Handling missing data in time series is a complex problem due to the presence of temporal dependence. General-purpose imputation methods, while widely used, often distort key statistical properties of the data, such as variance and dependence structure, leading to biased estimation and misleading inference. These issues become more pronounced in models that explicitly rely on capturing serial dependence, as standard imputation techniques fail to preserve the underlying dynamics. This paper proposes a novel multiple imputation method specifically designed for parameter estimation in observation-driven models (ODM). The approach takes advantage of the iterative nature of the systematic component in ODM to propagate the dependence structure through missing data, minimizing its impact on estimation. Unlike traditional imputation techniques, the proposed method accommodates continuous, discrete, and mixed-type data while preserving key distributional and dependence properties. We evaluate its performance through Monte Carlo simulations in the context of GARMA models, considering time series with up to 70\% missing data. An application to the proportion of stocked energy stored in South Brazil further demonstrates its practical utility.

翻译：时间序列中缺失数据的处理因存在时间依赖性而成为复杂问题。通用插补方法虽被广泛使用，但常会扭曲数据的关键统计特性（如方差与依赖结构），导致估计偏差与误导性推断。在明确依赖捕捉序列依赖性的模型中，这些问题尤为突出，因为标准插补技术无法保持潜在动态特性。本文提出一种专为观测驱动模型参数估计设计的新型多重插补方法。该方法利用ODM中系统分量的迭代特性，通过缺失数据传播依赖结构，从而最小化其对估计的影响。与传统插补技术不同，所提方法能同时处理连续型、离散型及混合型数据，并保持关键分布特性与依赖属性。我们通过在GARMA模型框架下的蒙特卡洛模拟评估其性能，考虑了缺失率高达70%的时间序列。对巴西南部储能比例数据的应用进一步证明了该方法的实用价值。