Real Time Econometrics

This paper considers the problems facing decision-makers using econometric models in real time. It identifies the key stages involved and highlights the role of automated systems in reducing the effect of data snooping. It sets out many choices that researchers face in construction of automated systems and discusses some of the possible ways advanced in the literature for dealing with them. The role of feedbacks from the decision-maker’s actions to the data generating process is also discussed and highlighted through an example.specification search, data snooping, recursive/sequential modelling, automated model selection

Analysis of diabetic patients through their examination history

The analysis of medical data is a challenging task for health care systems since a huge amount of interesting knowledge can be automatically mined to effectively support both physicians and health care organizations. This paper proposes a data analysis framework based on a multiple-level clustering technique to identify the examination pathways commonly followed by patients with a given disease. This knowledge can support health care organizations in evaluating the medical treatments usually adopted, and thus the incurred costs. The proposed multiple-level strategy allows clustering patient examination datasets with a variable distribution. To measure the relevance of specific examinations for a given disease complication, patient examination data has been represented in the Vector Space Model using the TF-IDF method. As a case study, the proposed approach has been applied to the diabetic care scenario. The experimental validation, performed on a real collection of diabetic patients, demonstrates the effectiveness of the approach in identifying groups of patients with a similar examination history and increasing severity in diabetes complication

Neural Network Models for Inflation Forecasting: An Appraisal

We assess the power of artificial neural network models as forecasting tools for monthly inflation rates for 28 OECD countries. For short out-of-sample forecasting horizons, we find that, on average, for 45% of the countries the ANN models were a superior predictor while the AR1 model performed better for 21%. Furthermore, arithmetic combinations of several ANN models can also serve as a credible tool for forecasting inflation.Artificial Neural Networks; Forecasting; Inflation

Analysis of Twitter Data Using a Multiple-level Clustering Strategy

Twitter, currently the leading microblogging social network, has attracted a great body of research works. This paper proposes a data analysis framework to discover groups of similar twitter messages posted on a given event. By analyzing these groups, user emotions or thoughts that seem to be associated with specific events can be extracted, as well as aspects characterizing events according to user perception. To deal with the inherent sparseness of micro-messages, the proposed approach relies on a multiple-level strategy that allows clustering text data with a variable distribution. Clusters are then characterized through the most representative words appearing in their messages, and association rules are used to highlight correlations among these words. To measure the relevance of specific words for a given event, text data has been represented in the Vector Space Model using the TF-IDF weighting score. As a case study, two real Twitter datasets have been analyse

Evaluation of the swat model in simulating catchment hydrology : case study of the Modder river basin

Thesis (M. Tech. (Civil engineering)) - Central University of Technology, free State, 2013Hydrological models have become vital tools for understanding hydrologic processes at the catchment level. In order to use model outputs for tasks ranging from regulation to research, models should be scientifically sound, robust, and tenable. Model evaluation is therefore beneficial in the acceptance of models to support scientific research and to guide policy, regulatory, and management decision-making. The objective of this study was to evaluate the performance of the SWAT model in simulating stream flow for the Modder River Basin. The study area is situated at -29° 11’ latitude and 26° 6’ longitude at an elevation of 1335 m and drains a land area of 949 km2. The land cover is mainly grassland (pasture) with other minor land use types. The climate of the area is semi-arid with Mean Annual Precipitation (MAP) of 563 mm. Two techniques that are widely used in evaluating models, namely quantitative statistics and graphical techniques, were applied to evaluate the performance of the SWAT model. Three quantitative statistics, namely Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the mean square error to the standard deviation of measured data (RSR), in addition to the graphical techniques, were identified to be used in model evaluation. Results of calibration and validation of the model at a monthly time step gave NSE of 0.65, Pbias of 15 and RSR of 0.4, while NSE of 0.5, Pbias of 31 and RSR of 0.5 were recorded for validation. According to monthly model performance ratings, the model performed well during calibration and performed satisfactory during the validation stage