| 000 | 02458cam a22003497 4500 | ||
|---|---|---|---|
| 001 | w26505 | ||
| 003 | NBER | ||
| 005 | 20211020103934.0 | ||
| 006 | m o d | ||
| 007 | cr cnu|||||||| | ||
| 008 | 210910s2019 mau fo 000 0 eng d | ||
| 100 | 1 |
_aLehrer, Steven F. _933148 |
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| 245 | 1 | 0 |
_aDoes High Frequency Social Media Data Improve Forecasts of Low Frequency Consumer Confidence Measures? / _cSteven F. Lehrer, Tian Xie, Tao Zeng. |
| 260 |
_aCambridge, Mass. _bNational Bureau of Economic Research _c2019. |
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| 300 |
_a1 online resource: _billustrations (black and white); |
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| 490 | 1 |
_aNBER working paper series _vno. w26505 |
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| 500 | _aNovember 2019. | ||
| 520 | 3 | _aSocial media data presents challenges for forecasters since one must convert text into data and deal with issues related to these measures being collected at different frequencies and volumes than traditional financial data. In this paper, we use a deep learning algorithm to measure sentiment within Twitter messages on an hourly basis and introduce a new method to undertake MIDAS that allows for a weaker discounting of historical data that is well-suited for this new data source. To evaluate the performance of approach relative to alternative MIDAS strategies, we conduct an out of sample forecasting exercise for the consumer confidence index with both traditional econometric strategies and machine learning algorithms. Irrespective of the estimator used to conduct forecasts, our results show that (i) including consumer sentiment measures from Twitter greatly improves forecast accuracy, and (ii) there are substantial gains from our proposed MIDAS procedure relative to common alternatives. | |
| 530 | _aHardcopy version available to institutional subscribers | ||
| 538 | _aSystem requirements: Adobe [Acrobat] Reader required for PDF files. | ||
| 538 | _aMode of access: World Wide Web. | ||
| 588 | 0 | _aPrint version record | |
| 690 | 7 |
_aC58 - Financial Econometrics _2Journal of Economic Literature class. |
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| 690 | 7 |
_aG17 - Financial Forecasting and Simulation _2Journal of Economic Literature class. |
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| 700 | 1 | _aXie, Tian. | |
| 700 | 1 | _aZeng, Tao. | |
| 710 | 2 | _aNational Bureau of Economic Research. | |
| 830 | 0 |
_aWorking Paper Series (National Bureau of Economic Research) _vno. w26505. |
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| 856 | 4 | 0 | _uhttps://www.nber.org/papers/w26505 |
| 856 |
_yAcceso en línea al DOI _uhttp://dx.doi.org/10.3386/w26505 |
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| 942 |
_2ddc _cW-PAPER |
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| 999 |
_c321617 _d280179 |
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