Antonio Aguirre

Time series analysis of monthly beef cattle prices with nonlinear autoregressive models

This paper analyses the dynamics underlying a time series of the monthly average beef cattle price received by producers in the State of São Paulo (Brazil). The time series under study records monthly prices since 1954. An exploratory analysis suggested that after a period of intense government intervention in the cattle and beef markets, the underlying dynamics seem to be settling to a pattern similar to the one observed prior to that period. In order to try to verify if the underlying dynamics after the interventionist phase are similar to those in former times, a forecasting procedure has been used based on nonlinear autoregressive models. This tye of models were used after the BDS test showed significant results which can be interpreted as nonlinearities in the data. The results discussed in the paper seem to suggest that after a period of intense interventions that lasted over two decades, the current underlying dynamics are close (from a forecasting point of view) to those observed more than thirty years ago.

Redução dos custos de quantificação de benefícios na avaliação contingente

The so-called contingent valuation method is frequently used to quantify the benefits brought about by the implementation of a project with environmental impacts. This method uses data collected in field researches conducted among the potential beneficiaries of the project. The objective of this paper is to show how to use information contained in existing studies for similar projects as a possible way to reduce the costs of the evaluation process. The paper proposes that such information, which supplies preliminary indicators for some crucial parameters, allows to narrow the scope of choices among projects of the same type in new places. The type of project selected for analysis is the construction of a sewage system in several middle-size cities in Brazil.

Analysis of Economic Time Series Using Narmax Polynomial Models

This chapter presents a methodology to fit nonlinear autoregressive moving average polynomial models with exogenous variables (NARMAX) to observed data. Because the models are nonlinear, it is sometimes possible to perform a more accurate analysis than if linear models were used. On the other hand, the model structure, that is, the set of independent variables has to be chosen with great care lest dynamically meaningless models be fitted to the data. An effective algorithm, that can be used to select the “best” regressors from a set of candidates, is reviewed with details and a simple version of its code is provided. The chapter has been designed to serve as a tutorial introduction to modeling and analysis using NARMAX polynomials of which linear and no-input (univariate) models are special cases. The main features of the approach are illustrated using time series of beef-cattle prices for the Brazilian State of So Paulo.