited Study of Waste's roles in Urban Transformations from Italy to Austria

This article traces the flow of municipal solid waste from southern Italy through a waste-to-energy facility and district heating system in Austria, examining the roles that waste's transformation from contaminant to commodity to fuel plays in interconnected, distributed, and contested urbanization processes. It contends that, while metabolic circulation hides socioecological costs in one place to facilitate valorization in another, specific spatial configurations emerge through territorialization—of waste economies, in this case—providing the spatial base to realize metabolic flows and to anchor political narratives. A decisive effect is that certain patterns of urbanization become locked-in, impeding alternative metabolic transitions and spatial configurations. Attending to the coproduction of three sites—Naples, Italy; Zwentendorf, Austria; and St Pölten, Austria—through the circulation and transformation of waste and energy the article provides an empirical multi-sited case study of a political ecology of urbanization.     

Forecasts of US short-term interest rates: A flexible forecast combination approach

This paper develops a flexible approach to combine forecasts of future spot rates with forecasts from time-series models or macroeconomic variables. We find empirical evidence that, accounting for both regimes in interest rate dynamics, and combining forecasts from different models, helps improve the out-of-sample forecasting performance for US short-term rates. Imposing restrictions from the expectations hypothesis on the forecasting model are found to help at long forecasting horizons.     

Forecasting macroeconomic variables using collapsed dynamic factor analysis

We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with principal components from a large time series panel of macroeconomic indicators using a multivariate unobserved components time series model. When the key economic variables are observed at a low frequency and the panel of macroeconomic variables is at a high frequency, we can use our approach for both nowcasting and forecasting purposes. Given a dynamic factor model as the data generation process, we provide Monte Carlo evidence of the finite-sample justification of our parsimonious and feasible approach. We also provide empirical evidence for a US macroeconomic dataset. The unbalanced panel contains quarterly and monthly variables. The forecasting accuracy is measured against a set of benchmark models. We conclude that our dynamic factor state space analysis can lead to higher levels of forecasting precision when the panel size and time series dimensions are moderate.     

Commodity house prices

This paper studies how commodity price movements have affected the local house prices in commodity-dependent economies, Australia and New Zealand. We build a geographically hierarchical empirical model and find that the commodity prices influence local house prices directly and also indirectly through macroeconomic variables. The impacts of commodity price changes are analogous to “income shocks” rather than “cost shocks”. Regional heterogeneity is also observed in terms of differential dynamic responses of local house prices to energy versus non-energy commodity price movements. The results are robust to alternative approaches. Directions for future research are also discussed.     

A new look at static spatial price equilibrium models

In this paper we propose a new view and more general formulations of static single commodity spatial equilibrium models, that lead to simpler and more efficient algorithms than those previously employed for solving models of this type. The proposed formulations incorporate general transportation networks and may be extended to allow for multiple commodities. Solution algorithms are suggested for the problem with multiple commodities, when there exists an equivalent optimization problem. We show that the multiple commodity problem may be cast in the form of a variational inequality, when there does not exist an equivalent optimization problem and propose algorithms to solve this version of the problem as well.     

Determining of threat perceptions affecting direction of the US defense expenditures by using ordinal regression models

The article examines whether the US threat perceptions defined in terms of federal government national defense outlays in billions of constant (FY 2000) dollars change along with periodical changes in international politics between 1945 and 2007. Three different models affecting direction of the US defense expenditures are developed. The first model are estimated by using five link functions even though results of only two of them, complementary log–log and cauchit, are presented. As complementary log–log produced the best results, others models are predicted by using only this function. The parameter estimates of complementary log–log function for the first model indicate that four of these variables (Ford, Carter, Reagan and Bush Sr.) out of eleven are significant in the category of presidents. “Truman Docrtrine/Cominform”, “Korean War”, “Vietnam War”, and “Invasion of Iraq” also seem to be the important independent variables on empirical grounds for the first model. While “Party”, “Invasion of Iraq”, “Vietnam War”, “Korean War”, and “Cuban Missile Crisis” constitute the important independent variables on empirical grounds for the second model, “Korean War”, “Vietnam War”, “Invasion of Iraq”, “Truman Docrtrine/Cominform”, “The Cold War and New World Order”, and “Cuban Missile Crisis” are important independent variables on empirical grounds for the third model. Estimations based on these three models therefore suggest that aforementioned independent variables do indeed have effect on the US defense expenditures.     

The structure of US food demand

An exactly aggregable system of Gorman Engel curves for US food consumption is developed and implemented. Box-Cox transformations on prices and income nest functional form. The model nests rank up to rank three. The model is estimated by nonlinear three-stage least squares with annual time series data on 21 foods, 17 nutrients, age and race demographics, and the distribution of income for 1919–1941 and 1947–2000. Results are consistent with full rank three. Point estimates for the Box-Cox parameters on income and prices are 0.86 and 1.09, respectively, strongly rejecting zero and one in both cases. No statistical evidence of serial correlation, specification errors, or parameter instability is found.     

Aggregation across countries in stochastic population forecasts

Stochastic methods of multi-state population modeling are less developed than methods for single states for two reasons. First, the structure of a multi-state population is inherently more complex than that of a single state because of state-to-state transitions. Second, estimates of cross-state correlations of the vital processes are a largely uncharted territory. Unlike multi-state lifetable theory, in forecasting applications the role of directed flows from state to state is often less important than the overall coherence of the assumptions concerning the vital processes. This is the case in the context of the European Union. Thus, a simplified approach is feasible, in which migration is represented by state-specific net numbers of migrants. This allows the use of existing single-state software, when simulations are suitably organized, in a multi-state setting. To address the second problem, we provide empirical estimates of cross-country covariances in the forecast uncertainty of fertility, mortality, and net migration. Together with point forecasts of these parameters that are coherent across countries, this produces coherent forecasts for aggregates of countries. The finding is that models for intermediate correlations are necessary for a proper accounting of forecast uncertainty at the aggregate level, in this case the European Union.     

Multicointegration in Stock-Flow Models

Multicointegration, in the sense of Granger and Lee (1990), frequently occurs in models of stock-flow adjustment and implies cointegration amongst I(2) variables and their differences (polynomial cointegration). The purpose of this article is two-fold. First, we demonstrate that based on a multicointegrated vector autoregression (VAR) two equivalent error correction model (ECM) representations can be derived; the first is expressed in terms of adjustments in the flows of the variables (the standard I(2) ECM), and the second is expressed in terms of adjustments in both the stocks and the flows. Secondly, we apply I(2) estimation and testing procedures for multicointegrated time series to analyze data for US housing construction. We find that stocks of housing units started and completed exhibit poly- nomial cointegration (and hence the flows are multicointegrated) and the associated ECM's are estimated. Lee (1992, 1996) also found multicointegration in this data set but without explicitly exploiting the I(2) property.     

Statistical inference on regression with spatial dependence

Central limit theorems are developed for instrumental variables estimates of linear and semiparametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss estimation of the variance matrix, including estimates that are robust to disturbance heteroscedasticity and/or dependence. A Monte Carlo study of finite-sample performance is included. In an empirical example, the estimates and robust and non-robust standard errors are computed from Indian regional data, following tests for spatial correlation in disturbances, and nonparametric regression fitting. Some final comments discuss modifications and extensions.     

Endogenizing the provision of money: Costs of commodity and fiat monies in relation to the value of trade

We consider the problem of including the costs and value of the institutions that define money and support trade, within the framework of economic optimization. We compare monetary systems mediated by durable commodity monies, versus pure fiat monies, in order to understand the separation and eventual independence of the institutionally-created value of money from the values of underlying traded goods. We treat the emergence of monetary function as a problem in mechanism design, modeled by minimal strategic market games that overcome a generalized Jevons failure when agents must commit ahead of time to specialist resource production. We consider in particular the problem of defining closures with respect to both money flows and labor-allocation and trading decisions, and show that minimal models require many of the fundamental institutions of banking and contract enforcement found in real economies, in order to define a self-policing system. We define costs, value, and the efficiencies of the institutions that support trade in terms of a natural money-metric welfare function, and compare the characteristics of commodity and fiat monies by these measures. Through careful treatment of the stock/flow distinction in repeated-game settings, we find that commodity money, even when its value derives heavily from its institutional role, remains defined by its flow characteristics, in contrast to fiat money, for which the control function is defined inherently in terms of stock variables. Our notation is somewhat nonconventional for economics but to do justice to econo-physics concepts such as scaling and dimensional analysis and to stress the distinction between stocks and flows, we believe this notation is justified. We provide a full listing of notation in Appendix A.     

INTER-REGIONAL TRADE FLOW ESTIMATION THROUGH NON-SURVEY MODELS: AN EMPIRICAL ASSESSMENT

Inter-regional trade estimation has been pointed out as a crucial problem when constructing a multiregional input–output system. Knowledge of inter-regional trade flows, at least of the pooled volume of exports and imports by commodity, is critical in accounting for important spillover and feedback effects deriving from inter-regional linkages. However, in most countries, there are no completely reliable survey-based statistics on inter-regional trade. Thus, this paper intends to evaluate the reasonability of using indirect inter-regional trade estimates, comparing different estimating methods and assessing the sensitivity of the model results. Based on our empirical comparisons we conclude that input–output models are not greatly affected by the insertion of different trade values. Thus, our results support the use of indirect estimates for inter-regional trade, whenever survey-based data are unavailable.     

THE FORMING OF SPATIAL PATTERNS OF CONSUMPTION IN TAIPEI: Some Observations in a Third-World Metropolis

This paper argues that traditional central place theory is problematic because it (1) treats a central place system as a self-contained system isolated from the rest of the world and immune from outside disturbance; (2) regards spatial structure as a product of laws of economics, consumer behavior and demography, but never of socio-cultural forces; and (3) tries to depict the hierarchical structure of tertiary activities as a static situation. The paper then proceeds to view the formation of metropolitan spatial structures from the perspectives of (1) dependency development, and (2) as a top-down development process open to the outside influence of international cultural flows, which sustain commodity diversification and symbolic consumption, both essential to the capitalist global economy. To illustrate how this cultural process takes place and helps change the spatial structure of a third-world metropolis, a case study of the permeation of McDonald's hamburger stores in the Taipei metropolis is reported in the third part of the paper. It concludes that the spatial structure is a result of both economic and socio-cultural forces and processes, and that the provider of a culturally effective commodity is also a shaping force in the mass consumption society. When a corporation promotes a new product image or diffuses its services, it is culturally preparing the society for the next step in the endless course of symbolic consumption and, therefore, of spatial restructuring.     

Estimation of Non-Stationary Social Accounting Matrix Coefficients with Supply-Side Information

Given aggregated data, a framework for estimating the entries of a social accounting matrix (SAM), or any large matrix of expenditures, trade or income flows, is developed. Under this framework it is possible to evaluate the contribution of structural and supply-side information, as well as policy variables, within the generalized context of a non-stationary SAM. Inference and diagnostic properties are developed as well. This new estimator can be viewed as a generalized maximum likelihood estimator. Stationary and non-stationary estimates of the US SAM for the years 1987-1994 together with the effects of supply-side variables are analyzed.     

Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows

Abstract

We use a spatial econometric extension of the traditional regression-based gravity model to model commodity flows, focusing on a formal methodology for incorporating information regarding the highway network into the spatial connectivity structure of the spatial autoregressive econometric model. We show that our simple approach to incorporating this information in the model produces improved model fit and higher likelihood function values. Empirical estimates of the relative importance of the different types of origin–destination connectivity between regions indicates that the strongest spatial autoregressive effects arise when both origin and destination regions have neighbouring regions located on the highway network.