{'fraction_non_alphanumeric': 0.029689608636977057, 'fraction_numerical': 0.0, 'mean_word_length': 6.134615384615385, '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}}
Inference on Time-Invariant Variables Using Panel Data: A Pretest Estimator

This paper proposes a new pretest estimator of panel data models including time-invariant variables based on the Mundlak-Krishnakumar estimator and an "unrestricted" Hausman-Taylor estimator. Furthermore, the paper evaluates the biases of currently used estimators: repeated between, ordinary least squares, two-stage restricted between, Oaxaca-Geisler estimator, fixed effect vector decomposition, and generalized least squares. Some of these may lead to erroneous conclusions regarding the statistical significance of the estimated parameter values of time-invariant variables, especially when time-invariant variables are correlated with the individual effects.