Responding to High Commodity Prices

The recent commodity price boom resulted from rising demand in the face of a long period of low supply growth and market distortions. Structural factors are expected to return international commodity prices to relatively high levels as the global economy recovers. The Asian and Pacific economies and the region's poor will be among the most exposed to a rebound in prices. The recent response to high commodity prices can be improved upon. At the global level, the priority for Asian and Pacific governments is to seek a commitment to lessening distortions favouring bio-fuels. At the regional level, there is a need to address the use of trade restrictions on food commodities in Asia, notably rice. At a national level, the long-term neglect of the rural economy warrants correction; and efforts to expand and make social safety nets more 'market friendly' are called for.     

Accounting for technical substitution in the input-output model

Input-output analysis is a general equilibrium economic model that is being applied with increasing frequency to situations in which the input patterns of various economic activities are bound to change. Applications of the model in this context unduly strain the model's customary assumption of fixed coefficients, an assumption which precludes the substitution of inputs in production processes. The focus here is on the development of a general procedure for incorporating input substitution under various sets of conditions into the input-output model.     

Information centre in Washington

The Center for Telecommunications Studies (CTS) of The George Washington University in Washington DC was founded in late 1980, partly as a result of Washington policy-maker recommendations, but also because of the University's existing degree programmes in telecommunications. The Center enjoys strong backing from University leadership. Its mission, much like the field itself, is multidisciplinary.     

Delegating Power to Bureaucracies: Evidence from the States

Empirical analysis has lagged behind theoretical advancementin the study of legislative delegation of power to bureaucracies.This article analyzes why state legislatures delegated advisoryand policy-forming powers to bureaucracies for the Aid to Familieswith Dependent Children (AFDC) program from 1935 through 1996.The analysis supports various theories of bureaucratic discretion,while painting a complex political picture of delegation decisions.Legislators rely on bureaucrats to resolve uncertainty, especiallywhen internal legislative information is scarce. Contrary torecent wisdom, however, delegation is not found to be associatedwith the general condition of unified government. Rather, delegationoccurs under both divided and unified government, but the procedureschosen and appointment powers granted vary under these two conditions.     

Is smarter better? A comparison of adaptive, and simple moving average trading strategies

This study examines the comparative performance of an Adaptive Moving Average (AMA) on the Australian All Ordinaries, Dow Jones Industrial Average, and Standard and Poor's 500 stock market indices. The theoretical advantage of the Adaptive Moving Average over fixed length Simple Moving Average (SMA) trading systems is its ability to automatically respond to changing market conditions dependant upon the level of volatility in the market. While the strategy is confirmed to have some market timing ability, the overall results show returns to the Adaptive Moving Average cannot compensate for the cost of trade therefore lending support for the use of a long run passive strategy.     

Cointegration: how short is the long run?

When testing for cointegration and faced with short sample periods, it is common for researchers to turn to more frequently sampled observations, that is, to move from annual to quarterly or monthly data, in order to increase the number of observations. We argue that such a gain in the degrees of freedom is more apparent than real. Essentially, cointegration is a long-run concept and hence requires long spans of data to give tests for cointegration much power rather than merely large numbers of observations. Using Monte Carlo simulations, we demonstrate this for four popular tests for cointegration.