An automatic Portmanteau test for serial correlation

Journal of Econometrics

Volumn 151, Issue 2, 2009, Pages 140-149

  Escanciano, J Carlos ^a   Lobato, Ignacio N ^b  

^a INDIANA UNIVERSITY   (United States)

^b INSTITUTO TECNOLÓGICO AUTÓNOMO DE MÉXICO   (Mexico)

Author keywords

Akaike's AIC; Autocorrelation; Consistency; Power; Schwarz's BIC

Indexed keywords

AKAIKE'S AIC; CONSISTENCY; CRITICAL VALUE; DATA-DRIVEN; DEGREE OF FREEDOM; HETEROSKEDASTICITY; NULL DISTRIBUTION; POWER; SCHWARZ'S BIC; SERIAL CORRELATION;

CORRELATION DETECTORS;

TESTING;

EID: 67650747649     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2009.03.001     Document Type: Article

References (64)

1. Aerts M., Claeskens G., and Hart J.D. Testing lack of fit in multiple regression. Journal of the American Statistical Association 94 (1999) 869-879
2. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 (1974) 716-723
3. Bhansali R.J. A derivation of the information criteria for selecting autoregressive models. Advances in Applied Probability 18 (1986) 360-387
4. Bhansali R.J., and Downhamd Y. Some properties of the order of an autoregressive model selected by a generalization of Akaike's FPE criterion. Biometrika 64 (1977) 547-551
5. Bartlett M.S. An Introduction to Stochastic Processes with Special Reference to Methods and Applications (1955), Cambridge University Press, London
6. Bera A.K., and Ghosh A. Neyman's smooth test and its applications in econometrics. In: Ullah A., Wan A., and Chaturvedi A. (Eds). Handbook of Applied Econometrics and Statistical Inference (2001), Marcel Dekker, New York 170-230 (Chapter 10)
7. Bera A.K., and Higgins M.L. ARCH models: Properties, estimation and testing. Journal of Economic Surveys 7 (1993) 305-362
8. Billingsley P. The Lindeberg-Levy theorem for martingales. Proceedings of the American Mathematical Society 12 (1961) 788-792
9. Bloomfield P. An exponential model for the spectrum of a scalar time series. Biometrika 60 (1973) 217-222
10. Bollerslev T., and Wooldridge J.M. Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews 11 (1992) 143-179
11. Box G., and Pierce D. Distribution of residual autocorrelations in autoregressive integrated moving average time series models. Journal of American Statistical Association 65 (1970) 1509-1527
12. Cumby R.E., and Huizinga J. Testing the autocorrelation structure of disturbances in ordinary least squares and instrumental variable regressions. Econometrica 60 (1992) 185-196
13. Davies N., and Newbold P. Some power studies of a Portmanteau test of time series model specification. Biometrika 66 (1979) 153-155
14. Davies N., Triggs C.M., and Newbold P. Significance levels of the Box-Pierce Portmanteau statistic in finite samples. Biometrika 64 (1977) 517-522
15. Delgado, M.A., Velasco, C., 2007. A new class of distribution-free tests for time series models specification. Preprint
16. Deo R.S. Spectral tests of the martingale hypothesis under conditional heteroskedasticity. Journal of Econometrics 99 (2000) 291-315
17. Diebold F.X. Testing for serial correlation in the presence of heteroskedasticity. Proceedings of the American Statistical Association, Business and Economics Statistics Section (1986) 323-328
18. Durlauf S. Spectral based testing of the martingale hypothesis. Journal of Econometrics 50 (1991) 355-376
19. Escanciano J.C. On the lack of power of omnibus specification tests. Econometric Theory 25 (2009) 162-194
20. Escanciano, J.C., Lobato, I.N., 2007. Testing the martingale hypothesis. In: Patterson, K., Mills, T.C. (Eds.), Palgrave Handbook of Econometrics, MacMillan, Palgrave (in press)
21. Escanciano, J.C., Mayoral, S., 2007. Data-driven smooth tests for the martingale difference hypothesis. Universidad de Navarra Working Paper no. 01/07
22. Eubank R. Testing for no effect by cosine series methods. Scandinavian Journal of Statistics 27 (2000) 747-763
23. Eubank R., and Hart J. Testing goodness-of-fit in regression via order selection criteria. Annals of Statistics 20 (1992) 1412-1425
24. Eubank R., and LaRiccia V. Asymptotic comparison of Cramer-von Mises and nonparametric function estimation techniques for testing goodness-of-fit. Annals of Statistics 20 (1992) 2071-2086
25. Fan J. Test of significance based on wavelet thresholding and Neyman's truncation. Journal of the American Statistical Association 91 (1996) 674-688
26. Fan J., and Huang L. Goodness-of-fit tests for parametric regression models. Journal of the American Statistical Association 96 (2001) 640-652
27. Francq C., Roy R., and Zakoïan J.-M. Diagnostic checking in ARMA models with uncorrelated errors. Journal of the American Statistical Association 13 (2005) 532-544
28. Godfrey L.G. Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables. Econometrica 46 (1978) 1303-1310
29. Grenander U., and Rosenblatt M. Statistical Analysis of Stationary Time Series (1957), Chelsea Publishing Company, New York
30. Guo, B., Phillips, P.C.B., 2003. Testing for autocorrelation and unit roots in the presence of conditional heteroskedasticity of unknown form. Cowles Foundation for Research in Economics, Yale University
31. Hall P., and Heyde C. Martingale Limit Theory and Its Application (1980), Academic Press, New York
32. Hong Y. Consistent testing for serial correlation of unknown form. Econometrica 64 (1996) 837-864
33. Hong Y., and Lee T.H. Inference on predictability of foreign exchange rate changes via generalized spectrum and nonlinear time series models. Review of Economics and Statistics 85 (2003) 1048-1062
34. Horowitz J., Lobato I.N., Nankervis J., and Savin N.E. Bootstrapping the Box-Pierce Q test: A robust test of uncorrelatedness. Journal of Econometrics 133 (2006) 841-862
35. Inglot T., and Ledwina T. Asymptotic optimality of data-driven Neyman's tests for uniformity. Annals of Statistics 24 (1996) 1982-2019
36. Inglot T., and Ledwina T. Intermediate approach to comparison of some goodness-of-fit tests. Annals of the Institute of Statistical Mathematics 53 (2001) 810-834
37. Inglot T., and Ledwina T. Towards data driven selection of a penalty function for data driven Neyman tests. Linear Algebra and its Applications 417 (2006) 124-133
38. Inglot, T., Ledwina, T., 2006b. Data driven score tests of fit for semiparametric homoscedastic linear regression model. Preprint 665, Institute of Mathematics Polish Academy of Sciences
39. Janssen A. Global power functions of goodness of fit tests. Annals of Statistics 28 (2000) 239-253
40. Kallenberg W.C.M. Intermediate efficiency, theory and examples. The Annals of Statistics 15 (1983) 1401-1420
41. Kallenberg W.C.M. The penalty in data driven Neyman's tests. Mathematical Methods of Statistics 11 (2002) 323-340
42. Kallenberg W.C.M., and Ledwina T. Consistency and Monte Carlo simulation of a data driven version of smooth goodness of fit tests. The Annals of Statistics 23 (1995) 1594-1608
43. Kallenberg W.C.M., and Ledwina T. Data-driven smooth tests when the hypothesis is composite. Journal of the American Statistical Association 92 (1997) 1094-1104
44. Koziol J.A. Percentage points of the asymptotic distributions of one and two sample Kuiper statistics for truncated or censored data. Technometrics 22 (1980) 437-442
45. Ledwina T. Data-driven version of Neyman's smooth test of fit. Journal of the American Statistical Association 89 (1994) 1000-1005
46. Li W.K., and McLeod A.I. Distribution of the residual autocorrelations in multivariate ARMA time series. Journal of the Royal Statistical Society, Series B 43 (1981) 231-239
47. Ljung G.M., and Box G.E.P. On a measure of lack of fit in time series models. Biometrika 65 (1978) 297-303
48. Lo A.W., and MacKinlay A.C. The size and power of the variance ratio test in finite samples: A Monte Carlo investigation. Journal of Econometrics 40 (1989) 203-238
49. Lobato I.N., Nankervis J.C., and Savin N.E. Testing for autocorrelation using a modified Box-Pierce Q test. International Economic Review 42 (2001) 187-205
50. Lobato I.N., Nankervis J.C., and Savin N.E. Testing for zero autocorrelation in the presence of statistical dependence. Econometric Theory 18 (2002) 730-743
51. Lobato I.N., and Robinson P.M. A nonparametric test for I(0). Review of Economic Studies 65 (1998) 475-495
52. Milbrodt H., and Strasser H. On the asymptotic power of the two-sided Kolmogorov-Smirnov test. Journal of Statistical Planning and Inference 26 (1990) 1-23
53. Milhøj A. A test of fit in time series models. Biometrika 68 (1981) 177-187
54. Nankervis, J.C., Savin, N.E., 2007. Testing for serial correlation: Generalized Andrews-Ploberger tests. Working paper. University of Essex
55. Neuhaus G. Asymptotic power properties of the Cramér-von Mises test under contiguous alternatives. Journal of Multivariate Analysis 6 (1976) 95-110
56. Neyman J. Smooth test for goodness of fit. Scandinavian Aktuarietidskr 20 (1937) 149-199
57. Nielsen B. Correlograms for non-stationary autoregressions. Journal of the Royal Statistical Society, Series B 68 (2006) 707-720
58. Rayner J.C.W., and Best D.J. Smooth Tests of Goodness of Fit (1989), Oxford University Press, London
59. Robinson P.M. Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression. Journal of Econometrics 47 (1991) 67-84
60. Robinson P.M. Efficient tests of non-stationary hypothesis. Journal of the American Statistical Association 89 (1994) 1420-1437
61. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 6 (1978) 461-464
62. Weiss A. ARCH and bilinear time series models: Comparison and combination. Journal of Business and Economic Statistics 4 (1986) 59-70
63. Woodroofe M. On model selection and Arc sine laws. The Annals of Statistics 10 (1982) 1182-1194
64. Yule G.U. Why do we sometimes get nonsense correlations between time-series? A study in sampling and the nature of time-series. Journal of the Royal Society 89 (1926) 1-63