Journal ArticleDOI
Heteroscedastic price forecasting for food security management in developing countries
Reads0
Chats0
TLDR
In this paper, extended GARCH models were used to forecast food price in low-income countries. But the accuracy of the forecast was not as good as the original GARCH model. And the accuracy was not sufficient for the analysis of food price data from Madagascar.Abstract:
Price forecasting systems are of considerable importance to food security management by governments’ and non‐governmental organizations. Sparse data availability in low‐income economies, however, generally necessitates reliance on reduced form forecasting methods. Relatively recent innovations in heteroscedasticity‐consistent time series techniques offer price forecasting tools that are feasible given available data and analysis technologies in low‐income economies. Moreover, extended GARCH models exhibit superior out‐of‐sample forecast accuracy using monthly food price data from Madagascar. These techniques also permit cost reduction in food security operations by more precise estimation of the risk of hitting a critical price level.read more
Citations
More filters
BookDOI
Principles of forecasting
TL;DR: A review of the evidence showed that role playing was effective in matching results for seven of eight experiments and was correct for 56 percent of 143 predictions, while unaided expert opinions were correct for 16 percent of 172 predictions.
Posted Content
Food Security and Food Assistance Programs
TL;DR: In this paper, the authors discuss both food security and food assistance programs and present a basic analytical model of food security, approached as a subtopic of the economics of health, and discuss the multiple threats to food security.
Posted Content
Food security and food assistance programs
TL;DR: In this article, the authors discuss both food security and food assistance programs designed to respond to the threat of food insecurity, and propose a framework to attain and maintain food security in the United States.
Book ChapterDOI
Chapter 40 Food security and food assistance programs
TL;DR: In this article, the authors discuss both food security and food assistance programs designed to respond to the threat of food insecurity, and propose a framework to attain and maintain food security in the United States.
Journal ArticleDOI
Empirical Forecasting of Slow-Onset Disasters for Improved Emergency Response: An Application to Kenya's Arid North
Andrew G. Mude,Christopher B. Barrett,John G. McPeak,Robert Kaitho,Robert Kaitho,Patti Kristjanson +5 more
TL;DR: This article developed an empirical model, based on a relatively parsimonious set of regularly measured variables from communities in Kenya's arid north, that generates remarkably accurate forecasts of the likelihood of famine with at least 3 months lead time.
References
More filters
Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Journal ArticleDOI
Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
Journal ArticleDOI
Time Series Analysis Forecasting and Control
TL;DR: This revision of a classic, seminal, and authoritative book explores the building of stochastic models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.