3 resultados para Demand forecast
em University of Connecticut - USA
Resumo:
Recent studies on the history of economic development demonstrate that concentration of power on a monarch or a ruling coalition impedes economic growth and that institutional changes that diffuse power, though beneficial to the society in general, are opposed by some social groups. In November 2005, Kenyans rejected a proposed constitution primarily because it did not reduce the powers of the executive to any significant degree. Using data of voting patterns in the constitutional referendum and following the rational choice framework, I estimate a model of the demand for power diffusion and demonstrate that groups voting decisions depend on expected gains and likelihood of monopolizing power. The results also reveal the importance of ethnic divisions in hindering the power diffusion process, and therefore the study establishes a channel through which ethnic fragmentation impacts on economic development.
Resumo:
This paper estimates the aggregate demand for private health insurance coverage in the U.S. using an error-correction model and by recognizing that people are without private health insurance for voluntary, structural, frictional, and cyclical reasons and because of public alternatives. Insurance coverage is measured both by the percentage of the population enrolled in private health insurance plans and the completeness of the insurance coverage. Annual data for the period 1966-1999 are used and both short and long run price and income elasticities of demand are estimated. The empirical findings indicate that both private insurance enrollment and completeness are relatively inelastic with respect to changes in price and income in the short and long run. Moreover, private health insurance enrollment is found to be inversely related to the poverty rate, particularly in the short-run. Finally, our results suggest that an increase in the number cyclically uninsured generates less of a welfare loss than an increase in the structurally uninsured.
Resumo:
This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.