Time Series Analysis by State Space Methods: Second Edition

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Filtering with State-Observation Examples via Kernel Monte Carlo Filter (2013, background): “ous in science and engineering. We often wish to extract useful information from such time-series data. State-space models have been one of the most successful approaches for this purpose (see, e.g., Durbin and Koopman (2012)). Suppose that we have a sequence of observations y 1;:::;y t;:::;y T. A s”
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting (2018, methodology): “We refer the reader to [39] for a more detailed presentation of this model.”
Tracking the Slowdown in Long-Run GDP Growth (2017, result): “by the recent growth literature such as Fernald and Jones (2014). Lawrence Summers and Robert Gordon have articulated a particularly pessimistic view of long-run growth which contrasts with the optimism prevailing before the Great Recession (see Jorgenson et al., 2006). To complement this evidence, ”