Paul M. Beaumont

Land degradation and property regimes

This paper addresses the relationship between property regimes and land degradation outcomes, in the context of peasant agriculture. We consider explicitly whether private property provides for superior soil resource conservation, as compared to common property and open access. To assess this we implement optimization algorithms on a supercomputer to address resource decision-making of individual households. We find that conditions exist under which private property does not lead to the best environmental outcome. Access to farming technology and off-farm employment opportunities are key factors in this result.

Supply and Demand Interaction in Integrated Econometric and Input-Output Models

Integrated input-output and econometric models are often claimed to be superior to either input-output or econometric models alone by virtue of capturing supply and demand interactions. This claim is examined by considering the structure of existing integrated models. It was found that existing integrated models do not offer supply and demand interaction. This is because neither of the major components of the integrated models offer an adequately specified supply side. Integrating two demand-driven models does not produce a model with supply and demand interaction. The prospects that integrated models will improve on this score, particularly at the subnational level, are not high. Rather, it is suggested that the integrated modeling strategies be skipped over and a computable general equilibrium modeling strategy be adopted.

New directions in quasi-experimental control group methods for project evaluation

Abstract Quasi-experimental control group methods can become valuable tools for evaluating public policies and programs that have a spatial dimension. Control groups of places can be used to establish a baseline from which the effect of “treatment” can be inferred. This paper describes the basic approach by presenting an empirical example and then reports on an on-going, multi-year research project intended to make these methods readily usable. The research is focusing on methods for computerizing the selection of control groups and for conducting statistical tests of the significance of the inferred treatment effects.

A distributed parallel genetic algorithm for solving optimal growth models

We use a distributed parallel genetic algorithm (DPGA) to fund numerical solutions to a single state deterministic optimal growth model for both the infinite and finite horizon cases. To evaluate the DPGA we consider a version of the Taylor-Uhlig problem for which the analytical solutions are known. The first-order conditions for the infinite horizon case lead to a nonlinear integral equation whose solution we approximate using a Chebyshev polynomial series expansion. The DPGA is used to search the parameter space for the optimal fit for this function. For the finite horizon case the DPGA searches the state space for a sequence of states which maximize the agents discounted utility over the finite horizon. The DPGA runs on a cluster of up to fifty workstations linked via PVM. The topology of the function to be optimized is mapped onto each node on the cluster and the nodes essentially complete with one another for the optimal solution. We demonstrate that the DPGA has several useful features. For instance, the DPGA solves the exact Euler equation over the full range of the state variable and it does not require an accurate initial guess. The DPGA is easily generalized to multiple state problems.

Robust Estimation of GARMA Model Parameters with an Application toCointegration among Interest Rates of Industrialized Countries

We review the literature on long memory ARFIMA and GARMA models andintroduce a new efficient estimator for GARMA models, which we show to berobust. Next we conduct a Monte Carlo study to demonstate the power of theDickie–Fuller test when the data are generated from a stationary GARMAprocess. We conclude with a brief discussion of cointegration in the contextof GARMA models with an application to international interest rates.

The magnetic bode fallacy

The magnetic moments and angular momenta of Solar System bodies are compared to evaluate the statistical reality of the relation that has come to be called the “magnetic Bode law.” Runcorn has suggested that this “law”, giving a slope of about 3/5 on a log-log plot, is only a geometrical effect of the angular momenta being proportional to the fifth power of radius and the magnetic moment the cube. The best fit line to the six planets with nonzero magnetic fields is log(m/me) = −0.2 (±0.2) + 0.82 (±0.05) log(L/Le), where the subscripts denote the values for Earth. The value 0.82 is shown to be consistent with the 3/5 slope once estimation bias is accounted for. Monte Carlo analysis was used to construct (2500) synthetic solar systems from the variables representing the range of the planets radii, densities, coefficients of inertia, periods of rotation, and surface poloidal field intensity. If these variables are considered independent, it was found that the probability of obtaining a slope as different from 0.6 as suggested by Runcorn is 34%, but only a 0.2% chance of obtaining values that are so linear as the actual data on this plot. If instead the covariances of the actual planets are included in the analysis, the mean slope is 0.82 with small deviation, but the odds of obtaining such a tight fit as that observed in the actual Solar System becomes 63%. It is concluded that considering the lack of physical plausibility of the correlations between the physical parameters besides field, the strong correlation with rotation as an important factor in computing the angular momentum is spurious. Comparisons are done using both the angular momenta of the whole planet, and the theorized magnetoactive shells and cores. The conducting volumes are also compared with the poloidal dipole fields external to these magnetoactive regions. The studies show that the volumes of the magnetoactive regions appear to be related to the strength of the poloidal field just outside, and that the tighter trend for the log momentum versus log dipole moment plots is mainly due to the geometric factor suggested by Runcorn. Plots of field just outside these regions versus their volumes show that the Earth and Io have significantly higher fields than those predicted by a line extending from the giant planets to Mercury. Such a line also does not miss Dolginovs estimate for Mars by a half order of magnitude. The upper limit of field of Venus falls 3 orders below this trend. If Mars has a core theorized to be some 1780 km in radius, a weak dynamo resulting in a surface field close to the limiting 10 – 100 nT range suggested by earlier spacecraft observations is not inconsistent with the trend of the other planets. The deviation of Earth and Io from a line joining the other planets is thought to be due to the added vigor of convection from their extra sources of energy. For Earth this would likely be caused by freezing of the inner core whereas for Io the enhancement would be from tidal heating as previously suggested.

The ECESIS Economic-Demographic Model of the United States

ECESIS is an interregional economic-demographic model of the United States. It consists of 51 regional econometric models (one for each state and the District of Columbia) and a multiregional demographic model. Its distinguishing feature is the linking of sophisticated demographic accounts with sophisticated structural econometric models. Emphasis is on the linkages in order to assess the need for and benefits from modeling economic-demographic feedbacks.

Wage Rate Specfication in Regional and Interregional Econometric Models

This paper addresses the issue of wage specification in interregional econometric models. Several shortcomings in commonly used regional wage specifications are demonstrated, and a new inter-regional wage specification is developed. It is based on a wage transmission variable that is simple to construct and that can be applied uniformly to diverse regions. Empirical tests demonstrate the usefulness of this specification.

Efficiency of the Cyber 205 for Stochastic Simulations of a Simultaneous, Nonlinear, Dynamic Econometric Model

A code for carrying out stochastic simulations of the MPS econometric model, a simultaneous, nonlinear, dynamic system of equations involving approximately 600 variables, was adapted to run on the CYBER 205 vector processor. The effi ciency gained relative to performance on an IBM 3081-GX was significant: the CPU time re quired for a given simulation was reduced by a factor of 10 to 12. However, this is considerably smaller than the theoretical gain expected from the manufacturers ratings. The basic causes of this smaller gain are the fairly small real memory of the CYBER 205 and the inefficiency of the CYBER 205 vector processor in performing ele mentary function look-up. The primitive nature of the operating system and some aspects of the FORTRAN compiler on the CYBER 205 required more reprogramming of our large code than such a conversion should demand. However, potential gains from such a conversion will be great once the deficiencies described above are eliminated.

Time series evidence on the linkage between the volatility and growth of output

Prior research on the relationship between volatility and growth has produced mixed results. However, the times series research has only been done for a few countries, namely the United States, United Kingdom and Japan. We extend the prior research by performing a systematic search over several GARCH in mean model specifications, including nonGaussian and asymmetric GARCH models, for 20 OECD countries. The results indicate very little evidence of any connection between volatility and growth.