Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more than 10% of total GDP. Despite the abundance of data available through tax forms and other sources, it is unclear which factors influence private donation, and a reliable predictive mechanism remains elusive. This study aims to develop predictive models to accurately estimate future charitable giving based on a set of potentially influential factors. We have selected several factors, including unemployment rate, household income, poverty level, population, sex, age, ethnicity, education level, and number of vehicles per household. This study sheds light on the relationship between donation and these variables. We use Stepwise Regression to identify the most influential variables among the available variables, based on which predictive models are developed. Multiple Linear Regression (MLR) and machine learning techniques, including Artificial Neural Networks (ANN) and Support Vector Regression (SVR) are used to develop the predictive models. The results suggest that population, education level, and the amount of charitable giving in the previous year are the most significant, independent variables. We propose three predictive models (MLR, ANN, and SVR) and validate them using 10-fold cross-validation method, then evaluate the performance using 9 different measuring criteria. All three models are capable of predicting the amount of future donations in a given region with good accuracy. Based on the evaluation criteria, using a test data set, ANN outperforms SVR and MLR in predicting the amount of charitable giving in the following year.
This research utilized five economic factors; 1) Consumer Price Index, 2) Return on Treasury Securities, 3) Total Nonfarm payroll, 4) Jobless Claims Filed, and 5) Stand & Poor 500 index to predict US unemployment rate. Historical time series data was obtained from the Economic Research web site of the Federal Reserve Bank of St. Louis and other finance web site. Multiple Linear Regression, Back Propagation Algorithm, and Support Vector Regression techniques were utilized to predict US unemployment rate. Based on Mean Squared Error and adjusted R2 values, the Support Vector Regression technique provided superior results for the given dataset. Future US unemployment rate was predicted with an average absolute error value of 0.815, 0.13 and 0.
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