A Formal Privacy Framework for Partially Private Data

Despite its many useful theoretical properties, differential privacy (DP) has one substantial blind spot: any release that non-trivially depends on confidential data without additional privacy-preserving randomization fails to satisfy DP. Such a restriction is rarely met in practice, as most data releases under DP are actually "partially private" data (PPD). This poses a significant barrier to accounting for privacy risk and utility under logistical constraints imposed on data curators, especially those working with official statistics. In this paper, we propose a privacy definition which accommodates PPD and prove it maintains similar properties to standard DP. We derive optimal transport-based mechanisms for releasing PPD that satisfy our definition and algorithms for valid statistical inference using PPD, demonstrating their improved performance over post-processing methods. Finally, we apply these methods to a case study on US Census and CDC PPD to investigate private COVID-19 infection rates. In doing so, we show how data curators can use our framework to overcome barriers to operationalizing formal privacy while providing more transparency and accountability to users.Comment: 31 pages, 7 figure

Does sentiment matter for stock market returns? evidence from a small European market

An important issue in finance is whether noise traders, those who act on information that has no value, influence prices. Recent research indicates that investor sentiment affects the return distribution of a few categories of assets in some stock markets. Other studies also document that US investor sentiment is contagious. This paper investigates whether Consumer Confidence (CC) and the Economic Sentiment Indicator (ESI) – as proxies for investor sentiment – affects Portuguese stock market returns, at aggregate and industry levels, for the period between 1997 to 2009. Moreover the impact of US investor sentiment on Portuguese stock market returns is also addressed. We find several interesting results. First, our results provide evidence that consumer confidence index and ESI are driven by both, rational and irrational factors. Second, ESI is significantly negative related to stock returns. Sentiment negatively forecasts aggregate stock market returns, but not all industry indices returns. Finally, we don’t find a contagious effect of US investor sentiment in Portuguese market returns

Exploring Park Quality in Urban Setting with Environmental Justice, Alternative Measurements, and Social Interaction

With rapid urbanization, urban green resources, such as parks have become important assets for quality of life in urban settings. Parks provide urban residents with both physical and psychological health benefits through various mechanisms such as physical activity and social interaction. Quality is an important non-spatial dimension of urban parks and has started to gain attention among researchers. To better understand park quality in an urban setting, additional knowledge should be explored. This dissertation studies the quality of urban parks from three different perspectives: 1) the equal distribution of park quality resources and its relationship to environmental justice issues, 2) the protocols used for measuring the most commonly acknowledged non-spatial dimensions of urban parks, and 3) the association between park quality and social interaction in urban parks. This study explores park quality from those three different perspectives and presents findings in a 3-part dissertation. The first part determines whether the distribution of park quality was spatially autocorrelated and assessed the associations between separate park features qualities, overall park quality, and multiple indicators of environmental justice issues via a case study in Cache County, Utah; The second part of this study conducts a systematic study to analyze and synthesize the different developed approaches used for assessing non-spatial dimensions of urban parks including park quality and draws implications for future urban landscape planning, design, and research; The third part uses a case study in Logan and North Logan, Utah, and explores the associations between park quality and people’s social interaction in urban parks through an innovatively systematic observational protocol

The relative value relevance of cash flow accounting disclosures by South African Banks

During recent decades, researchers have developed the value relevance method of accounting based research. Value relevance, at its core, attempts to describe the information usefulness of a disclosure figure in relation to the impact it has on the market values of a given stock. Much of the focus of this research, both internationally and locally, has been based on earnings or balance sheet disclosures with little attention being paid to other sections of disclosure. This study takes the use of value relevance methods one step further and analyses the information usefulness of operating cash flow disclosures of financial firms versus non-financial firms in a South African context. The study proceeds to explain and then test the presumption that the nature of the banking business model makes operating cash flow disclosures irrelevant; some interesting and somewhat counter-intuitive results are obtained

Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection

The automatic collection and increasing availability of health data provides a new opportunity for techniques to monitor this information. By monitoring pre-diagnostic data sources, such as over-the-counter cough medicine sales or emergency room chief complaints of cough, there exists the potential to detect disease outbreaks earlier than traditional laboratory disease confirmation results. This research is particularly important for a modern, highly-connected society, where the onset of disease outbreak can be swift and deadly, whether caused by a naturally occurring global pandemic such as swine flu or a targeted act of bioterrorism. In this dissertation, we first describe the problem and current state of research in disease outbreak detection, then provide four main additions to the field. First, we formalize a framework for analyzing health series data and detecting anomalies: using forecasting methods to predict the next day's value, subtracting the forecast to create residuals, and finally using detection algorithms on the residuals. The formalized framework indicates the link between the forecast accuracy of the forecast method and the performance of the detector, and can be used to quantify and analyze the performance of a variety of heuristic methods. Second, we describe improvements for the forecasting of health data series. The application of weather as a predictor, cross-series covariates, and ensemble forecasting each provide improvements to forecasting health data. Third, we describe improvements for detection. This includes the use of multivariate statistics for anomaly detection and additional day-of-week preprocessing to aid detection. Most significantly, we also provide a new method, based on the CuScore, for optimizing detection when the impact of the disease outbreak is known. This method can provide an optimal detector for rapid detection, or for probability of detection within a certain timeframe. Finally, we describe a method for improved comparison of detection methods. We provide tools to evaluate how well a simulated data set captures the characteristics of the authentic series and time-lag heatmaps, a new way of visualizing daily detection rates or displaying the comparison between two methods in a more informative way

Morocco's exports today: A real legacy from the past

Exports of goods and services are a major driver of the economic and social development of countries, especially in the case of small open economies, such as Morocco. Since independence, Moroccan exports have been characterised by a concentration on basic products and a relatively weak evolution in comparison with competitors. Since 2005, these exports have undergone a profound structural transformation. The integration of the Moroccan economy in the new world trades has changed the base of specialisation of the country. It has gone from a country specialised in the production and export of agricultural products and textiles to a country producing and exporting automobile, electrical and aeronautical products. Therefore, forecasting Moroccan exports are of great importance for policy makers and government. The most popular and appropriate model for forecasting macroeconomic series in the literature are times series models. In this paper, we test whether the Auto Regressive Moving Average (ARMA) model can forecast Moroccan exports. To do so, we follow the modelling process developed by Box & Jenkins (1976), namely: identification of the susceptible models, parameter estimates and validation testing. The results show that satisfactory forecasts can be obtained using the ARIMA model (0,1,0) or AR (1). This confirms the idea that the behaviour of exports today is the result of a rich historical antecedent of events. The examination of the country's economic history allows us to deduce the importance of structural factors for the development of exports. This analysis can serve as the basis for a multivariate model for the behaviour of Moroccan exports in the future.Exports of goods and services are a major driver of the economic and social development of countries, especially in the case of small open economies, such as Morocco. Since independence, Moroccan exports have been characterised by a concentration on basic products and a relatively weak evolution in comparison with competitors. Since 2005, these exports have undergone a profound structural transformation. The integration of the Moroccan economy in the new world trades has changed the base of specialisation of the country. It has gone from a country specialised in the production and export of agricultural products and textiles to a country producing and exporting automobile, electrical and aeronautical products. Therefore, forecasting Moroccan exports are of great importance for policy makers and government. The most popular and appropriate model for forecasting macroeconomic series in the literature are times series models. In this paper, we test whether the Auto Regressive Moving Average (ARMA) model can forecast Moroccan exports. To do so, we follow the modelling process developed by Box & Jenkins (1976), namely: identification of the susceptible models, parameter estimates and validation testing. The results show that satisfactory forecasts can be obtained using the ARIMA model (0,1,0) or AR (1). This confirms the idea that the behaviour of exports today is the result of a rich historical antecedent of events. The examination of the country's economic history allows us to deduce the importance of structural factors for the development of exports. This analysis can serve as the basis for a multivariate model for the behaviour of Moroccan exports in the future

Stock Market Integration and International Portfolio Diversification between U.S. and ASEAN Equity Markets

The paper empirically analyzes stock market integration and the benefit possibilities of international portfolio diversification across the Southeast Asia (ASEAN) and U.S. equity markets. It employs daily sample of 6 ASEAN equity market indices and S&P 500 index as a proxy of U.S. market index from years 2001 to 2010. The paper examines the stock market return interdependence from three different perspectives which are ‘long-term’, ‘short-term’ and ‘dynamic’ perspectives. In order to investigate the long-run interdependencies, the Johansen-Juselius multivariate co-integration test and the bivariate Engle-Granger 2-step method were used. In respect to the short-run interdependencies, the Generalized Impulse Response Function (GIRF) and the Generalized Forecast Error Variance Decomposition (GFEVD) are employed. Finally, to assess the dynamic structure of equity market co-movements, the Dynamic Conditional Correlation (DCC) model is engaged. Results suggest that in the long-run, there are no potential benefits in diversifying investment portfolios across the ASEAN and U.S. market since there are evidences of cointegration among them. However, the potential benefits of international portfolio diversification can be seen throughout the short-run-period. Subsequently, the DCC findings suggest an overall proposition that by the end of 2010, most of the ASEAN markets do not share the U.S. stock price movement

Stock Market Integration and International Portfolio Diversification between U.S. and ASEAN Equity Markets

The paper empirically analyzes stock market integration and the benefit possibilities of international portfolio diversification across the Southeast Asia (ASEAN) and U.S. equity markets. It employs daily sample of 6 ASEAN equity market indices and S&P 500 index as a proxy of U.S. market index from years 2001 to 2010. The paper examines the stock market return interdependence from three different perspectives which are ‘long-term’, ‘short-term’ and ‘dynamic’ perspectives. In order to investigate the long-run interdependencies, the Johansen-Juselius multivariate co-integration test and the bivariate Engle-Granger 2-step method were used. In respect to the short-run interdependencies, the Generalized Impulse Response Function (GIRF) and the Generalized Forecast Error Variance Decomposition (GFEVD) are employed. Finally, to assess the dynamic structure of equity market co-movements, the Dynamic Conditional Correlation (DCC) model is engaged. Results suggest that in the long-run, there are no potential benefits in diversifying investment portfolios across the ASEAN and U.S. market since there are evidences of cointegration among them. However, the potential benefits of international portfolio diversification can be seen throughout the short-run-period. Subsequently, the DCC findings suggest an overall proposition that by the end of 2010, most of the ASEAN markets do not share the U.S. stock price movement