{'fraction_non_alphanumeric': 0.01849217638691323, 'fraction_numerical': 0.008534850640113799, 'mean_word_length': 5.605633802816901, '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}}
The Impact of Measurement Errors on OLS Estimates of Import Demand Parameters

Aggregate import demand equations have always been very popular in applied econometric research because trade theory suggests immediately relevant interpretations of the estimates obtained (see Magee 1975). Accordingly, methodological problems generally arising with the estimation of aggregate demand functions have frequently been discussed in the context of the import demand equation. The seminal paper in this area is Orcutt (1950), which was stimulated by the traditional elasticity approach to exchange rate policy and the fact that the low estimates of import demand price elasticities seemed to indicate a violation of the Marshall Lerner stability condition for the international trade equilibrium. While methodological progress rendered some of the sources of systematic bias in the regression coefficients, as considered by Orcutt, potentially irrelevant), one such source still seems to be with us, although widely neglected: measurement errors, particularly in data on the prices of internationally traded goods. The quality of these data may have been improved during the last few decades, but they should still be regarded as particularly unreliable, compared to most other economic data. Detailed reasons for this are given in Richardson (1976). Hence, if one is not prepared to choose "second best" estimation