Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
create a website

Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks. (2024). Dzhunkeev, Urmat.
In: Russian Journal of Money and Finance.
RePEc:bkr:journl:v:83:y:2024:i:1:p:53-76.

Full description at Econpapers || Download paper

Cited: 0

Citations received by this document

Cites: 80

References cited by this document

Cocites: 32

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

    This document has not been cited yet.

References

References cited by this document

  1. Aanes, B. and Gullien, M. (2018). Forecasting Norwegian Inflation with Deep Neural Networks [Master Thesis, Norwegian School of Economics]. Bergen. https:// openaccess.nhh.no/nhh-xmlui/bitstream/handle/11250/2586572/masterthesis.PDF [accessed on 1 December 2023].
    Paper not yet in RePEc: Add citation now
  2. Abramov, V., Morozov, A., Sinyakov, A. and Sterkhova, A. (2022). O roli global’nykh faktorov v inflyatsii [On the Role of Global Factors in Inflation]. Bank of Russia Analytical Note. [In Russian].
    Paper not yet in RePEc: Add citation now
  3. Almosova, A. and Andresen, N. (2023). Nonlinear Inflation Forecasting with Recurrent Neural Networks. Journal of Forecasting, 42(2), pp. 240–259. https://doi.org/10.1002/for.2901.

  4. Angelico, C., Marcucci, J., Miccoli, M. and Quarta, F. (2022). Can We Measure Inflation Expectations Using Twitter? Journal of Econometrics, 228(2), pp. 259–277. https://doi.org/10.1016/j.jeconom.2021.12.008.

  5. Araujo, G. and Gaglianone, W. (2023). Machine Learning Methods for Inflation Forecasting in Brazil: New Contenders Versus Classical Models. Latin American Journal of Central Banking, 4(2), Article 100087. https://doi.org/10.1016/j.latcb.2023.100087.

  6. Athey, S. and Imbens, G. (2019). Machine Learning Methods that Economists Should Know About. Annual Review of Economics, 11, pp. 685–725. https://doi.org/10.1146/annurev-economics-080217-053433.

  7. Atkenson, A. and Ohanian, L. (2001). Are Phillips Curve Useful for Forecasting Inflation? Federal Reserve Bank of Minneapolis Quarterly Review, 25(1), pp. 2–11. https://doi.org/10.21034/qr.2511.
    Paper not yet in RePEc: Add citation now
  8. Barkan, O., Benchimol, J., Caspi, I., Cohen, E., Hammer, A. and Koenigstein, N. (2023). Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks. International Journal of Forecasting, 39(3), pp. 1145–1162. https://doi.org/10.1016/j.ijforecast.2022.04.009.

  9. Baybuza, I. (2018). Inflation Forecasting Using Machine Learning Methods. Russian Journal of Money and Finance, 77(4), pp. 42–59. https://doi.org/10.31477/rjmf.201804.42.

  10. Breiman, L. (1996). Bagging Predictors. Machine Learning, 24, pp. 123–140.
    Paper not yet in RePEc: Add citation now
  11. Brownlee, J. (2018). Deep Learning for Time Series Forecasting. Predict the Future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.
    Paper not yet in RePEc: Add citation now
  12. Chakraborty, C. and Joseph, A. (2017). Machine Learning at Central Banks. Bank of England Staff Working Paper, N 674.
    Paper not yet in RePEc: Add citation now
  13. Chollet, F. (2021). Deep Learning with Python (2nd ed.). Shelter Island: Manning Publications Co.
    Paper not yet in RePEc: Add citation now
  14. Chung, J., Gulcehre, C., Cho, K. and Bengio, Y. (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv Preprint, arXiv:1412.3555.
    Paper not yet in RePEc: Add citation now
  15. Cochrane, J. (2001). Long-Term Debt and Optimal Policy in the Fiscal Theory of the Price Level. Econometrica, 69(1), pp. 69–116. https://doi.org/10.1111/1468-0262.00179.

  16. Coibion, O., Gorodnichenko, Y. and Kamdar, R. (2018). The Formation of Expectations, Inflation, and the Phillips Curve. Journal of Economic Literature, 56(4), pp. 1447–1491. https://doi.org/10.1257/jel.20171300.

  17. Coulombe, P. G. (2022). A Neural Phillips Curve and a Deep Output Gap. arXiv Preprint, arXiv:2202.04146. https://doi.org/10.48550/arXiv.2202.04146.

  18. Coulombe, P., Leroux, M., Stevanovic, D. and Surprenant, S. (2022). How Is Machine Learning Useful for Macroeconomic Forecasting? Journal of Applied Econometrics, 37(5), pp. 920–964. https://doi.org/10.1002/jae.2910.

  19. Ditzen, J. and Ravazzolo, F. (2022). Dominant Drivers of National Inflation. arXiv Preprint, arXiv:2212.05841. https://doi.org/10.48550/arXiv.2212.05841.
    Paper not yet in RePEc: Add citation now
  20. Dorogush, A. V., Ershov, V. and Gulin, A. (2018). CatBoost: Gradient Boosting with Categorical Features Support. arXiv Preprint, arXiv:1810.11363.
    Paper not yet in RePEc: Add citation now
  21. Dzhunkeev: Forecasting Inflation in Russia, pp. 53–76 73 vol. 83 no. 1 Fisher, I. (1973). I Discovered the Phillips Curve: ‘A Statistical Relationship Between Unemployment and Price Changes’. Journal of Political Economy, 81(2), Part 1, pp. 496–502. https://doi.org/10.1086/260048.
    Paper not yet in RePEc: Add citation now
  22. Dzhunkeev: Forecasting Inflation in Russia, pp. 53–76 75 vol. 83 no. 1 Nakamura, E. (2005). Inflation Forecasting Using a Neural Network. Economics Letters, 86(3), pp. 373–378. https://doi.org/10.1016/j.econlet.2004.09.003.
    Paper not yet in RePEc: Add citation now
  23. Estrella, A. (2005). Why Does the Yield Curve Predict Output and Inflation? The Economic Journal, 115(505), pp. 722–744. https://doi.org/10.1111/j.1468-0297.2005.01017.x. Faust, J. and Wright, J. (2013). Forecasting Inflation. In: G. Elliott and A. Timmermann, eds. Handbook of Economic Forecasting, Vol. 2, Part A, pp. 2–56. Elsevier.
    Paper not yet in RePEc: Add citation now
  24. Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2003). Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area? Journal of Monetary Economics, 50(6), pp. 1243–1255. https://doi.org/10.1016/S0304-3932(03)00079-5.

  25. Freund, Y. and Schapire, R. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), pp. 119–139. https://doi.org/10.1006/jcss.1997.1504.
    Paper not yet in RePEc: Add citation now
  26. Friedman, J. (2001). Greedy Function Approximation: A Gradient Boosting Machine.
    Paper not yet in RePEc: Add citation now
  27. Friedman, M. (1968). The Role of Monetary Policy. American Economic Review, 58(1), pp. 1–17.
    Paper not yet in RePEc: Add citation now
  28. Gafarov, B. (2011) Phillips Curve and Development of the Labor Market in Russia. HSE Economic Journal, 15(2), pp. 155–176. [In Russian].
    Paper not yet in RePEc: Add citation now
  29. Garcia, M., Medeiros, M. and Vanconcelos, G. (2017). Real-Time Inflation Forecasting with High-Dimensional Models: The Case of Brazil. International Journal of Forecasting, 33(3), pp. 679–693. https://doi.org/10.1016/j.ijforecast.2017.02.002.

  30. Garratt, A., Lee, K., Pesaran, H. and Shin, Y. (2003). Forecast Uncertainties in Macroeconomic Modeling: An Application to the U.K. Economy. Journal of the American Statistical Association, 98(464), pp. 829–838.

  31. Geerolf, F. (2020). The Phillips Curve: A Relation Between Real Exchange Rate Growth and Unemployment. https://fgeerolf.com/phillips.pdf [accessed on 1 December 2023].
    Paper not yet in RePEc: Add citation now
  32. Harding, M., Lindé, J. and Trabandt, M. (2023). Understanding Post-COVID Inflation Dynamics. Journal of Monetary Economics, 140 (Supplement), pp. S101–S118. https://doi.org/10.1016/j.jmoneco.2023.05.012.

  33. Hochreiter, S. and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), pp. 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.
    Paper not yet in RePEc: Add citation now
  34. https://doi.org/10.1111/j.1467-937X.2007.00436.x. Kudrin, A. (2007). Inflation: Recent Trends in Russia and in the World. Voprosy Ekonomiki, 10, pp. 4–26. [In Russian]. https://doi.org/10.32609/0042-8736-2007-10-4-26.

  35. Inoue, A. and Kilian, L. (2008). How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation. Journal of the American Statistical Association, 103(482), pp. 511–522. https://doi.org/10.1198/016214507000000473.

  36. Joseph, A., Potjagailo, G., Kalamara, E., Chakraborty, C. and Kapetanios, G. (2021). Forecasting UK Inflation Bottom Up. Bank of England Staff Working Paper, N 915.
    Paper not yet in RePEc: Add citation now
  37. Kapetanios, G., Labhard, V. and Price, S. (2008). Forecasting Using Bayesian and Information-Theoretic Model Averaging: An Application to U.K. Inflation. Journal of Business and Economic Statistics, 26(1), pp. 33–41.

  38. Karpathy, A., Johnson, J. and Fei-Fei, L. (2015). Visualizing and Understanding Recurrent Networks. arXiv Preprint, arXiv:1506.02078. https://doi.org/10.48550/arXiv.1506.02078.
    Paper not yet in RePEc: Add citation now
  39. Ke, G., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: I. Guyon, U.
    Paper not yet in RePEc: Add citation now
  40. Kingma, D. and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv Preprint, arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980.
    Paper not yet in RePEc: Add citation now
  41. Kinlaw, W., Kritzman, M., Metcalfe, M. and Turkington, D. (2023). The Determinants of Inflation. Journal of Investment Management, 21(3), pp.29–41.
    Paper not yet in RePEc: Add citation now
  42. Kiselev, A. and Zhivaykina, A. (2020). The Role of Global Relative Price Changes in International Comovement of Inflation. Journal of Economic Asymmetries, 22, Article e00175. https://doi.org/10.1016/j.jeca.2020.e00175.

  43. Kohlscheen, E. (2022). What Does Machine Learning Says about the Drivers of Inflation? BIS Working Papers, N 980.
    Paper not yet in RePEc: Add citation now
  44. Koop, G. and Korobilis, D. (2012). Forecasting Inflation Using Dynamic Model Averaging. International Economic Review, 53(3), pp. 867–886. https://doi.org/10.1111/j.1468-2354.2012.00704.x. Koop, G. and Potter, S. (2007). Estimation and Forecasting in Models with Multiple Breaks. Review of Economic Studies, 74(3), pp. 763–789.

  45. Longo, L. and Soltanieh-ha, M. (2023). SHAPoly: A Novel Shapley-Polynomial Framework for Estimating Nonlinear Dynamics in Macroeconomic Data Using Deep Neural Networks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4350978.
    Paper not yet in RePEc: Add citation now
  46. Lundberg, S., and Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions.
    Paper not yet in RePEc: Add citation now
  47. Maheu, J. and Gordon, S. (2008). Learning, Forecasting and Structural Breaks. Journal of Applied Econometrics, 23(5), pp. 553–583. https://doi.org/10.1002/jae.1018.

  48. Mamedli, M. and Shibitov, D. (2021). Forecasting Russian CPI with Data Vintages and Machine Learning Techniques. Bank of Russia Working Paper Series.
    Paper not yet in RePEc: Add citation now
  49. Masini, R., Medeiros, M. and Mendes, E. (2023). Machine Learning Advances for Time Series Forecasting. Journal of Economic Surveys, 37(1), pp. 76–111. https://doi.org/10.1111/joes.12429.

  50. Medeiros, M., Schutte, E. and Soussi, T. (2023). Global Inflation: Implications for Forecasting and Monetary Policy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4145665.
    Paper not yet in RePEc: Add citation now
  51. Medeiros, M., Vasconcelos, G., Veiga, A. and Zilberman, E. (2019). Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods.

  52. Nagy, E. and Tengey, V. (2018). External and Domestic Drivers of Inflation: The Case Study of Hungary. Russian Journal of Money and Finance, 77(3), pp. 49–64. https://doi.org/10.31477/rjmf.201803.49.

  53. Orlov, D. and Postnikov, E. (2020). Krivaya Fillipsa: inflyatsiya i NAIRU v rossiyskikh regionakh [Phillips Curve: Inflation and NAIRU in Russian Regions]. Bank of Russia Working Paper Series. [In Russian].
    Paper not yet in RePEc: Add citation now
  54. Paranhos, L. (2021). Predicting Inflation with Neural Networks. arXiv Preprint, arXiv:2104.03757. https://doi.org/10.48550/arXiv.2104.03757.

  55. Pavlov, E. (2020). Forecasting Inflation in Russia Using Neural Networks. Russian Journal of Money and Finance, 79(1), pp. 57–73. https://doi.org/10.31477/rjmf.202001.57.

  56. Perevyshin, Y. (2022). Short-Term Inflation Forecasting in the Russian Economy.
    Paper not yet in RePEc: Add citation now
  57. Phelps, E. (1968). Money-Wage Dynamics and Labor-Market Equilibrium. Journal of Political Economy, 76(4), pp. 678–711. https://doi.org/10.1086/259438.

  58. Phillips, A. (1958). The Relation Between Unemployment and the Rate of Change of Money Rates in the United Kingdom, 1861–1957. Economica, 25(100), pp. 283–299. https://doi.org/10.2307/2550759.
    Paper not yet in RePEc: Add citation now
  59. Ponomarev, Y., Trunin, P. and Ulyukayev, A. (2014). Exchange Rate Pass-Through in Russia. Voprosy Ekonomiki, 3, pp. 21–35. [In Russian]. https://doi.org/10.32609/0042-8736-2014-3-21-35.

  60. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. and Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18), pp. 6639–6649. Curran Associates Inc.
    Paper not yet in RePEc: Add citation now
  61. Russian Journal of Money and Finance 72 march 2024 Banbura, M. and Bobeica, E. (2023). Does the Phillips Curve help to Forecast Euro Area Inflation? International Journal of Forecasting, 39(1), pp. 364–390. https://doi.org/10.1016/j.ijforecast.2021.12.001.
    Paper not yet in RePEc: Add citation now
  62. Russian Journal of Money and Finance 74 march 2024 Khabibullin, R. (2019). What Measures of Real Economic Activity Slack are Helpful for Forecasting Russian Inflation? Bank of Russia Working Paper Series, N 50. [In Russian].
    Paper not yet in RePEc: Add citation now
  63. Russian Journal of Money and Finance 76 march 2024 Stock, J. and Watson, M. (2007). Why Has U.S. Inflation Become Harder to Forecast? Journal of Money, Credit and Banking, 39(s1), pp. 3–33. https://doi.org/10.1111/j.1538-4616.2007.00014.x. Stock, J. and Watson, M. (2008). Phillips Curve Inflation Forecasts. NBER Working Paper, N 14322.
    Paper not yet in RePEc: Add citation now
  64. Saul, S. (2021). Do Global Output Gaps Help Forecast Inflation in Russia? Bank of Russia Working Paper Series, N 85.
    Paper not yet in RePEc: Add citation now
  65. Semiturkin, O. and Shevelev, A. (2023). Correct Comparison of Predictive Features of Machine Learning Methods: The Case of Forecasting Inflation Rates in Siberia. Russian Journal of Money and Finance, 82(1), pp. 87–103.

  66. Shulyak, E. (2022). Macroeconomic Forecasting Using Data from Social Media. Russian Journal of Money and Finance, 81(4), pp. 86–112.

  67. Sims, C. (2010). Stepping on a Rake: The Role of Fiscal Policy in the Inflation of the 1970s. European Economic Review, 55(1), pp. 48–56. https://doi.org/10.1016/j.euroecorev.2010.11.010.
    Paper not yet in RePEc: Add citation now
  68. Sinyakov, A., Chernyadyev, D. and Sapova, A. (2019). Estimating the Exchange Rate Pass-Through Effect on Producer Prices of Final Products Based on Micro-Data of Russian Companies. Journal of the New Economic Association, 1(41), pp. 128–157.

  69. Stella, A. and Stock, J. (2012). A State-Dependent Model for Inflation Forecasting. Board of Governors of the Federal Reserve System International Finance Discussion Papers, N 1062.
    Paper not yet in RePEc: Add citation now
  70. Stock, J. and Watson, M. (1999). Forecasting Inflation. Journal of Monetary Economics, 44(2), pp. 293–335. https://doi.org/10.1016/S0304-3932(99)00027-6.

  71. Stock, J. and Watson, M. (2003). Forecasting Output and Inflation: The Role of Asset Prices. Journal of Economic Literature, 41(3), pp. 788–829. https://doi.org/10.1257/002205103322436197.

  72. Stock, J. and Watson, M. (2010). Modeling Inflation After the Crisis. NBER Working Paper, N 16488.

  73. Styrin, K. (2019). Forecasting Inflation in Russia by Dynamic Model Averaging. Russian Journal of Money and Finance, 78(1), pp. 3–18. https://doi.org/10.31477/rjmf.201901.03.

  74. Styrin, K. and Zamulin, O. (2012). A Real Exchange Rate Based Phillips Curve. CEFIR / NES Working Paper Series, N 179.

  75. Svensson, L. (2010). Inflation Targeting. In: B. Friedman and M. Woodford, eds.
    Paper not yet in RePEc: Add citation now
  76. Szafranek, K. (2019). Bagged Neural Networks for Forecasting Polish (Low) Inflation.

  77. Tretyakov, D. and Fokin, N. (2021). Does the High-Frequency Data is Helpful for Forecasting Russian inflation? St Petersburg University Journal of Economic Studies, 37(2), pp. 318–343.
    Paper not yet in RePEc: Add citation now
  78. Van den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K. (2016). WaveNet: A Generative Model for Raw Audio. arXiv Preprint, arXiv:1609.03499.
    Paper not yet in RePEc: Add citation now
  79. von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan and R. Garnett, eds. Advances in Neural Information Processing Systems, Vol. 30 (Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, USA), pp. 3149–3157.
    Paper not yet in RePEc: Add citation now
  80. Zeng, J. (2017). Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors? Journal of Forecasting, 36(1), pp. 74–90. https://doi.org/10.1002/for.2415.

Cocites

Documents in RePEc which have cited the same bibliography

  1. Toward a smart forecasting model in supply chain management: A case study of coffee in Vietnam. (2025). Le, Thi Muoi ; Abed, Mourad ; Kantasaard, Anirut ; Hanh, Thi Thuy ; Bekrar, Abdelghani.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:44:y:2025:i:1:p:173-199.

    Full description at Econpapers || Download paper

  2. Inflation, Attention and Expectations. (2025). Stevanovic, Dalibor ; Marcellino, Massimiliano ; Briand, Etienne.
    In: CIRANO Working Papers.
    RePEc:cir:cirwor:2025s-01.

    Full description at Econpapers || Download paper

  3. Investor attention and consumer price index inflation rate: Evidence from the United States. (2024). Zhang, Yinpeng ; Zhou, Qingjie ; Zhu, Panpan.
    In: Palgrave Communications.
    RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03036-y.

    Full description at Econpapers || Download paper

  4. A systematic literature review of the implications of media on inflation expectations. (2024). Law, Chee-Hong ; Goh, Kim Huat.
    In: International Economics and Economic Policy.
    RePEc:kap:iecepo:v:21:y:2024:i:2:d:10.1007_s10368-024-00591-2.

    Full description at Econpapers || Download paper

  5. Forecasting UK inflation bottom up. (2024). Potjagailo, Galina ; Kapetanios, George ; Chakraborty, Chiranjit ; Joseph, Andreas.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:40:y:2024:i:4:p:1521-1538.

    Full description at Econpapers || Download paper

  6. The clarity of monetary policy communication and financial market volatility in developing economies. (2024). Sohn, Wook ; Jombo, Wytone ; Vyshnevskyi, Iegor.
    In: Emerging Markets Review.
    RePEc:eee:ememar:v:59:y:2024:i:c:s1566014124000165.

    Full description at Econpapers || Download paper

  7. Disentangling demand and supply inflation shocks from electronic payments data. (2024). Hernndez-Romn, Luis G ; Eterovic, Nicols ; Carlomagno, Guillermo.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:141:y:2024:i:c:s0264999324002281.

    Full description at Econpapers || Download paper

  8. Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks. (2024). Dzhunkeev, Urmat.
    In: Russian Journal of Money and Finance.
    RePEc:bkr:journl:v:83:y:2024:i:1:p:53-76.

    Full description at Econpapers || Download paper

  9. .

    Full description at Econpapers || Download paper

  10. .

    Full description at Econpapers || Download paper

  11. Sentiment Analysis on Inflation after COVID-19. (2023). Tang, Zihan ; Li, Xinyu.
    In: Applied Economics and Finance.
    RePEc:rfa:aefjnl:v:10:y:2023:i:1:p:1023.

    Full description at Econpapers || Download paper

  12. Identifying Financial Crises Using Machine Learning on Textual Data. (2023). Sicilian, Martin J ; Lee, Seung Jung ; Kitschelt, Isabel ; Dehaven, Matthew ; Chen, Mary.
    In: JRFM.
    RePEc:gam:jjrfmx:v:16:y:2023:i:3:p:161-:d:1084784.

    Full description at Econpapers || Download paper

  13. Gender Differences in Inflation Expectations: Recent Evidence from India. (2023). Salve, Sangita ; Gite, Chaitanya ; Sharma, Nitin Mohanlal ; Joshi, Preeti Tushar ; Chalwadi, Swapnil Virendra.
    In: Administrative Sciences.
    RePEc:gam:jadmsc:v:13:y:2023:i:2:p:60-:d:1068215.

    Full description at Econpapers || Download paper

  14. Identifying Financial Crises Using Machine Learning on Textual Data. (2023). Sicilian, Martin ; Lee, Seung Jung ; Kitschelt, Isabel ; Dehaven, Matthew ; Chen, Mary.
    In: International Finance Discussion Papers.
    RePEc:fip:fedgif:1374.

    Full description at Econpapers || Download paper

  15. More than Words: Twitter Chatter and Financial Market Sentiment. (2023). Vazquez-Grande, Francisco ; Adams, Travis ; Silva, Diego ; Ajello, Andrea.
    In: Finance and Economics Discussion Series.
    RePEc:fip:fedgfe:2023-34.

    Full description at Econpapers || Download paper

  16. The power of text-based indicators in forecasting Italian economic activity. (2023). Monteforte, Libero ; Marcucci, Juri ; Luciani, Andrea ; Guaitoli, Gabriele ; Emiliozzi, Simone ; Aprigliano, Valentina.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:2:p:791-808.

    Full description at Econpapers || Download paper

  17. Utiliser la presse pour construire un nouvel indicateur de perception d’inflation en France. (2023). Pierre-Antoine, Robert ; Annabelle, De Gaye ; Alexandre, Dhenin ; Julien, Denes ; Jean-Charles, Bricongne ; Olivier, De Bandt.
    In: Working papers.
    RePEc:bfr:banfra:921.

    Full description at Econpapers || Download paper

  18. Turning Words into Numbers: Measuring News Media Coverage of Shortages. (2023). Houle, Stephanie ; Chen, Lin.
    In: Discussion Papers.
    RePEc:bca:bocadp:23-8.

    Full description at Econpapers || Download paper

  19. Digitalization: Implications for Monetary Policy. (2023). Yanni, Pierre-Yves ; Hajzler, Christopher ; Dahlhaus, Tatjana ; Chu, Vivian.
    In: Discussion Papers.
    RePEc:bca:bocadp:23-18.

    Full description at Econpapers || Download paper

  20. What Can Earnings Calls Tell Us About the Output Gap and Inflation in Canada?. (2023). Taskin, Temel ; Gosselin, Marc-Andre.
    In: Discussion Papers.
    RePEc:bca:bocadp:23-13.

    Full description at Econpapers || Download paper

  21. More than Words: Twitter Chatter and Financial Market Sentiment. (2023). Vazquez-Grande, Francisco ; Silva, Diego ; Ajello, Andrea ; Adams, Travis.
    In: Papers.
    RePEc:arx:papers:2305.16164.

    Full description at Econpapers || Download paper

  22. Machine Learning for Economics Research: When What and How?. (2023). Desai, Ajit.
    In: Papers.
    RePEc:arx:papers:2304.00086.

    Full description at Econpapers || Download paper

  23. Assessment of inflation expectations based on internet data. (2022). Petrova, Diana.
    In: Applied Econometrics.
    RePEc:ris:apltrx:0444.

    Full description at Econpapers || Download paper

  24. The demand and supply of information about inflation. (2022). Stevanovic, Dalibor ; Marcellino, Massimiliano.
    In: CIRANO Working Papers.
    RePEc:cir:cirwor:2022s-27.

    Full description at Econpapers || Download paper

  25. Press news and social media in credit risk assessment: the experience of Banca d’Italia’s In-house Credit Assessment System. (2022). Viggiano, Gianluca ; Gariano, Giulio.
    In: Temi di discussione (Economic working papers).
    RePEc:bdi:wptemi:misp_024_22.

    Full description at Econpapers || Download paper

  26. Textual analysis of a Twitter corpus during the COVID-19 pandemics. (2022). Marcucci, Juri ; Langiulli, Marco ; Crispino, Marta ; Astuti, Valerio.
    In: Questioni di Economia e Finanza (Occasional Papers).
    RePEc:bdi:opques:qef_692_22.

    Full description at Econpapers || Download paper

  27. Sentiment Analysis on Inflation after Covid-19. (2022). Tang, Zihan ; Li, Xinyu.
    In: Papers.
    RePEc:arx:papers:2209.14737.

    Full description at Econpapers || Download paper

  28. .

    Full description at Econpapers || Download paper

  29. A sentiment-based risk indicator for the Mexican financial sector. (2021). Rho, Caterina ; Guizar, Brenda Palma ; Fernandez, Raul.
    In: Latin American Journal of Central Banking (previously Monetaria).
    RePEc:eee:lajcba:v:2:y:2021:i:3:s2666143821000168.

    Full description at Econpapers || Download paper

  30. A Sentiment-based Risk Indicator for the Mexican Financial Sector. (2021). Palma, Brenda ; Fernandez, Raul ; Rho, Caterina.
    In: Working Papers.
    RePEc:bdm:wpaper:2021-04.

    Full description at Econpapers || Download paper

  31. Exploiting payments to track Italian economic activity: the experience at Banca d’Italia. (2021). Zizza, Roberta ; Gambini, Alessandro ; aprigliano, valentina ; Renzi, Nazzareno ; Emiliozzi, Simone ; Cavallero, Alessandro ; Cassetta, Alessia ; Ardizzi, Guerino.
    In: Questioni di Economia e Finanza (Occasional Papers).
    RePEc:bdi:opques:qef_609_21.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-03-04 13:47:41 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Sponsored by INOMICS. Last updated October, 6 2023. Contact: CitEc Team.