Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-26T05:55:58.905Z Has data issue: false hasContentIssue false

STATIONARITY TESTS FOR IRREGULARLY SPACED OBSERVATIONS AND THE EFFECTS OF SAMPLING FREQUENCY ON POWER

Published online by Cambridge University Press:  19 July 2005

Fabio Busetti
Affiliation:
Bank of Italy
A.M. Robert Taylor
Affiliation:
University of Birmingham

Abstract

In this article, starting from continuous-time local level unobserved components models for stock and flow data we derive locally best invariant (LBI) stationarity tests for data available at potentially irregularly spaced points in time. We demonstrate that the form of the LBI test differs between stock and flow variables. In cases where the data are observed at regular intervals throughout the sample we show that the LBI tests for stock and flow data both reduce to the form of the standard stationarity test in the discrete-time local level model. Here we also show that the asymptotic local power of the LBI test increases with the sampling frequency in the case of stock, but not flow, variables. Moreover, for a fixed time span we show that the LBI test for stock (flow) variables is (is not) consistent against a fixed alternative as the sampling frequency increases to infinity. We also consider the case of mixed frequency data in some detail, providing asymptotic critical values for the LBI tests for both stock and flow variables, together with a finite-sample power study. Our results suggest that tests that ignore the infraperiod aspect of the data involve rather small losses in efficiency relative to the LBI test in the case of flow variables but can result in significant losses of efficiency when analyzing stock variables.We are grateful to co-editor Jeff Wooldrige, two anonymous referees, Marcus Chambers, Andrew Harvey, Rod McCrorie, Neil Shephard, and seminar participants at Nuffield College, Oxford, for their helpful comments and suggestions on earlier versions of this paper. We are especially indebted to Marcus Chambers, who suggested the formulation of the observation equations used in our continuous-time unobserved components models for stock and flow variables. All errors are ours.

Type
Research Article
Copyright
© 2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bailey, R.W. & A.M.R. Taylor (2002) An optimal test against a random walk component in a non-orthogonal unobserved components model. Econometrics Journal 5, 520532.Google Scholar
Bergstrom, A.R. (1983) Gaussian estimation of structural parameters in higher order continuous time dynamic models. Econometrica 51, 117152.Google Scholar
Bergstrom, A.R. (1984) Continuous time stochastic models and issues of aggregation over time. In Z. Griliches & M. Intriligator (eds.), Handbook of Econometrics, vol. 2, pp. 11451212. North-Holland.
Bergstrom, A.R. (1985) The estimation of parameters in nonstationary higher-order continuous-time dynamic models. Econometric Theory 1, 369385.Google Scholar
Bergstrom, A.R. (1986) The estimation of open higher-order continuous-time dynamic models with mixed stock and flow data. Econometric Theory 2, 350373.Google Scholar
Busetti, F. & A.C. Harvey (2001) Testing for the presence of a random walk in series with structural breaks. Journal of Time Series Analysis 22, 127150.Google Scholar
Canova, F. & B.E. Hansen (1995) Are seasonal patterns constant over time? A test for seasonal stability. Journal of Business & Economic Statistics 2, 292349.Google Scholar
Chambers, M.J. (2004) Testing for unit roots with flow data and varying sampling frequency. Journal of Econometrics 119, 118.Google Scholar
Chambers, M.J. & J. McGarry (2002) Modelling cyclical behavior with differential-difference equations in an unobserved components framework. Econometric Theory 18, 387419.Google Scholar
Comte, F. (1999) Discrete and continuous time cointegration. Journal of Econometrics 88, 207226.Google Scholar
Doornik, J.A. (1998) Object-Oriented Matrix Programming Using Ox 2.0. Timberlake Consultants Press.
Harvey, A.C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Harvey, A.C. (2001) Testing in unobserved components models. Journal of Forecasting 20, 119.Google Scholar
Harvey, A.C. & J.H. Stock (1988) Continuous time autoregressive models with common stochastic trends. Journal of Economic Dynamics and Control 12, 365384.Google Scholar
Harvey, A.C. & J.H. Stock (1989) Estimating integrated higher-order continuous time autoregressions with an application to money-income causality. Journal of Econometrics 42, 319336.Google Scholar
Harvey, A.C. & J.H. Stock (1993) Estimation, smoothing, interpolation, and distribution for structural time-series models in continuous time. In P.C.B. Phillips (ed.), Models, Methods, and Applications of Econometrics: Essays in Honour of A.R. Bergstrom, pp. 5570. Blackwell.
Imhof, J.P. (1961) Computing the distribution of quadratic forms in normal variables. Biometrika 48, 419426.Google Scholar
King, M.L. & G.H. Hillier (1985) Locally best invariant tests of the error covariance matrix of the linear regression model. Journal of the Royal Statistical Society, Series B 47, 98102.Google Scholar
Koopman, S.J., A.C. Harvey, J.A. Doornik, & N. Shephard (2000) STAMP 6.0, Structural Time Series Analyser, Modeller and Predictor. Chapman and Hall.
Kwiatkowski, D., P.C.B. Phillips, P. Schmidt, & Y. Shin (1992) Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics 54, 159178.Google Scholar
Nabeya, S. & K. Tanaka (1988) Asymptotic theory of a test for the constancy of regression coefficients against the random walk alternative. Annals of Statistics 16, 218235.Google Scholar
Ng, S. (1995) Testing for unit roots in flow data sampled at different frequencies. Economics Letters 47, 237242.Google Scholar
Nishino, H. (2002) Stationarity Test for Data with Missing Observations. Manuscript, Faculty of Law and Economics, Chiba University, Japan.
Nyblom, J. (1986) Testing for deterministic linear trend in time series. Journal of the American Statistical Association 81, 545549.Google Scholar
Nyblom, J. & A.C. Harvey (2000) Tests of common stochastic trends. Econometric Theory 16, 176199.Google Scholar
Nyblom, J. & T. Mäkeläinen (1983) Comparisons of tests for the presence of random walk coefficients in a simple linear model. Journal of the American Statistical Association 78, 856864.Google Scholar
Perron, P. (1991) Test consistency with varying sampling frequency. Econometric Theory 7, 341368.Google Scholar
Phillips, P.C.B. (1987) Time series regression with a unit root. Econometrica 55, 277301.Google Scholar
Phillips, P.C.B. (1991) Error correction and long-run equilibrium in continuous time. Econometrica 59, 967980.Google Scholar
Phillips, P.C.B. & S. Jin (2002) The KPSS test with seasonal dummies. Economics Letters 77, 239243.Google Scholar
Phillips, P.C.B. & Z. Xiao (1998) A primer on unit root testing. Journal of Economic Surveys 12, 423470.Google Scholar
Stock, J.H. (1994) Unit roots, structural breaks and trends. In R.F. Engle & D.L. McFadden (eds.), Handbook of Econometrics, vol. 4, pp. 27392840. Elsevier Science.
Tanaka, K. (1996) Time Series Analysis: Nonstationary and Noninvertible Distribution Theory. Wiley.
Wymer, C. (1993) Estimation of nonlinear continuous-time models from discrete data. In P.C.B. Phillips (ed.), Models, Methods and Applications of Econometrics: Essays in Honour of A.R. Bergstrom, pp. 91114. Blackwell.