BackgroundCommunity mobilisation is an important component of a participatory approach to health and development interventions. However, it is challenging to define, measure and assess community participation and ownership of a programme, especially at scale.MethodsAn iterative cross-sectional survey was designed for implementation across a representative sample of community-based groups, using a weighted index that captured both qualitative and quantitative data in a standardised form. These data were aggregated at the level of individual groups, as well as state-wide or across the whole programme. Community participation in the survey is a primary feature of the methodology and was integral to the process of designing the index and administering the survey.ResultsThe survey provided programme management and communities with objective tools for monitoring community mobilisation across a large-scale and complex intervention covering 32 districts in India. The implementation of the survey engaged communities in an open discussion of their goals and capabilities and helped them to challenge the power dynamics between themselves and other stakeholders.ConclusionsIt is possible to translate the theoretical premises of participatory development into a tool that both measures and fosters meaningful participation. The active participation of community members in the collection and analysis of data on their mobilisation suggests that monitoring of participation can be undertaken to inform a scaled-up programme and can be a useful intervention in its own right.

<p>This paper reviews the theory and applications related to fractionally integrated generalized autoregressive conditional heteroscedastic (FIGARCH) models, mainly for describing the observed persistence in the volatility of a time series. The long memory nature of FIGARCH models allows to be a better candidate than other conditional heteroscedastic models for modeling volatility in exchange rates, option prices, stock market returns and inflation rates. We discuss some of the important properties of FIGARCH models in<br />this review. We also compare the FIGARCH with the autoregressive fractionally integrated moving average (ARFIMA) model. Problems related to parameter estimation and forecasting using a FIGARCH model are presented. The application of a FIGARCH model to exchange rate data is discussed. We briefly introduce some other models, that are closely related to FIGARCH models. The paper ends with some concluding remarks and future directions of research.</p>

This paper reviews the recent literature on conditional duration modeling in high‐frequency finance. These conditional duration models are associated with the time interval between trades, price, and volume changes of stocks, traded in a financial market. An earlier review by Pacurar provides an exhaustive survey of the first and some of the second generation conditional duration models. We consider almost all of the third‐generation and some of the second‐generation conditional duration models. Notable applications of these models and related empirical studies are discussed. The paper may be seen as an extension to Pacurar.

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