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Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models AgEcon
Ramirez, Octavio A.; Fadiga, Mohamadou L..
The performance of a proposed asymmetric-error GARCH model is evaluated in comparison to the normal-error- and Student-t-GARCH models through three applications involving forecasts of U.S. soybean, sorghum, and wheat prices. The applications illustrate the relative advantages of the proposed model specification when the error term is asymmetrically distributed, and provide improved probabilistic forecasts for the prices of these commodities.
Tipo: Journal Article Palavras-chave: GARCH; Nonnormality; Skewness; Time-series forecasting; U.S. commodity prices; Demand and Price Analysis.
Ano: 2003 URL: http://purl.umn.edu/30714
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Issues and Strategies for Aggregate Supply Response Estimation for Policy Analyses AgEcon
Ramirez, Octavio A.; Mohanty, Samarendu; Carpio, Carlos E.; Denning, Megan.
We demonstrate the use of the small-sample econometrics principles and strategies to come up with reliable yield and acreage models for policy analyses. We focus on demonstrating the importance of proper representation of systematic and random components of the model for improving forecasting precision along with more reliable confidence intervals for the forecasts. A probability distribution function modeling approach, which has been shown to provide more reliable confidence intervals for the dependent variable forecasts than the standard models that assume error term normality, is used to estimate cotton supply response in the Southeastern United States.
Tipo: Journal Article Palavras-chave: Nonnormality; Probability distribution function; Supply response; Q11; Q18; C32.
Ano: 2004 URL: http://purl.umn.edu/43420
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Profile likelihood for estimation and confidence intervals AgEcon
Royston, Patrick.
Normal-based confidence intervals for a parameter of interest are inaccurate when the sampling distribution of the estimate is nonnormal. The technique known as profile likelihood can produce confidence intervals with better coverage. It may be used when the model includes only the variable of interest or several other variables in addition. Profile-likelihood confidence intervals are particularly useful in nonlinear models. The command pllf computes and plots the maximum likelihood estimate and profile likelihood–based confidence interval for one parameter in a wide variety of regression models.
Tipo: Article Palavras-chave: Pllf; Profile likelihood; Confidence interval; Nonnormality; Nonlinear model; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119282
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