Three Essays in Public Finance

The first chapter examines the long-run and short-run elasticity of income with respect to changes in tax rates. The Elasticity of Taxable Income (ETI) is a largely-debated parameter in both research and policy. Despite the growing importance of ETI, the literature has not fully considered the intertemporal impacts of taxation. I expand the literature by estimating short-run and long-run impacts of tax rate changes relying on the most recent estimation method and using appropriate lagged values of income when constructing the predicted net-of-tax rate instruments. The short-run ETI in the baseline specification is 0.69 whereas estimates for the Elasticity of Broad Income (EBI) are much smaller and imprecise. The second chapter studies the impact of tax base on the elasticity of income. Most of the existing literature has appropriately used a constant definition of taxable income to focus on the effects of tax rate changes. It is important to recognize that a decrease in the tax base (in the form of a new deduction, exemption, or credit, for example) can create new opportunities for legal tax avoidance without altering real behavior. Using the most recent estimation method, I estimate the impact of tax base on the behavioral responses to taxation. Estimated results for the impact of tax base are much smaller than those in the existing literature. The third chapter examines the possible linkages between school choice and home values. I use home prices to draw inferences about households’ value for school choice, and a Herfindahl-Hirschman Index (HHI) for enrollment among four different types of schools as a proxy measure of school choice. I empirically test two hypotheses: 1) less concentrated counties will have less variability in home prices, 2) less concentrated counties will have higher median home prices. Based on county-level data, I find evidence that an increase in competition for enrollment is associated with a decrease in inequality of home prices within the county. Moreover, I find evidence of an overall increase in home prices within the counties following increased competition for enrollment among schools

Boosting Stock Price Prediction with Anticipated Macro Policy Changes

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach

Advancing Brain Tumor Detection: A Thorough Investigation of CNNs, Clustering, and SoftMax Classification in the Analysis of MRI Images

Brain tumors pose a significant global health challenge due to their high prevalence and mortality rates across all age groups. Detecting brain tumors at an early stage is crucial for effective treatment and patient outcomes. This study presents a comprehensive investigation into the use of Convolutional Neural Networks (CNNs) for brain tumor detection using Magnetic Resonance Imaging (MRI) images. The dataset, consisting of MRI scans from both healthy individuals and patients with brain tumors, was processed and fed into the CNN architecture. The SoftMax Fully Connected layer was employed to classify the images, achieving an accuracy of 98%. To evaluate the CNN's performance, two other classifiers, Radial Basis Function (RBF) and Decision Tree (DT), were utilized, yielding accuracy rates of 98.24% and 95.64%, respectively. The study also introduced a clustering method for feature extraction, improving CNN's accuracy. Sensitivity, Specificity, and Precision were employed alongside accuracy to comprehensively evaluate the network's performance. Notably, the SoftMax classifier demonstrated the highest accuracy among the categorizers, achieving 99.52% accuracy on test data. The presented research contributes to the growing field of deep learning in medical image analysis. The combination of CNNs and MRI data offers a promising tool for accurately detecting brain tumors, with potential implications for early diagnosis and improved patient care

Retail Demand Forecasting Using Neural Networks and Macroeconomic Variables

With the growing competition among firms in the globalized corporate environment and considering the complexity of demand forecasting approaches, there has been a large literature on retail demand forecasting utilizing various approaches. However, the current literature largely relies on micro variables as inputs, thereby ignoring the influence of macroeconomic conditions on households’ demand for retail products. In this study, I incorporate external macroeconomic variables such as Consumer Price Index (CPI), Consumer Sentiment Index (ICS), and unemployment rate along with time series data of retail products’ sales to train a Long Short-Term Memory (LSTM) model for predicting future demand. The inclusion of macroeconomic conditions in the predictive model provides greater explanatory power. As anticipated, the developed model, including this external macroeconomic information, outperforms the model developed without this macroeconomic information, thereby demonstrating strong potential for industry application with improved forecasting capability

Enhancing Traffic Density Detection and Synthesis through Topological Attributes and Generative Methods

This study investigates the utilization of Graph Neural Networks (GNNs) within the realm of traffic forecasting, a critical aspect of intelligent transportation systems. The accuracy of traffic predictions is pivotal for various applications, including trip planning, road traffic control, and vehicle routing. The research comprehensively explores three notable GNN architectures—Graph Convolutional Networks (GCNs), GraphSAGE (Graph Sample and Aggregation), and Gated Graph Neural Networks (GGNNs)—specifically in the context of traffic prediction. Each architecture's methodology is meticulously examined, encompassing layer configurations, activation functions, and hyperparameters. With the primary aim of minimizing prediction errors, the study identifies GGNNs as the most effective choice among the three models. The outcomes, presented in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), reveal intriguing insights. While GCNs exhibit an RMSE of 9.25 and an MAE of 8.2, GraphSAGE demonstrates improved performance with an RMSE of 8.5 and an MAE of 7.6. Gated Graph Neural Networks (GGNNs) emerge as the leading model, showcasing the lowest RMSE of 9.2 and an impressive MAE of 7.0. However, the study acknowledges the dynamic nature of these results, emphasizing their dependency on factors such as the dataset, graph structure, feature engineering, and hyperparameter tuning

Boosting Stock Price Prediction with Anticipated Macro Policy Changes

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach

Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content

Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and contentComment: Incorrectly Inpu

Retail Demand Forecasting: A Comparative Study for Multivariate Time Series

Accurate demand forecasting in the retail industry is a critical determinant of financial performance and supply chain efficiency. As global markets become increasingly interconnected, businesses are turning towards advanced prediction models to gain a competitive edge. However, existing literature mostly focuses on historical sales data and ignores the vital influence of macroeconomic conditions on consumer spending behavior. In this study, we bridge this gap by enriching time series data of customer demand with macroeconomic variables, such as the Consumer Price Index (CPI), Index of Consumer Sentiment (ICS), and unemployment rates. Leveraging this comprehensive dataset, we develop and compare various regression and machine learning models to predict retail demand accurately

Deep Learning-Based COVID-19 Detection from Chest X-ray Images: A Comparative Study

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has rapidly spread across the globe, leading to a significant number of illnesses and fatalities. Effective containment of the virus relies on the timely and accurate identification of infected individuals. While methods like RT-PCR assays are considered the gold standard for COVID-19 diagnosis due to their accuracy, they can be limited in their use due to cost and availability issues, particularly in resource-constrained regions. To address this challenge, our study presents a set of deep learning techniques for predicting COVID-19 detection using chest X-ray images. Chest X-ray imaging has emerged as a valuable and cost-effective diagnostic tool for managing COVID-19 because it is non-invasive and widely accessible. However, interpreting chest X-rays for COVID-19 detection can be complex, as the radiographic features of COVID-19 pneumonia can be subtle and may overlap with those of other respiratory illnesses. In this research, we evaluated the performance of various deep learning models, including VGG16, VGG19, DenseNet121, and Resnet50, to determine their ability to differentiate between cases of coronavirus pneumonia and non-COVID-19 pneumonia. Our dataset comprised 4,649 chest X-ray images, with 1,123 of them depicting COVID-19 cases and 3,526 representing pneumonia cases. We used performance metrics and confusion matrices to assess the models' performance. Our study's results showed that DenseNet121 outperformed the other models, achieving an impressive accuracy rate of 99.44%