{'fraction_non_alphanumeric': 0.028695652173913042, 'fraction_numerical': 0.006956521739130435, 'mean_word_length': 5.6531791907514455, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Comparison of shrinkage and feature selection methods for modeling GDP of Pakistan

Abstract Modeling economic data through Regression Analysis is one of the central issues in Econometrics. Usually, there exists Multicollinearity among the economic variables. This study is designed to compare various available methods that tackle the problem of Multicollinearity among predictor variables while modeling the Gross Domestic Product (GDP) of Pakistan. Various methods were examined including Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET). Annual time series data for the variables under study for the period 1972 to 2018 were taken from various issues of Pakistan Economic Survey. The accuracy of model-fitting was evaluated using Mean Square Error (MSE), R-Squares, Adjusted R-Squares, Root Mean Squares Error Cross Validated (RMSECV), Bayesian Information Criterion (BIC) and Akaike’s Information Criterion (AIC). This study recommends LASSO to tackle the problem of Multicollinearity among economic variables as it provides better results as compared to other methods utilized in this study.