No CrossRef data available.
Article contents
Time Series Analysis in Pooled Cross-Sections
Published online by Cambridge University Press: 18 October 2010
Abstract
This article proposes the use of spectral methods to pool cross-sectional replications (N) of time series data (T) for time series analysis. Spectral representations readily suggest a weighting scheme to pool the data. The asymptotically desirable properties of the resulting estimators seem to translate satisfactorily into samples as small as T = 25 with N = 5. Simulation results, Monte Carlo results, and an empirical example help confirm this finding. The article concludes that there are many empirical situations where spectral methods canbe used where they were previously eschewed.
Information
- Type
- Articles
- Information
- Copyright
- Copyright © Cambridge University Press 1986
References
REFERENCES
1.Akaike, H.Fitting autoregressive models for prediction. Annals Institute Statistics Math.21 (1969): 243–247.Google Scholar
2.Anderson, T. W. and Hsiao., C.Formulation and estimation of dynamic models using panel data. Journal of Econometrics 18 (1982): 47–82.CrossRefGoogle Scholar
3.Anderson, T. W. and Hsiao., C.Estimation of dynamic models with error components.Journal of the American Statistical Association 76 (1981): 598–606.CrossRefGoogle Scholar
5.Ashenfelter, O.Estimating the effect of training programs on earnings. Review of Economics and Statistics 60 (1978): 47–57.Google Scholar
6.Balestra, P. and Nerlove., M.Pooling cross section and time series data in the estimation of a dynamic model. Econometrica 34 (1966): 585–612.Google Scholar
7.Beggs, J. J. Systemic effects and the dynamic interactions between profits and research and development. Mimeo, 1985.Google Scholar
9.Granger, C.W.J. and Hatanaka., M.Spectral analysis of economic time series. Princeton University Press, 1964.Google Scholar
10.Grenander, U. and Rosenblatt., M.Statistical analysis of stationary time series. New York, John Wiley, 1957.CrossRefGoogle Scholar
11.Grether, D. and Nerlove., M.Some properties of “optimal” seasonal adjustment. Econometrica 38 (1970): 682–703.Google Scholar
12.Hannan, E. J. and Quinn., d B. G.The determination of the order of an autoregression. Journal of the Royal Statistical Society, Series B 41 (1979): 190–195.Google Scholar
13.Hannan, E. J.Fourier methods and random processes. Bulletin of the International Statistical Institute 42 (1960): 475–496.Google Scholar
14.Heckman, J. J.Simple statistical models for discrete panel data developed and applied to test the hypothesis of true state dependence against th e hypothesis of spuriou s state dependence. Annales de I'nsee 30–31 (1978): 227–269.Google Scholar
15.Heckman, J. J. The incidental parameters problem and the problem of initial conditions in estimating a discrete time-discrete data stochastic process and some Monte Carlo evidence.In McFadden, D. and Manski, C. (eds), Structural Analysis of Discrete Data. Mass: MIT Press, 1979.Google Scholar
16.Hausman, J. and Taylor., W.Panel data and unobserved individual effects. Econometrica 49 (1981):Google Scholar
17.Kuh, E.The validity of cross-sectionally estimated behavior equations in time series applications. Econometrica 27(1959): 197–214.Google Scholar
18.Lillard, L. and Willis., R. Dynamic aspects of earnings mobility. Econometrica (1978): 985–1012.CrossRefGoogle Scholar
20.McCurdy, T. E.The use of time series processes to model the error structure of earnings i n a longitudinal data analysis. Journal of Econometrics 18, 1 (1981): 83–114.CrossRefGoogle Scholar
21.Mundlak, Y.On the pooling of time series and cross section data. Econometrica 46 (1978): 69–85.Google Scholar
22.Mundlak, Y. and Yahav., J. A.Random effects, fixed effects, convolution, and separation.Econometrica 49 (1981): 1399–1416.CrossRefGoogle Scholar
23.Nerlove, M.Spectral analysis of seasonal adjustment procedures. Econometrica 32 (1964): 241–286.Google Scholar
24.Nerlove, M. Distributed lags and unobserved components in economic time series. In Ten Economic Studies in the Tradition of Irving Fisher, pp. 127–170. New York: John Wiley, 1967.Google Scholar
25.Nerlove, M.Experimental evidence on the estimation of dynamic economic relations from a time series of cross sections. Economic Studies Quarterly 18 (1967): 42–74.Google Scholar
26.Nerlove, M.Furthe r evidence on the estimation of dynamic economic relations from a time series of cross sections. Econometrica 39 (1971): 359–382.Google Scholar
27.Nerlove, M., Grether, D., and Carvalho., J.In Analysis of Economic Time Series: A Synthesis. New York: Academic Press, 1979.Google Scholar
28.Nickell, S.Biases in dynami c models with fixed effects. Econometrica 49 (1981): 1417–1426.Google Scholar
29.Penm, J.H.W. and Terrell., R. D.On the recursive fitting of subset autoregressions. Journal of Time Series Analysis 2, 4(1981).Google Scholar
30.Revankar, N. S.Analysis of regressions containing serially correlated and serially un correlated error components. International Economic Review 21 (1980): 185–199.Google Scholar
31.Schwarz, G.Estimating the dimension of a model. Annals of Statistics 6 (1978): 461–464.Google Scholar
32.Silver, J. L.Generalized estimation of error components with a serially correlated temporal effect. International Economic Review 23, 2 (1982): 463–478.Google Scholar
33.Taylor, W. E.Small sample considerations in estimation from pane l data. Journal of Econometrics 13 (1980):203–223.CrossRefGoogle Scholar
34.Trognon, A.Miscellaneous asymptotic properties of ordinary least squares and maximum likelihood estimators in dynamic error components models. Annales de I'insee 30–31 (1978): 632–657.Google Scholar
35.Wallace, T. and Hussain., A.The use of error components models in combining cross sections with time series data. Econometrica 37 (1969): 55–72.Google Scholar
- 3
- Cited by