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Parameter cascading for panel models with unknown number of unobserved factors: An application to the credit spread puzzle

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  • Bada, Oualid
  • Kneip, Alois

Abstract

The iterative least squares method for estimating panel models with unobservable factor structure is extended to cover the case where the number of factors is unknown a priori. The proposed estimation algorithm optimizes a penalized least squares objective function to estimate the factor dimension jointly with the remaining model parameters in an iterative hierarchical order. Monte Carlo experiments show that, in many configurations of the data, such a refinement provides more efficient estimates in terms of MSE than those that could be achieved if the feasible iterative least squares estimator is calculated with an externally selected factor dimension. The method is applied to the problem of the credit spread puzzle to estimate the space of the missing risk factors jointly with the effects of the observed credit and illiquidity risks.

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  • Bada, Oualid & Kneip, Alois, 2014. "Parameter cascading for panel models with unknown number of unobserved factors: An application to the credit spread puzzle," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 95-115.
    • Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:95-115
    DOI: 10.1016/j.csda.2013.11.007
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    1. Cao, J. & Ramsay, J. O., 2010. "Linear Mixed-Effects Modeling by Parameter Cascading," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 365-374.
    2. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    3. Greenaway-McGrevy, Ryan & Han, Chirok & Sul, Donggyu, 2012. "Estimating the number of common factors in serially dependent approximate factor models," Economics Letters, Elsevier, vol. 116(3), pages 531-534.
    4. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    5. Houweling, Patrick & Mentink, Albert & Vorst, Ton, 2005. "Comparing possible proxies of corporate bond liquidity," Journal of Banking & Finance, Elsevier, vol. 29(6), pages 1331-1358, June.
    6. Bai, Jushan & Kao, Chihwa & Ng, Serena, 2009. "Panel cointegration with global stochastic trends," Journal of Econometrics, Elsevier, vol. 149(1), pages 82-99, April.
    7. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    8. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
    9. Carolina Castagnetti & Eduardo Rossi, 2013. "Euro Corporate Bond Risk Factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 372-391, April.
    10. Francis A. Longstaff & Sanjay Mithal & Eric Neis, 2005. "Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market," Journal of Finance, American Finance Association, vol. 60(5), pages 2213-2253, October.
    11. Gebhardt, William R. & Hvidkjaer, Soeren & Swaminathan, Bhaskaran, 2005. "Stock and bond market interaction: Does momentum spill over?," Journal of Financial Economics, Elsevier, vol. 75(3), pages 651-690, March.
    12. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
    13. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    14. Jeffery D Amato & Eli M Remolona, 2003. "The credit spread puzzle," BIS Quarterly Review, Bank for International Settlements, December.
    15. Edwin J. Elton & Martin J. Gruber & Deepak Agrawal & Christopher Mann, 2001. "Explaining the Rate Spread on Corporate Bonds," Journal of Finance, American Finance Association, vol. 56(1), pages 247-277, February.
    16. Kneip, Alois & Sickles, Robin C. & Song, Wonho, 2012. "A New Panel Data Treatment For Heterogeneity In Time Trends," Econometric Theory, Cambridge University Press, vol. 28(3), pages 590-628, June.
    17. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    18. Alexei Onatski, 2010. "Determining the Number of Factors from Empirical Distribution of Eigenvalues," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1004-1016, November.
    19. De Wachter, Stefan & Tzavalis, Elias, 2012. "Detection of structural breaks in linear dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3020-3034.
    20. Yusho Kagraoka, 2010. "A time-varying common risk factor affecting corporate yield spreads," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 527-539.
    21. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    22. Alexei Onatski, 2009. "Testing Hypotheses About the Number of Factors in Large Factor Models," Econometrica, Econometric Society, vol. 77(5), pages 1447-1479, September.
    23. Jerry Coakley & Ana-Maria Fuertes & Ron Smith, 2002. "A Principal Components Approach to Cross-Section Dependence in Panels," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 B5-3, International Conferences on Panel Data.
    24. Jing-Zhi Huang & Ming Huang, 2012. "How Much of the Corporate-Treasury Yield Spread Is Due to Credit Risk?," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 2(2), pages 153-202.
    25. Jushan Bai, 2009. "Panel Data Models With Interactive Fixed Effects," Econometrica, Econometric Society, vol. 77(4), pages 1229-1279, July.
    26. Onatski, Alexei, 2012. "Asymptotics of the principal components estimator of large factor models with weakly influential factors," Journal of Econometrics, Elsevier, vol. 168(2), pages 244-258.
    27. Dray, Stephane, 2008. "On the number of principal components: A test of dimensionality based on measurements of similarity between matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2228-2237, January.
    28. Josse, Julie & Husson, François, 2012. "Selecting the number of components in principal component analysis using cross-validation approximations," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1869-1879.
    29. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    30. Lesmond, David A & Ogden, Joseph P & Trzcinka, Charles A, 1999. "A New Estimate of Transaction Costs," The Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1113-1141.
    31. Chan, Louis K C & Jegadeesh, Narasimhan & Lakonishok, Josef, 1996. "Momentum Strategies," Journal of Finance, American Finance Association, vol. 51(5), pages 1681-1713, December.
    32. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    33. Chen, Yin-Ping & Huang, Hsin-Cheng & Tu, I-Ping, 2010. "A new approach for selecting the number of factors," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2990-2998, December.
    34. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    35. Pierre Collin-Dufresn & Robert S. Goldstein & J. Spencer Martin, 2001. "The Determinants of Credit Spread Changes," Journal of Finance, American Finance Association, vol. 56(6), pages 2177-2207, December.
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    Cited by:

    1. Changqing Luo & Mengzhen Li & Zisheng Ouyang, 2016. "An empirical study on the correlation structure of credit spreads based on the dynamic and pair copula functions," China Finance Review International, Emerald Group Publishing Limited, vol. 6(3), pages 284-303, August.
    2. Shiyun Cao & Qiankun Zhou, 2022. "Common Correlated Effects Estimation for Dynamic Heterogeneous Panels with Non-Stationary Multi-Factor Error Structures," Econometrics, MDPI, vol. 10(3), pages 1-27, August.
    3. Bada, O. & Kneip, A. & Liebl, D. & Mensinger, T. & Gualtieri, J. & Sickles, R.C., 2022. "A wavelet method for panel models with jump discontinuities in the parameters," Journal of Econometrics, Elsevier, vol. 226(2), pages 399-422.
    4. Povilas Lastauskas & Julius Stakenas, 2019. "Does It Matter When Labor Market Reforms Are Implemented? The Role of the Monetary Policy Environment," Bank of Lithuania Working Paper Series 66, Bank of Lithuania.
    5. Anthony N. Rezitis, 2015. "Empirical Analysis of Agricultural Commodity Prices, Crude Oil Prices and US Dollar Exchange Rates using Panel Data Econometric Methods," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 851-868.
    6. Bada, Oualid & Liebl, Dominik, 2014. "phtt: Panel Data Analysis with Heterogeneous Time Trends in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i06).

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