Econometric Modelling with Mixed Frequency and Temporally Aggregated Data

Frequency Domain Predictive Modelling with Aggregated Data

Existing work in spatio-temporal data analysis invariably assumes data available as individual measurements with localised estimates. However, for many applications like econometrics, financial forecasting and climate science, data is often obtained as aggregates. Data aggregation presents severe mathematical challenges to learning and inference, and application of standard techniques is suscep...

Frequency Domain Predictive Modelling with Aggregated Data Supplementary Material

Topic modelling approaches to aggregated citation data

In this research in progress paper we report on preliminary results from the proposed novel uses of topic modelling approaches to bibliographic references as sources for “bags-of-words” instead of actual text content in scientometric settings. The actual cited references, viewed as concept symbols for paradigmatic approaches to earlier research, could thereby be used to cluster research. We wil...

Causal Discovery from Temporally Aggregated Time Series

Discovering causal structure of a dynamical system from observed time series is a traditional and important problem. In many practical applications, observed data are obtained by applying subsampling or temporally aggregation to the original causal processes, making it difficult to discover the underlying causal relations. Subsampling refers to the procedure that for every k consecutive observa...

Estimation of temporally aggregated multivariate GARCH models

This paper investigates the performance of quasi maximum likelihood (QML) and nonlinear least squares (NLS) estimation applied to temporally aggregated GARCH models. Since these are known to be only weak GARCH, the conditional variance of the aggregated process is in general not known. Thus, one major condition that is often used in proving the consistency of QML, the correct specification of t...