create a website

The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach. (2015). Plakandaras, Vasilios ; Papadimitriou, Theophilos ; GUPTA, RANGAN ; Gogas, Periklis.
In: Working Papers.
RePEc:pre:wpaper:201548.

Full description at Econpapers || Download paper

Cited: 1

Citations received by this document

Cites: 40

References cited by this document

Cocites: 21

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

  1. Gold, platinum and the predictability of bond risk premia. (2021). Wohar, Mark ; GUPTA, RANGAN ; Demirer, Riza ; Bouri, Elie.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319309079.

    Full description at Econpapers || Download paper

References

References cited by this document

    References contributed by pko254-4998

  1. Öğüt, H, M. Mete Doğanay, N.B. Ceylan, and R. Aktaş (2012), Prediction of bank financial strength ratings: The case of Turkey. Economic Modelling, vol. 29(3), pp. 632-640.

  2. Álvarez-Díaz, M., and Gupta R. (2015). Forecasting the US CPI: Does Nonlinearity Matter? Department of Economics, University of Pretoria, Working Paper No. 2015-12. Ang A., Bekaert G. and M. Wei (2007), Do Macro Variables, Asset Markets, or Surveys Forecast Inflation Better? , Journal of Monetary Economics, vol. 54, pp. 1163-1212.

  3. Ascari G. and Marrocu E. (2003), Forecasting Inflation: A Comparison of Linear Phillips Curve Models and Nonlinear Time Series Models, Working Paper CRENoS 00307, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

  4. Bai J. and Ng. S (2008) Forecasting economic time series with targeted predictors, Journal of Econometrics, vol. 146, pp.304-317.

  5. Bai J. and Perron P. (2003) Critical values for multiple structural change tests, Econometrics Journal, vol. 6, pp. 72-78.

  6. Bekiros, S and Paccagnini, A. (2013). On the Predictability of Time-Varying VAR AND DSGE Models, Empirical Economics, 45 (1), pp. 635-664.

  7. Bekiros, S and Paccagnini, A. (Forthcoming). Microprudential Policy and Forecasting Using Hybrid DSGE Models with Financial Frictions and State-Space Markov-Switching TV-VARs, Macroeconomic Dynamics. Berardi A. (2009), Term Structure, Inflation, and Real Activity, Journal of Financial and Quantitative Analysis, vol. 44(4), pp. 987-1011.

  8. Cortes C. and Vapnik V. (1995) Support-Vector Networks, Machine Learning, vol 20, pp. 273-297.
    Paper not yet in RePEc: Add citation now
  9. Dickey D. and Fuller W. (1981), Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root, Econometrica, vol. 49, pp. 1057-1072.

  10. Enders W. and Lee J. (2012) A Unit Root Test Using a Fourier Series to Approximate Smooth Breaks, Oxford Bulletin of Economics and Statistics, vol. 74 (4), pp. 574-599.

  11. Estrella, A. and Mishkin, F. S. (1997), The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank, European Economic Review, vol. 41(7), pp. 1375-1401.

  12. Giacomini R. and Rossi B. (2010), Forecast Comparisons in Unstable Environments, Journal of Applied Econometrics, vol. 25 (4), pp. 595-620.

  13. Goyal, A., and Welch I. (2008). A Comprehensive Look at the Empirical Performance of Equity Premium Prediction, Review of Financial Studies, vol. 21(4), pp. 1455-1508.

  14. Härdle, W., Lee Y-J, Schäfer D. and Yeh Y-R (2009) Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies, Journal of Forecasting, vol.28(6), pp. 512-534.

  15. Inoue A. and Killian L. (2008), How Useful is Bagging in Forecasting Economic Time Series? A Case Study of U.S. CPI Inflation, Journal of the American Statistical Association, vol. 103 (482), pp. 511-522.

  16. Jarque C. M., and A. K. Bera. (1987), A Test for Normality of Observations and Regression Residuals, International Statistical Review, vol. 5 (2), pp. 163–172.
    Paper not yet in RePEc: Add citation now
  17. Johansen, S.(1991).Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models, Econometrica,vol.59, pp. 1551–1580.

  18. Jorion P. and F. S. Mishkin (1991), A Multi-Country Comparison of Term Structure Forecasts at Long Horizons, Journal of Financial Economics, vol. 29, pp. 59 – 80.

  19. Khandani, A E., A J. Kim, and A. W. Lo (2010), Consumer credit-risk models via machine-learning algorithms, Journal of Banking & Finance, vol. 34(11), pp. 2767-2787.

  20. Koop, G. and Korobilis, D. (2012). Forecasting Inflation Using Dynamic Model Averaging, International Economic Review, vol. 53(3), pp. 867-886.

  21. Koop, G. and Korobilis, D. (2013). Large Time-Varying Parameter VARs, Journal of Econometrics, vol. 177 (2), pp. 185-198.

  22. Korobilis, D. (2015). Quantile forecasts of inflation under model uncertainty, MPRA Paper No. 64341.

  23. Kwiatkowski D., Phillips, P. C.B., Schmidt, P. and Shin, Y. (1992), Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root, Journal of Econometrics, vol. 54, pp. 159-178.
    Paper not yet in RePEc: Add citation now
  24. Liu, J., Wu, S. and Zidek, J. V. (1997). On Segmented Multivariate Regression, Statistica Sinica, Vol. 7, pp. 497–525.
    Paper not yet in RePEc: Add citation now
  25. Manzan, S. and Zerom, D. (2015). Asymmetric quantile persistence and predictability: the case of U.S. inflation, Oxford Bulletin of Economics and Statistics, vol. 7 (2), pp. 297-318.

  26. Marcellino, M. (2008), A Benchmark Model for Growth and Inflation, Journal of Forecasting, vol. 27(4), pp. 305-340.
    Paper not yet in RePEc: Add citation now
  27. Mishkin F. S. (1990a), What Does the Term Structure Tell Us About Future Inflation?, Journal of Monetary Economics, vol. 25, pp. 77 – 95.

  28. Mishkin F. S. (1990b), The Information in the Longer-Maturity Term Structure About Future Inflation, Quarterly Journal of Economics, vol. 55, pp. 815 – 828.

  29. Perron, P. (1997), Further Evidence on Breaking Trend Functions in Macroeconomic Variables, Journal of Econometrics, vol. 80 (2), pp.355-385.

  30. Phillips P.C.B and P. Perron (1988),"Testing for a Unit Root in Time Series Regression, Biometrika, vol. 75, pp. 335–346.

  31. Plakandaras V., Gupta R., Gogas P. and Papadimitriou T. (2015), Forecasting the U.S, Real House Price Index, Economic Modelling, vol. 45, pp. 259-267.

  32. Rossi B. and Sehkposyan T. (2010) Have Economic Models’ forecasting performance for US output and inflation changed over time and when?, International Journal of Forecasting, vol. 26(4), pp. 808-835.

  33. Rubio G., Pomares H., Rojas I. and Herrera L. J.(2011) A heuristic method for parameterselection in LS-SVM: Application to time series prediction, International Journal of Forecasting, vol. 27(3), pp. 725-739.

  34. Schwarz G. E. (1978), Estimating the dimension of a model, Annals of Statistics, vol. 6 (2), pp. 461–464.
    Paper not yet in RePEc: Add citation now
  35. Stock J. H. and M. W. Watson (2003), Forecasting Output and Inflation: The Role of Asset Prices, Journal of Economic Literature, vol. 41(3), pp. 788-829.

  36. Stock J. H. and M. W. Watson (2008), Phillips Curve Inflation Forecasts, NBER Working Paper, No. 14322.

  37. Stock, J. H. and Watson, M. W. (2010). Modeling inflation after the crisis, Proceedings - Economic Policy Symposium - Jackson Hole, pp. 173–220.

  38. Tibshirani R. (1996), Regression shrinkage and selection via the lasso, Journal of the Royal Statistics Society B, vol. 58 (1), pp. 267-288.
    Paper not yet in RePEc: Add citation now
  39. Vapnik, V., Boser, B. and Guyon, I. (1992) A training algorithm for optimal margin classifiers, Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, ACM, pp.144–152.
    Paper not yet in RePEc: Add citation now
  40. Zivot E. and Andrews K. (1992), Further Evidence On The Great Crash, The Oil Price Shock, and The Unit Root Hypothesis, Journal of Business and Economic Statistics, vol. 10 (10), pp. 251–70.

Cocites

Documents in RePEc which have cited the same bibliography

  1. Detecting and Forecasting Financial Bubbles in The Indian Stock Market Using Machine Learning Models. (2024). Kayal, Parthajit ; Manian, Mahalakshmi.
    In: Working Papers.
    RePEc:mad:wpaper:2024-270.

    Full description at Econpapers || Download paper

  2. Bankruptcy prediction using fuzzy convolutional neural networks. (2023). Serret, Vanessa ; ben Jabeur, Sami.
    In: Research in International Business and Finance.
    RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531922002306.

    Full description at Econpapers || Download paper

  3. Tracking customer risk aversion. (2023). Kim, Woo Chang ; Yun, Wonje ; Kong, Hyeongwoo.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:54:y:2023:i:c:s1544612323000727.

    Full description at Econpapers || Download paper

  4. Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models. (2022). Mirza, Nawazish ; Umar, Muhammad ; Li, Changming ; Yu, Baojun.
    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:174:y:2022:i:c:s0040162521006892.

    Full description at Econpapers || Download paper

  5. Support Vector Machine Algorithms: An Application to Ship Price Forecasting. (2021). Syriopoulos, Theodore ; Tsatsaronis, Michael ; Karamanos, Ioannis.
    In: Computational Economics.
    RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10032-2.

    Full description at Econpapers || Download paper

  6. An Analysis of Bank Financial Strength Ratings and Credit Rating Data. (2021). Ruddy, John A.
    In: Risks.
    RePEc:gam:jrisks:v:9:y:2021:i:9:p:155-:d:622521.

    Full description at Econpapers || Download paper

  7. Machine learning and credit ratings prediction in the age of fourth industrial revolution. (2020). Mirza, Nawazish ; Li, Jing-Ping ; Xiong, Deping ; Rahat, Birjees.
    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:161:y:2020:i:c:s0040162520311355.

    Full description at Econpapers || Download paper

  8. Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach. (2020). Li, Weiping ; Chi, Guotai ; Shi, Baofeng.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:85:y:2020:i:c:p:420-428.

    Full description at Econpapers || Download paper

  9. Banks and shareholders credit ratings – evidence from the European market. (2019). Chodnicka-Jaworska, Patrycja.
    In: Faculty of Management Working Paper Series.
    RePEc:sgm:fmuwwp:32019.

    Full description at Econpapers || Download paper

  10. Forecasting Bitcoin Returns: Is there a Role for the U.S. – China Trade War?. (2019). Plakandaras, Vasilios ; GUPTA, RANGAN ; Bouri, Elie.
    In: Working Papers.
    RePEc:pre:wpaper:201980.

    Full description at Econpapers || Download paper

  11. Forecasting transportation demand for the U.S. market. (2019). Plakandaras, Vasilios ; Papadimitriou, Theophilos ; Gogas, Periklis.
    In: Transportation Research Part A: Policy and Practice.
    RePEc:eee:transa:v:126:y:2019:i:c:p:195-214.

    Full description at Econpapers || Download paper

  12. Strategic direction re-evaluation of bank ratings in Brazil. (2019). Domeneghetti, Valdir ; Lima, Fabiano Guasti.
    In: Economics Bulletin.
    RePEc:ebl:ecbull:eb-18-01030.

    Full description at Econpapers || Download paper

  13. Banks credit ratings – is the size of the credit rating agency important?. (2018). Chodnicka-Jaworska, Patrycja.
    In: Faculty of Management Working Paper Series.
    RePEc:sgm:fmuwwp:32018.

    Full description at Econpapers || Download paper

  14. The adjustment of bank ratings in the financial crisis: International evidence. (2018). Fernández-de-Guevara, Juan ; de Guevara, Juan Fernandez ; Pastor, Jose Manuel ; Salvador, Carlos.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:44:y:2018:i:c:p:289-313.

    Full description at Econpapers || Download paper

  15. Ratings based Inference and Credit Risk: Detecting likely-to-fail Banks with the PC-Mahalanobis Method. (2017). Pompella, Maurizio ; Dicanio, Antonio.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:67:y:2017:i:c:p:34-44.

    Full description at Econpapers || Download paper

  16. Modelling banks’ credit ratings of international agencies. (2016). Karminsky, Alexandr ; Khromova, Ella.
    In: Eurasian Economic Review.
    RePEc:spr:eurase:v:6:y:2016:i:3:d:10.1007_s40822-016-0058-5.

    Full description at Econpapers || Download paper

  17. The Term Premium as a Leading Macroeconomic Indicator. (2016). Plakandaras, Vasilios ; Papadimitriou, Theophilos ; GUPTA, RANGAN ; Gogas, Periklis.
    In: Working Papers.
    RePEc:pre:wpaper:201613.

    Full description at Econpapers || Download paper

  18. The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach. (2015). Plakandaras, Vasilios ; Papadimitriou, Theophilos ; GUPTA, RANGAN ; Gogas, Periklis.
    In: Working Papers.
    RePEc:pre:wpaper:201548.

    Full description at Econpapers || Download paper

  19. Forecasting the U.S. real house price index. (2015). Plakandaras, Vasilios ; Papadimitriou, Theophilos ; GUPTA, RANGAN ; Gogas, Periklis.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:45:y:2015:i:c:p:259-267.

    Full description at Econpapers || Download paper

  20. An ensemble-based model for two-class imbalanced financial problem. (2014). Chen, Tai-Feng ; Hsu, Ming-Fu ; Liao, Jui-Jung ; Shih, Ching-Hui .
    In: Economic Modelling.
    RePEc:eee:ecmode:v:37:y:2014:i:c:p:175-183.

    Full description at Econpapers || Download paper

  21. Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment. (2013). Xu, Xuan-Guo ; Hong, Lu-Yao ; Sun, Jie ; He, Jia-Xun ; Li, Hui.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:33:y:2013:i:c:p:747-761.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-09-23 06:03:59 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.