EMPIRICAL ANALYSIS OF THE QUANTILE AUTOREGRESSION MODELS / ANÁLISE EMPÍRICA DOS MODELOS DE AUTO-REGRESSÃO QUANTÍLICA
AUTOR(ES)
FABIANO DOS SANTOS SOUZA
DATA DE PUBLICAÇÃO
2007
RESUMO
Autoregressive models (AR(p)) for time series assume that the series dynamics has a linear dependence on past observations up to a lag p, plus an independent and identically distributed (i.i.d.) random error. Quantile autoregressive models (QAR(p)) generalize the AR(p) by allowing different autoregressive coefficients for different quantiles of the conditional distribution and so there is no need for an explicit random error component. This dissertation studies the statistical inference proposed by Koenker e Xiao (2004) for QAR(p) models, by means of Monte Carlo simulations. While the estimation tools show themselves very accurate, the hypothesis test which considers an AR model as the null hypothesis yields poor results, and these vary with the data generating process
ASSUNTO(S)
stochastic processes processos estocasticos monte carlo simulation series temporais auto-regressao quantilica time series quantile autoregression simulacao de monte carlo
