We put forward a simple new randomized missing data (RMD) approach to robust filtering of state-space models, motivated by the idea that the inclusion of only a small fraction of available highly precise measurements can still extract most of the attainable efficiency gains for filtering latent states, estimating model parameters, and producing out-of-sample forecasts. In our general RMD framework we develop two alternative implementations: endogenous (RMD-N) and exogenous (RMD-X) randomization of missing data. A degree of robustness to outliers and model misspecification is achieved by purposely randomizing over the utilized subset of seemingly highly precise but possibly misspecified or outlier contaminated data measurements in their original time series order, while treating the rest as if missing. Time-series dependence is thus fully preserved and all available measurements can get utilized subject to a degree of downweighting depending on the loss function of interest. The arising robustness-efficiency trade-off is controlled by varying the fraction of randomly utilized measurements or the incurred relative efficiency loss. As an empirical illustration, we show consistently attractive performance of our RMD framework in popular state space models for extracting inflation trends along with model extensions that more directly reflect inflation targeting by central banks.
翻译:我们提出了一个简单、新随机的缺失数据(RMD)方法,用于稳健地筛选国家空间模型,其动机是,仅仅纳入一小部分现有高度精确的测量数据,仍然可以提取大部分可实现的效率收益,以过滤潜伏状态,估计模型参数,并作出抽样预测。在我们的总RMD框架内,我们制定了两种备选执行办法:本地(RMD-N)和外部(RMD-X)对缺失数据随机化。对异常点和模型误分明度的一定程度的强度是通过在最初的时间序列中使用的、似乎非常精确但可能错误地指定或更明显地污染的数据测量子集进行随机随机随机化来实现的,同时将其余数据作为缺失处理。因此,完全保留了时间序列依赖,所有可用的测量方法都可以根据利息损失功能的减量使用。 稳健性交易是通过随机利用测量的一小部分或相对效率损失来控制的。作为经验说明,我们展示了我们的RMD框架在流行的空间模型中一贯具有吸引力的绩效,以便根据中央的通胀趋势,通过更直接的扩展来反映模型,从而更直接地反映通货膨胀趋势。