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1. /N 50 100 250 400 50 100 250 400 50 100 250 400 50 -0.0053 -0.0048 -0.0045 -0.0048 -0.0031 -0.0032 -0.0033 -0.0034 0.9996 0.9999 0.9999 0.9999 100 -0.0023 -0.0017 -0.0017 -0.0017 -0.0013 -0.0010 -0.0011 -0.0012 0.9997 0.9999 0.9999 1.0000 250 -0.0009 -0.0006 -0.0005 -0.0004 -0.0003 -0.0003 -0.0003 -0.0003 0.9997 0.9998 0.9999 1.0000 400 -0.0006 -0.0003 -0.0002 -0.0003 -0.0002 -0.0001 -0.0001 -0.0002 0.9997 0.9998 0.9999 1.0000 Table 5: MCS p-values for May 2017 - September 2021 out-of-sample period using the January 2000 - April 2017 in-sample period. Bold indicates the models included in c M0.95, and h indicates the forecast horizon.
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2. > 0 for all k, l = 1, . . . , r with k < l. These conditions match their Assumption 1, except our factor DGP is assumed to be given by (2). However, we can write cf. (3), ∆δk0 t fkt = ψ(L; ξk0)−1 εkt, t = 1, . . . , T, and under Assumption A.4, the RHS induces short-memory dynamics resembling the conditions in Barigozzi et al. (2021)’s Assumption 1(b). The assumptions regarding loadings given in our Assumption A.3 match their Assumption 2.
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3. 7 Appendix 7.1 Proof of Lemma 1 For the proof, we only show that our assumptions match those imposed by Barigozzi et al. (2021) for proving their Lemma 1 although in (1) we do not consider deterministic components, so the proof under our setup follows easier steps. Under our Assumption A.1, εt ∼ iid(0, Ωε), Ωε > 0, with E kεtk4 < ∞, rank E ∆δ0 t ft∆δ0 t f0 t = r, with r fixed, and E ∆δk0 t fkt 2 > E ∆δl0 t flt 2
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4. ——— (2002b): “Forecasting Using Principal Components from a Large Number of Predictors,” Journal of the American Statistical Association, 97, 1167–1179.
   Paper not yet in RePEc: Add citation now
   
5. ——— (2004): “The generalized dynamic factor model consistency and rates,” Journal of Econometrics, 119, 231–255.
   Paper not yet in RePEc: Add citation now
   
6. ——— (2005): “The generalized dynamic factor model: one-sided estimation and forecasting,” Journal of the American Statistical Association, 100, 830–840.
   Paper not yet in RePEc: Add citation now
   
7. ——— (2008): “Large Dimensional Factor Analysis,” Foundations and Trends (R) in Econometrics, 3, 89–163.
   Paper not yet in RePEc: Add citation now
   
8. ——— (2013): “Principal Components Estimation and Identification of Static Factors,” Journal of Econometrics, 176, 18–29.
   Paper not yet in RePEc: Add citation now
   
9. ——— (2019): “Estimation for dynamic panel data with individual effects,” Econometric Theory, 36, 185–222.
   Paper not yet in RePEc: Add citation now
   
10. ——— (2021): “Large-dimensional Dynamic Factor Models: Estimation of Impulse-Response Functions with I(1) cointegrated factors,” Journal of Econometrics, 221, 455–482.
    Paper not yet in RePEc: Add citation now
    
11. Abadir, K. M., W. Distaso, and L. Giraitis (2007): “Nonstationarityextended local Whittle estimation,” Journal of Econometrics, 141, 1353–1384.
    Paper not yet in RePEc: Add citation now
    

12. Ahn, S. C. and A. R. Horenstein (2013): “Eigenvalue Ratio Test for the Number of Factors,” Econometrica, 81, 1203–1227.

13. Bai, J. (2003): “Inferential Theory for Factor Models of Large Dimensions,” Econometrica, 71, 135–171.

14. Bai, J. and S. Ng (2002): “Determining the number of factors in approximate factor models,” Econometrica, 70, 191–221.

15. Baker, R., N. Bloom, and S. J. Davis (2016): “Measuring Economic Policy Uncertainty,” The Quarterly Journal of Economics, 131(4), 1593–1636.

16. Baltagi, B., C. Kao, and F. Wang (2021): “Estimating and Testing High Dimensional Factor Models with Multiple Structural Changes,” Journal of Econometrics, 220(2), 349–365.

17. Barigozzi, M. and M. Hallin (2016): “Generalized dynamic factor models and volatilities: recovering the market volatility shocks,” The Econometrics Journal, 19.

18. Barigozzi, M., M. Lippi, and M. Luciani (2016): “Non-Stationary Dynamic Factor Models for Large Datasets,” Working Paper, arXiv:1602.02398.
    Paper not yet in RePEc: Add citation now
    

19. Bernanke, B. S., J. Boivin, and P. Eliasz (2005): “Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach, ” The Quarterly Journal of Economics, 120, 387–422.

20. Bollerslev, T., D. Osterrieder, N. Sizova, and G. Tauchen (2013): “Risk and Return: Long-Run Relationships, Fractional Cointegration, and Return Predictability,” Journal of Financial Economics, 108(2), 409–424.
    Paper not yet in RePEc: Add citation now
    
21. By the weak convergence results in Marinucci and Robinson (2000), hkT (δk) ⇒ λ0 ∞ (1; θk) Z 1 0
    Paper not yet in RePEc: Add citation now
    

22. Chamberlain, G. and M. Rothschild (1983): “Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets,” Econometrica: Journal of the Econometric Society, 1281–1304.

23. Chambers, M. J. (1998): “Long Memory and Aggregation in Macroeconomic Time Series,” International Economic Review, 39(4), 1053–1072.

24. Chauvet, M., Z. Senyuz, and E. Yoldas (2015): “What Does Realized Volatility Tell Us About Macroeconomic Fluctuations?” Journal of Economic Dynamics and Control, 52, 340–360.

25. Chen, W. W. and C. M. Hurvich (2006): “Semiparametric Estimation of Fractional Cointegrating Subspaces,” Annals of Statistics, 34(6), 2939–2979.
    Paper not yet in RePEc: Add citation now
    
26. Cheung, Y. L. (2021): “Long Memory Factor Model: On Estimation of Factor Memories,” Journal of Business and Economic Statistics, DOI: 10.1080/07350015.2020.1867559.
    Paper not yet in RePEc: Add citation now
    

27. Choi, I. (2012): “Efficient Estimation of Factor Models,” Econometric Theory, 28(2), 274–308.

28. Cipollini, A. and G. Kapetanios (2008): “A stochastic variance factor model for large datasets and an application to S&P data,” Economics Letters, 100, 130–134.

29. Cristadoro, R., M. Forni, L. Reichlin, and G. Veronese (2005): “A core inflation indicator for the euro area,” Journal of Money, Credit, and Banking, 37, 539–560.

30. Dordonnat, V., S. J. Koopman, and M. Ooms (2012): “Dynamic factors in periodic time-varying regressions with an application to hourly electricity load mo-delling,” Computational Statistics & Data Analysis, 56, 3134–3152.

31. Ergemen, Y. E. and C. Velasco (2017): “Estimation of Fractionally Integrated Panels with Fixed-Effects and Cross-Section Dependence,” Journal of Econometrics, 196(2), 248–258.

32. Ergemen, Y. E., N. Haldrup, and C. V. Rodrı́guez-Caballero (2016): “Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads,” Energy Economics, 60, 79–96.
    Paper not yet in RePEc: Add citation now
    

33. Fan, J., Y. Ke, and Y. Liao (2021): “Augmented Factor Models with Applications to Validating Market Risk Factors and Forecasting Bond Risk Premia,” Journal of Econometrics, 222(1), 269–294.

34. Fan, J., Y. Liao, and M. Mincheva (2013): “Large Covariance Estimation by Thresholding Principal Orthogonal Complements,” Journal of the Royal Statistical Society – Series B, 75, 603–680.

35. Favero, C. A., M. Marcellino, and F. Neglia (2005): “Principal components at work: the empirical analysis of monetary policy with large data sets,” Journal of Applied Econometrics, 20, 603–620.

36. Following Hualde and Robinson (2011), we give the proof for the most general case where possibly δk ≤ δk0 −1/2. While δk may take arbitrary values from [δk, δk] for given δk < δk, ensuring uniform convergence of Lk,T (θk) requires the study of cases depending on δk0 −δk, noting that under Assumption A.4 short memory dynamics are dominated by long memory properties, see Robinson and Velasco (2015). We analyze each case separately in the following.
    Paper not yet in RePEc: Add citation now
    

37. Forni, M., M. Hallin, M. Lippi, and L. Reichlin (2000): “The generalized dynamic-factor model: Identification and estimation,” The Review of Economics and Statistics, 82, 540–554.

38. Gil-Alaña, L. and P. Robinson (1997): “Testing of Unit Root and Other Nonstationary Hypotheses in Macroeconomic Time Series,” Journal of Econometrics, 80(2), 241–268.
    Paper not yet in RePEc: Add citation now
    

39. Granger, C. (1980): “Long Memory Relationships and the Aggregation of Dynamic Models,” Journal of Econometrics, 14, 227–238.

40. Hallin, M., C. Mathias, H. Pirotte, and D. Veredas (2011): “Market liquidity as dynamic factors,” Journal of econometrics, 163, 42–50.

41. Hansen, P. R., A. Lunde, and J. M. Nason (2011): “The model confidence set,” Econometrica, 79, 453–497.
    Paper not yet in RePEc: Add citation now
    

42. Hartl, T. (2020): “Macroeconomic Forecasting with Fractional Factor Models,” arXiv preprint arXiv:2005.04897.

43. Hartl, T. and R. Weigand (2019): “Multivariate fractional components analysis, ” arXiv preprint arXiv:1812.09149.
    Paper not yet in RePEc: Add citation now
    

44. Hosoya, Y. (2005): “Fractional Invariance Principle,” Journal of Time Series Analysis, 26, 463–486.

45. Hualde, J. and P. M. Robinson (2011): “Gaussian Pseudo-Maximum Likelihood Estimation of Fractional Time Series Models,” The Annals of Statistics, 39(6), 3152–3181.
    Paper not yet in RePEc: Add citation now
    

46. Kapetanios, G. (2004): “A note on modelling core inflation for the UK using a new dynamic factor estimation method and a large disaggregated price index dataset,” Economics Letters, 85, 63–69.

47. Karabiyik, H. and J. Westerlund (2021): “Forecasting using cross-section averageâaugmented time series regressions,” The Econometrics Journal, 24(2), 315–333.

48. Luciani, M. and D. Veredas (2015): “Estimating and Forecasting Large Panels of Volatilities with Approximate Dynamic Factor Models,” Journal of Forecasting, 34(3), 163–176.

49. Marinucci, D. and P. Robinson (2000): “Weak Convergence of Multivariate Fractional Processes,” Stochastic Processes and their Applications, 86, 103–120.

50. Michelacci, C. and P. Zaffaroni (2000): “(Fractional) Beta Convergence,” Journal of Monetary Economics, 45, 129–153.

51. Morana, C. (2007): “On the Macroeconomic Causes of Exchange Rates Volatility, ” ICER Working Papers.

52. Nielsen, M. Ø. (2015): “Asymptotics for the Conditional-Sum-of-Squares Estimator in Multivariate Fractional Time Series Models,” Journal of Time Series Analysis, doi: 10.1111/jtsa.12100.

53. Onatski, A. (2010): “Determining the number of factors from empirical distribution of eigenvalues,” The Review of Economics and Statistics, 92, 1004–1016.

54. Pesaran, H. (2006): “Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure,” Econometrica, 74(4), 967–1012.

55. Pesaran, M. H. and A. Chudik (2014): “Aggregation in Large Dynamic Panels,” Journal of Econometrics, 178, 273–285.

56. Ray, B. K. and R. S. Tsay (2000): “Long-Range Dependence in Daily Stock Volatilities,” Journal of Business and Economic Statistics, 18(2), 254–262.

57. Robinson, P. M. (1978): “Statistical Inference for a Random Coefficient Autoregressive Model,” Scandinavian Journal of Statistics, 5, 163–168.
    Paper not yet in RePEc: Add citation now
    

58. Robinson, P. M. and C. Velasco (2015): “Efficient Inference on Fractionally Integrated Panel Data Models with Fixed Effects,” Journal of Econometrics, 185, 435–452.

59. Stock, J. H. and M. W. Watson (2002a): “Forecasting using principal components from a large number of predictors,” Journal of the American statistical association, 97, 1167–1179.
    Paper not yet in RePEc: Add citation now
    
60. The conditions (1 − ρiL)eit = ai(L)uit for all i, where ai(L) = P∞ k=0 aikLk with P∞ k=0 k |aik| ≤ M, and |ρi| ≤ 1; ut = (u1t, . . . , uNt)0 , satisfy ut ∼ iid (0, Ωu) , Ωu > 0, with E kutk4 < ∞, and E (uitujt) = τij with PN j=1 |τij| ≤ M uniformly in i in Assumption A.1, together with our Assumption A.2 match those in Assumption 3 of Barigozzi et al. (2021). Finally, their Assumption 4 is stated within our Assumption A.1 as for all i and t ≤ 0, uit = 0 and εt = 0. Therefore, the uniform result on the loadings readily applies in our case. For obtaining the convergence rates of the factor estimates, our setup is not affected by the estimation of deterministic components, which is why the rate N−(1−η) ,
    Paper not yet in RePEc: Add citation now
    
61. The convergence of the Hessian of Lk,T (θk), L̈k,T (θk) →p L̈k,T (θk0) can be shown as in Theorem 2 of Hualde and Robinson (2011) under Assumptions A.1 and A.4 since θ̂k →p θk0. The proof is then complete. Table 1: Memory Estimation Bias and Factor Estimate Consistency with r = 2, ξ 0 = 0, and ρ i = 0.5 This table reports the memory bias based on ˆ f t (i.e. δ̂ ˆ f − δ 0 ), memory bias based on f t (i.e. δ̂ f − δ 0 ), and the average R 2 measure from the regression of f t on ˆ f t (i.e. R̄ 2 ) across δ 0 ∈ {0, 0.3, 0.6, 1}, cross-section dimensions N ∈ {50, 100, 250, 400}, and time series lengths T ∈ {50, 100, 250, 400}.
    Paper not yet in RePEc: Add citation now
    
62. Then, applying Proposition 2 in Robinson and Velasco (2015), we have as T → ∞, 2 √ T T X t=1 εkt (χt (L; ξk0) εkt) →d N 0, 4σ4 kB (ξk0) under Assumptions A.1 and A.4. Finally, we analyze the second derivative of Lk,T (θk) evaluated at θk = θk0, (∂2 /∂θk∂θ0 k) Lk,T (θk)|θk=θk0 , which equals 2 T T X t=1 (χt (L; ξk0) εkt) (χt (L; ξk0) εkt)0 + 2 T T X t=1 εkt b0 t (L)εkt where b0 t (L) = χ̇t (L; ξk0)+χt (L; ξk0) χt (L; ξk0)0 and χ̇t (L; ξk) = (∂/∂θ0 ) χt (L; ξk) . Therefore, as T → ∞, ∂2 ∂θk∂θ0 k Lk,T (θk)|θk=θk0 →p 2σ2 kB (ξk0) .
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63. Uematsu, Y. and T. Yamagata (2022): “Estimation of SparsityInduced Weak Factor Models,” Journal of Business and Economic Statistics, https://doi.org/10.1080/07350015.2021.2008405.
    Paper not yet in RePEc: Add citation now
    

1. Nowcasting services trade for the G7 economies. (2024). Mourougane, Annabelle ; Gonzales, Frederic ; Jaax, Alexander.
   In: The World Economy.
   RePEc:bla:worlde:v:47:y:2024:i:4:p:1336-1386.

   Full description at Econpapers || Download paper

2. Housing Demand Shocks and Households’ Balance Sheets. (2021). Anderes, Marc.
   In: KOF Working papers.
   RePEc:kof:wpskof:21-492.

   Full description at Econpapers || Download paper

3. Quantile Factor Models. (2019). Gonzalo, Jesus ; Dolado, Juan ; Chen, Liang.
   In: Papers.
   RePEc:arx:papers:1911.02173.

   Full description at Econpapers || Download paper

4. How well can we learn large factor models without assuming strong factors?. (2019). Zhu, Yinchu.
   In: Papers.
   RePEc:arx:papers:1910.10382.

   Full description at Econpapers || Download paper

5. Sequential testing for structural stability in approximate factor models. (2018). Trapani, Lorenzo ; Barigozzi, Matteo.
   In: Papers.
   RePEc:arx:papers:1708.02786.

   Full description at Econpapers || Download paper

6. Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics. (2016). Stock, J H ; Watson, M W.
   In: Handbook of Macroeconomics.
   RePEc:eee:macchp:v2-415.

   Full description at Econpapers || Download paper

7. Generalized Efficient Inference on Factor Models with Long-Range Dependence. (2016). Ergemen, Yunus Emre .
   In: CREATES Research Papers.
   RePEc:aah:create:2016-05.

   Full description at Econpapers || Download paper

8. Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?. (2015). Hartigan, Luke .
   In: Discussion Papers.
   RePEc:swe:wpaper:2015-17.

   Full description at Econpapers || Download paper

9. Short-term forecasting with mixed-frequency data: A MIDASSO approach. (2015). Siliverstovs, Boriss.
   In: KOF Working papers.
   RePEc:kof:wpskof:15-375.

   Full description at Econpapers || Download paper

10. A noisy principal component analysis for forward rate curves. (2015). Laurini, Márcio ; Ohashi, Alberto .
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:246:y:2015:i:1:p:140-153.

    Full description at Econpapers || Download paper

11. Asymptotic analysis of the squared estimation error in misspecified factor models. (2015). Onatski, Alexei.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:186:y:2015:i:2:p:388-406.

    Full description at Econpapers || Download paper

12. Risks of large portfolios. (2015). Liao, Yuan ; Fan, Jianqing ; Shi, Xiaofeng .
    In: Journal of Econometrics.
    RePEc:eee:econom:v:186:y:2015:i:2:p:367-387.

    Full description at Econpapers || Download paper

13. Estimating the common break date in large factor models. (2015). Chen, Liang.
    In: Economics Letters.
    RePEc:eee:ecolet:v:131:y:2015:i:c:p:70-74.

    Full description at Econpapers || Download paper

14. Analyzing business cycle asymmetries in a multi-level factor model. (2015). Breitung, Jörg ; Eickmeier, Sandra.
    In: Economics Letters.
    RePEc:eee:ecolet:v:127:y:2015:i:c:p:31-34.

    Full description at Econpapers || Download paper

15. Empirical Analysis of Agricultural Commodity Prices, Crude Oil Prices and US Dollar Exchange Rates using Panel Data Econometric Methods. (2015). Rezitis, Anthony.
    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2015-03-28.

    Full description at Econpapers || Download paper

16. The macroeconomic effects of the sovereign debt crisis in the euro area. (2015). Ropele, Tiziano ; Neri, Stefano.
    In: Temi di discussione (Economic working papers).
    RePEc:bdi:wptemi:td_1007_15.

    Full description at Econpapers || Download paper

17. Supervision in Factor Models Using a Large Number of Predictors. (2015). Hillebrand, Eric ; Boldrini, Lorenzo .
    In: CREATES Research Papers.
    RePEc:aah:create:2015-38.

    Full description at Econpapers || Download paper

18. Efficient estimation of heterogeneous coefficients in panel data models with common shock. (2014). Lu, Lina ; Li, Kunpeng.
    In: MPRA Paper.
    RePEc:pra:mprapa:59312.

    Full description at Econpapers || Download paper

19. Commodity-Price Comovement and Global Economic Activity. (2014). Coibion, Olivier ; Bhattarai, Saroj ; Alquist, Ron.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:20003.

    Full description at Econpapers || Download paper

20. Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities. (2014). Schorfheide, Frank ; Liao, Zhipeng ; Cheng, Xu.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:19792.

    Full description at Econpapers || Download paper

21. Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models. (2014). Okui, Ryo ; Iwakura, Haruo .
    In: KIER Working Papers.
    RePEc:kyo:wpaper:887.

    Full description at Econpapers || Download paper

22. The KOF Economic Barometer, Version 2014. (2014). Sturm, Jan-Egbert ; Siliverstovs, Boriss ; Graff, Michael ; Abberger, Klaus.
    In: KOF Working papers.
    RePEc:kof:wpskof:14-353.

    Full description at Econpapers || Download paper

23. Das neue KOF Konjunkturbarometer – Version 2014. (2014). Sturm, Jan-Egbert ; Siliverstovs, Boriss ; Graff, Michael ; Abberger, Klaus.
    In: KOF Analysen.
    RePEc:kof:anskof:v:8:y:2014:i:1:p:91-106.

    Full description at Econpapers || Download paper

24. Linear regression for panel with unknown number of factors as interactive fixed effects. (2014). Weidner, Martin ; Moon, Hyungsik.
    In: CeMMAP working papers.
    RePEc:ifs:cemmap:35/14.

    Full description at Econpapers || Download paper

25. Understanding the Deviations of the Taylor Rule: A New Methodology with an Application to Australia. (2014). Vespignani, Joaquin ; Hudson, Kerry B..
    In: CAMA Working Papers.
    RePEc:een:camaaa:2014-78.

    Full description at Econpapers || Download paper

26. Sufficient information in structural VARs. (2014). Gambetti, Luca ; Forni, Mario.
    In: Journal of Monetary Economics.
    RePEc:eee:moneco:v:66:y:2014:i:c:p:124-136.

    Full description at Econpapers || Download paper

27. Detecting big structural breaks in large factor models. (2014). Gonzalo, Jesus ; Dolado, Juan ; Chen, Liang ; JuanJ. Dolado, .
    In: Journal of Econometrics.
    RePEc:eee:econom:v:180:y:2014:i:1:p:30-48.

    Full description at Econpapers || Download paper

28. Selection of the number of factors in presence of structural instability: a Monte Carlo study. (2014). Stevanovic, Dalibor ; MAO TAKONGMO, Charles Olivier.
    In: CIRANO Working Papers.
    RePEc:cir:cirwor:2014s-44.

    Full description at Econpapers || Download paper

29. Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis. (2014). Henzel, Steffen ; Rengel, Malte.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_4991.

    Full description at Econpapers || Download paper

30. Commodity Price Co-Movement and Global Economic Activity. (2014). Coibion, Olivier ; Alquist, Ron.
    In: Staff Working Papers.
    RePEc:bca:bocawp:14-32.

    Full description at Econpapers || Download paper

31. The Global Partnership for Sustainable Development. (2013). Quibria, M.G. ; Huang, Yongfu.
    In: WIDER Working Paper Series.
    RePEc:unu:wpaper:wp2013-057.

    Full description at Econpapers || Download paper

32. Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons. (2013). Choi, In.
    In: Working Papers.
    RePEc:sgo:wpaper:1209.

    Full description at Econpapers || Download paper

33. Global Currency Misalignments, Crash Sensitivity, and Downside Insurance Costs. (2013). Zhao, Yang ; MacDonald, Ronald ; Huang, Huichou.
    In: MPRA Paper.
    RePEc:pra:mprapa:53745.

    Full description at Econpapers || Download paper

34. A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets. (2013). GUO-FITOUSSI, Liang .
    In: MPRA Paper.
    RePEc:pra:mprapa:50005.

    Full description at Econpapers || Download paper

35. Detecting Big Structural Breaks in Large Factor Models. (2013). Gonzalo, Jesus ; Dolado, Juan ; Chen, Liang.
    In: Economics Series Working Papers.
    RePEc:oxf:wpaper:677.

    Full description at Econpapers || Download paper

36. Bond Spreads and Economic Activity in Eight European Economies. (2013). Mizen, Paul ; Bleaney, Michael ; Veleanu, Veronica.
    In: Discussion Papers.
    RePEc:not:notcfc:13/09.

    Full description at Econpapers || Download paper

37. The Comovement in Commodity Prices; Sources and Implications. (2013). Coibion, Olivier ; Alquist, Ron.
    In: IMF Working Papers.
    RePEc:imf:imfwpa:2013/140.

    Full description at Econpapers || Download paper

38. Testing for Factor Loading Structural Change under Common Breaks. (2013). Yamamoto, Yohei ; Tanaka, Shinya .
    In: Discussion Papers.
    RePEc:hit:econdp:2013-17.

    Full description at Econpapers || Download paper

39. Shrinkage estimation of high-dimensional factor models with structural instabilities. (2013). Schorfheide, Frank ; Liao, Zhipeng ; Cheng, Xu.
    In: Working Papers.
    RePEc:fip:fedpwp:14-4.

    Full description at Econpapers || Download paper

40. Large panel data models with cross-sectional dependence: a survey. (2013). Pesaran, M ; Chudik, Alexander.
    In: Globalization Institute Working Papers.
    RePEc:fip:feddgw:153.

    Full description at Econpapers || Download paper

41. Group Invariance, Likelihood Ratio Tests, and the Incidental Parameter Problem in a High-Dimensional Linear Model. (2013). Moreira, Marcelo ; Hallin, Marc ; Onatski, Alexei ; Marcelo Moreira J., .
    In: Working Papers ECARES.
    RePEc:eca:wpaper:2013/137736.

    Full description at Econpapers || Download paper

42. Dimensions of macroeconomic uncertainty: A common factor analysis. (2013). Henzel, Steffen ; Rengel, Malte.
    In: ifo Working Paper Series.
    RePEc:ces:ifowps:_167.

    Full description at Econpapers || Download paper

43. Large Panel Data Models with Cross-Sectional Dependence: A Survey. (2013). Pesaran, M ; Chudik, Alexander.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_4371.

    Full description at Econpapers || Download paper

44. Factor models. (2011). Choi, In ; Breitung, Jörg.
    In: Working Papers.
    RePEc:sgo:wpaper:1121.

    Full description at Econpapers || Download paper

45. Principal Components and Factor Analysis. A Comparative Study.. (2011). Travaglini, Guido.
    In: MPRA Paper.
    RePEc:pra:mprapa:35486.

    Full description at Econpapers || Download paper

46. Panel Data Models with Unobserved Multiple Time- Varying Effects to Estimate Risk Premium of Corporate Bonds. (2010). Kneip, Alois ; Bada, Oualid .
    In: MPRA Paper.
    RePEc:pra:mprapa:26006.

    Full description at Econpapers || Download paper

47. Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005.. (2010). Travaglini, Guido.
    In: MPRA Paper.
    RePEc:pra:mprapa:22077.

    Full description at Econpapers || Download paper

48. Cross-sectional Dependence in Panel Data Analysis. (2010). Sarafidis, Vasilis ; Wansbeek, Tom.
    In: MPRA Paper.
    RePEc:pra:mprapa:20367.

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49. A Robust Criterion for Determining the Number of Factors in Approximate Factor Models. (2009). Capasso, Marco ; Barigozzi, Matteo ; Alessi, Lucia.
    In: Working Papers ECARES.
    RePEc:eca:wpaper:2009_023.

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50. Bond Spreads as Predictors of Economic Activity in Eight European Economies. (). Mizen, Paul ; Bleaney, Michael ; Veleanu, Veronica.
    In: Discussion Papers.
    RePEc:not:notcfc:12/11.

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