Time varying VARs with inequality restrictions

In many applications involving time-varying parameter VARs, it is desirable to restrict the VAR coe¢ cients at each point in time to be non-explosive. This is an example of a problem where inequality restrictions are imposed on states in a state space model. In this paper, we describe how existing MCMC algorithms for imposing such inequality restrictions can work poorly (or not at all) and suggest alternative algorithms which exhibit better performance. Furthermore, previous algorithms involve an approximation relating to a key integrating constant. Our algorithms are exact, not involving this approximation. In an application involving a commonly-used U.S. data set, we show how this approximation can be a poor one and present evidence that the algorithms proposed in this paper work well

STOCHASTIC FOOD PRICES AND SLASH-AND-BURN AGRICULTURE

Demand and Price Analysis, Farm Management,

THE ESSENTIALS OF RAINFALL DERIVATIVES AND INSURANCE

This paper investigates the use of rainfall insurance to manage agricultural production risks. A number of rainfall insurance products are presented along with a raitonal model which identifies the economics of rainfall. The use of rainfall insurance will increase in future years as capital markets, financial institutions, reinsurance companies, crop insurance companies, and hedge funds collectively organize to share and distribute weather risks. The focus of this paper is in fact directed towards the intermediation function of risk markets rather than on end user benefits.Risk and Uncertainty,

Point process modeling of wildfire hazard in Los Angeles County, California

The Burning Index (BI) produced daily by the United States government's National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS401 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Misc. Pub. 91-1

I submit herewith the annual report of the Agricultural and Forestry Experiment Station, School of Agriculture and Land Resources Management, University of Alaska Fairbanks, for the period ending December 31,1990. This is done in accordance with an act of the Congress, approved March 2,1887, entitled "An act to establish Agricultural Experiment Stations, in connection with the Agricultural Colleges established in the several states under the provisions of an act approved July 2,1862, and under the acts supplementary thereto," and also of the act of the Alaska Territorial Legislature, approved March 12,1935, accepting the provisions of the act of Congress. James V. Drew, DirectorStatement of Purpose -- Plant and Animal Sciences -- Forest Sciences -- Resources Management -- Financial Statement -- Publications - Staf

Responding to the Coffee Crisis: What Can We Learn from Price Dynamics

We develop a semi-structural price vector autoregression to capture coffee price dynamics over various time horizons. The presence of the International Coffee Agreement is permitted to alter supply responses to price signals through yield and planting effects. In the short run, the ICA caused Brazilian farm prices to become disconnected from international prices. In the long run, the ICA promoted supply response by providing a stable environment in which producers could use current price information to predict future prices. In the intermediate run, it muted supply response by necessitating an institutional price wedge between wholesale and farm level prices. In net, the ICA created a price cycle that does not exist in non-ICA periods. Oxfam's proposal to burn 300 million pounds of coffee will provide temporary relief to farmers, but cannot be used repeatedly as a long term strategy. The low coffee prices experienced since the disintegration of the ICA may be due to the interaction of supply lags, a shift in the composition of coffee demand, and low price response due to price uncertainty. No evidence of asymmetric price transmission is found.