IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v17y1988i1p93-120.html
   My bibliography  Save this article

Measures of Dependence for Cross-Lagged Panel Models

Author

Listed:
  • LAWRENCE S. MAYER

    (Arizona State University)

  • STEVEN S. CARROLL

    (Arizona State University)

Abstract

Panel studies are statistical studies in which two or more responses are measured for two or more individuals, or other sampling units, at two or more waves, or points in time. Typically the number of responses and the number of waves are small but the number of subjects is large. Cross-lagged panel studies are those in which the responses are continuous and divide naturally into two sets. One focus in such studies is to estimate and test the temporal effects with emphasis given to the effects of each set on the other. One approach to making such inferences involves casting the model as a set of regression equations and then estimating and testing the parameters by some variant of classical regression methods. Within this regression approach several authors have considered the problem of summarizing the degree of dependence by adapting the principle of “reduction in error.†We consider the same problem and develop measures from the multivariate likelihood ratio test statistics. We interpret these measures by relating them, for a stationary process, to the “feedback†measures developed for vector autoregressive processes. Finally we display the use of these measures on two sets of panel data.

Suggested Citation

  • Lawrence S. Mayer & Steven S. Carroll, 1988. "Measures of Dependence for Cross-Lagged Panel Models," Sociological Methods & Research, , vol. 17(1), pages 93-120, August.
    • Handle: RePEc:sae:somere:v:17:y:1988:i:1:p:93-120
    DOI: 10.1177/0049124188017001005
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124188017001005
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124188017001005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Alok Bhargava & J. D. Sargan, 2006. "Estimating Dynamic Random Effects Models From Panel Data Covering Short Time Periods," World Scientific Book Chapters, in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 1, pages 3-27, World Scientific Publishing Co. Pte. Ltd..
    2. Taylor, William E., 1980. "Small sample considerations in estimation from panel data," Journal of Econometrics, Elsevier, vol. 13(2), pages 203-223, June.
    3. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    4. P. Bentler, 1986. "Structural modeling and psychometrika: An historical perspective on growth and achievements," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 35-51, March.
    5. Geweke, John, 1984. "Inference and causality in economic time series models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 19, pages 1101-1144, Elsevier.
    6. P. Bentler & David Weeks, 1980. "Linear structural equations with latent variables," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 289-308, September.
    7. Pierce, David A. & Haugh, Larry D., 1977. "Causality in temporal systems : Characterization and a survey," Journal of Econometrics, Elsevier, vol. 5(3), pages 265-293, May.
    8. Larry D. Haugh & David A. Pierce, 1977. "Causality in temporal systems: characterizations and a survey," Special Studies Papers 87, Board of Governors of the Federal Reserve System (U.S.).
    9. Mayer, Lawrence S, 1986. "On Cross-Lagged Panel Models with Serially Correlated Errors," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(3), pages 347-357, July.
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    11. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dufour, Jean-Marie & Pelletier, Denis & Renault, Eric, 2006. "Short run and long run causality in time series: inference," Journal of Econometrics, Elsevier, vol. 132(2), pages 337-362, June.
    2. Uri, Noel D., 1999. "Energy and the use of conservation tillage in US agriculture," Energy Policy, Elsevier, vol. 27(5), pages 299-306, May.
    3. Bai, Zhidong & Wong, Wing-Keung & Zhang, Bingzhi, 2010. "Multivariate linear and nonlinear causality tests," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(1), pages 5-17.
    4. Nidhal Mgadmi & Houssem Rachdi & Hichem Saidi & Khaled Guesmi, 2019. "On the Instability of Tunisian Money Demand: Some Empirical Issues with Structural Breaks," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(1), pages 153-165, March.
    5. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    6. Marc Hallin & Abdessamad Saidi, 2005. "Testing Non‐Correlation and Non‐Causality between Multivariate ARMA Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 83-105, January.
    7. Utku Utkulu & Durmus Özdemir, 2005. "Does Trade Liberalization Cause a Long Run Economic Growth in Turkey," Economic Change and Restructuring, Springer, vol. 37(3), pages 245-266, September.
    8. Abdurrahman Korkmaz & Mustafa Erhan Bilman, 2017. "The S-curve Behaviour of the Trade Balance," Foreign Trade Review, , vol. 52(1), pages 1-14, February.
    9. Phipps, Tim, 1982. "Farmland Prices and the Return to Land: An Application of Causality Testing," 1982 Annual Meeting, August 1-4, Logan, Utah 279162, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    10. Cheung, Yin-Wong & Ng, Lilian K., 1996. "A causality-in-variance test and its application to financial market prices," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 33-48.
    11. S. Gurcan Gulen, 1996. "Is OPEC a Cartel? Evidence from Cointegration and Causality Tests," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 43-57.
    12. Allan Fels & Tran Van Hoa, 1981. "Causal Relationships in Australian Wage Inflation and Minimum Award Rates," The Economic Record, The Economic Society of Australia, vol. 57(1), pages 23-34, March.
    13. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    14. Langley, Suchada Vichitakul, 1982. "The formation of price expectations: a case study of the soybean market," ISU General Staff Papers 198201010800009358, Iowa State University, Department of Economics.
    15. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    16. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    17. Yazdanpanah, Ahmad, 1994. "The impact of oil price on food security in the Algeria, Iran, and Saudi Arabia: cointegration, vector-error correction model, dynamics, and causality analysis," ISU General Staff Papers 1994010108000011661, Iowa State University, Department of Economics.
    18. McCrorie, J. Roderick & Chambers, Marcus J., 2006. "Granger causality and the sampling of economic processes," Journal of Econometrics, Elsevier, vol. 132(2), pages 311-336, June.
    19. Cheng Hsiao, 1977. "Money And Income, Causality Detection," NBER Working Papers 0167, National Bureau of Economic Research, Inc.
    20. R. Jeffery Green & Gary R. Albrecht, 1979. "Testing for Causality in Regional Econometric Models," International Regional Science Review, , vol. 4(2), pages 155-164, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:somere:v:17:y:1988:i:1:p:93-120. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.