By Van-Nam Huynh, Vladik Kreinovich, Songsak Sriboonchitta
This ebook is dedicated to the research of causal inference which is likely one of the so much tricky initiatives in facts research: while phenomena are saw to be comparable, it's always tough to choose no matter if one in every of them causally affects the opposite one, or even if those phenomena have a standard reason. This research is the main target of this quantity.
To get an exceptional figuring out of the causal inference, you will need to have types of monetary phenomena that are as actual as attainable. due to this want, this quantity additionally includes papers that use non-traditional financial types, comparable to fuzzy versions and types acquired through the use of neural networks and information mining innovations. It additionally includes papers that follow assorted econometric types to investigate real-life fiscal dependencies.
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Additional info for Causal Inference in Econometrics
Copicentro Granada S L (2014). : Time Series Analysis by State Space Methods. Number 24 in Oxford Statistical Science Series. : The estimation and application of long memory time series models. J. Time Ser. Anal. : Large Sample Inference for Long Memory Processes. : An introduction to long-memory time series models and fractional differencing. J. Time Ser. Anal. : When long memory meets the Kalman filter: a comparative study. Comput. Stat. Data Anal. : How can we define the concept of long memory?
The overriding factor that has retarded the use of spatial econometric analyses to date has been in assembling the necessary data to undertake them effectively. This drawback has been reduced in recent years and, together with advances in econometric methods, there is now greater opportunity for a renewed emphasis on accumulating knowledge of spatial impacts to inform agricultural policy making, particularly how it affects smallholders. In the next section of the paper, we outline these advances in data availability, focusing on geographic information system (GIS) and global positioning system (GPS) mapping, so-called ‘big data’ and panel data collection to reflect the presence of spatial and temporal heterogeneity (the uneven distribution of phenomena of interest across space and time).
81] used a panel data set to investigate the determinants of forest clearing in Indonesia. They observed that, until recently, empirical research into the economic dynamics of forest clearing has been hindered by a lack of spatially disaggregated time-series data. However, they claimed that the emergence of more effective ways of translating satellite images into credible estimates of forest clearing have made it possible to conduct econometric analyses of these dynamics at a high level of spatial and temporal disaggregation.