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An extensive empirical literature has addressed a wide array of issues pertaining to price linkages over space and across time. Empirical models of price linkages have been used to measure market power and to characterize the operation of markets that are separated by space, time, and product form. The long history of these empirical models extends from simple tests of price correlation, to conventional regression tests, to modern time series models that account for nonstationarity, nonlinearities, and threshold behavior in market linkages. This paper proposes an entirely different and potentially novel approach to analyzing these same types of time series data in a nonlinear fashion. Copula-based models that consider the joint distribution of prices separated by space are developed and applied to weekly prices for important lumber products at geographically distinct markets. In particular, we consider prices taken from weekly editions of the Random Lengths publication for homogeneous OSB products., Replaced with revised version of paper 06/28/11.

Spatial regression models incorporating non-stationarity in the regression coefficients are popular. We propose a spatial variant of the Smooth Transition AutoRegressive (STAR) model that is more parsimonious than commonly used approaches and endogenously determines the extent of spatial parameter variation. Uncomplicated estimation and inference procedures are demonstrated using a neoclassical convergence model for United States counties.

In this research we estimate the effect of El Nino Southern Oscillation (ENSO) over time on market dynamics for eight major vegetable oil prices. We estimate a system for vegetable oil prices by using a smooth transition vector error correction model (STVECM) to analyze impacts of ENSO events on production, and, more interestingly, their asymmetric nature. The results of estimated Exponential STVECM and Quadratic STAR models, respectively for the system of oil price equations and the ENSO variable regressions, suggest a smooth transition between ENSO regimes, and provide a better overall fit to the data than do linear models. Effects of the ENSO shock are analyzed using generalized impulse-response functions (GIRFs). The non-linear nature of these shocks is apparent, as the GIRFs are observed to be asymmetric depending on whether ENSO shocks are positive or negative. For most vegetable oil prices an ENSO shock has a permanent effect, meaning that prices advance to a new equilibrium level. Generally, a positive ENSO shock results in increased prices, and the opposite is true for the negative ENSO shock. The magnitude of the price change is largest for the coconut oil and palm kernel oil, and is the smallest for the ground nut oil. Also, it takes approximately two years for prices to stabilize at a new equilibrium level after the shock.

This paper investigates non-linearity in spatial processes models and allows for a gradual regime-switching structure in the form of a smooth transition autoregressive process. Until now, applications of the smooth transition autoregressive (STAR) model have been largely confined to the time series context. The paper focuses on extending the non-linear smooth transition perspective to spatial processes models, in which spatial correlation is taken into account through the use of a so-called weights matrix identifying the topology of the spatial system. We start by deriving a non-linearity test for a simple spatial model, in which spatial correlation is only included in the transition function. Next, we propose a non-linearity test for a model that includes a spatially lagged dependent variable or spatially autocorrelated innovations as well. Monte Carlo simulations of the various test statistics are performed to examine their power and size. The proposed modeling framework is then used to identify convergence clubs in the context of U.S. county-level economic growth over the period 1963–2003.

This paper examines the Prebisch-Singer hypothesis, that is, the hypothesis of a long-run decline in the prices of primary commodity prices relative to that of manufactured goods, by considering nonlinear alternatives. Specifically, in this paper we use bootstrap procedures to test the linear unit root model against models belonging to the family of smooth transition autoregressions (STARs) for 24 commodities, 1900-98. In eighteen cases we reject the linear null at usual significance levels. In fourteen cases we are able to successfully fit STAR-type models. Simulation results show there is very little support for the Prebisch-Singer hypothesis., Replaced with revised version of paper 06/27/06.

Replaced with revised version of paper 06/27/06.

This paper compares the finite sample performance of subsample bootstrap and subsample jackknife techniques to the traditional bootstrap method when parameters are constrained to be on some boundary. To assess how these three methods perform in an empirical application, a negative semi-definite translog cost function is estimated using U.S. manufacturing data.

This paper compares the finite sample performance of subsample bootstrap and subsample jackknife techniques to the traditional bootstrap method when parameters are constrained to be on some boundary. To assess how these three methods perform in an empirical application, a negative semi-definite translog cost function is estimated using U.S. manufacturing data.

This paper estimates an Almost Ideal Supply System using aggregate U.S. agricultural data. Share equations derived from an indirect production function yield elasticities that are consistent with production theory. A nested test comparing the Almost Ideal Supply System to the Translog Production Function finds little difference between the two models.

This paper estimates an Almost Ideal Supply System using aggregate U.S. agricultural data. Share equations derived from an indirect production function yield elasticities that are consistent with production theory. A nested test comparing the Almost Ideal Supply System to the Translog Production Function finds little difference between the two models.

In this paper we introduce inverse demand systems that include quadratic scale terms. These systems are similar to regular quadratic demand systems introduced by Howe, Pollack, and Wales. A unique feature of these specifications is that they maintain linear scale curves as a special case. For illustrative purposes we estimate the Normalized Quadratic Inverse Demand-Quadratic Scale System using monthly South Atlantic fish landings and valuation data, 1980-1996. In estimation concavity is maintained locally, and the rank reduction procedures advocated by Diewert and Wales (1988b) are employed. The estimated model is then used to obtain welfare estimates associated with catch restrictions.

This article applies recent developments in time-series modeling to analyze the retail prices of beef, pork, and chicken. Specifically, generalized autoregressive conditional heteroscedasticity (GARCH) models were fitted to these data to determine if, unlike more traditional time-series models, the conditional variances of the underlying stochastic processes are nonconstant. The estimation results indicate that the constant conditional variances assumption can be rejected. Furthermore, ex post forecast intervals generated from the GARCH processes indicate that the forecasting accuracy of the estimated models has varied widely over time with substantial volatility occurring during the 1970s and early 1980s.