Abadie, A., Athey, S., Imbens, G.W., and Wooldridge, J.M. (2020). “Sampling Based versus Design Based Uncertainty in Regression Analysis,” Econometrica, 88, 265–296.CrossRefGoogle Scholar

Abadie, A., Diamond, A. and Hainmueller, J. (2010). “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program,” Journal of the American Statistical Association 105, 493–505.CrossRefGoogle Scholar

Abraham, K., Jarmin, R.S., Moyer, B.C., and Shapiro, M., ed. (2021). Big Data for Twenty-First Century Economic Statistics, Chicago: University of Chicago Press.Google Scholar

Abramowitz, M., and Stegun, J. (1965). Handbook of Mathematical Functions with Formulas, Graphs and Mathematical Tables, New York: Dover.Google Scholar

Acemoglu, D., Akcigit, U. and Kerr, W. (2016). “Networks and the Macroeconomy: An Empirical Exploration,” in NBER Macroeconomics Annual 2015, edited by Eichenbaum, M. and Parker, J., Vol. 30, Chapter 4, pp. 276–335, Chicago: University of Chicago Press.Google Scholar

Ahn, H., and Horenstein, A.R. (2013). “Eigenvalue Ration Test for the Number of Factors,” Econometrica, 81(3), 1203–1227.Google Scholar

Ahn, H., and Powell, J.L. (1993). “Semiparametric Estimation of Censored Selection Models with a Nonparametric Selection Mechanism,” Journal of Econometrics, 58, 3–30.CrossRefGoogle Scholar

Ahn, S.C. (2015). “Comment on ‘IV Estimation of Panels with Factor Residuals,” Journal of Econometrics, 185, 525–544.CrossRefGoogle Scholar

Ahn, S.C., and Moon, H.R. (2001). “On Large-N and Large-T Properties of Panel Data Estimators and the Hausman Test,” mimeo.CrossRefGoogle Scholar

Ahn, S.C., and Schmidt, P. (1995). “Efficient Estimation of Models for Dynamic Panel Data,” Journal of Econometrics, 68, 5–27.CrossRefGoogle Scholar

Ahn, S.C., Lee, Y.H., and Schmidt, P. (2001). “GMM Estimation of Linear Panel Data Models with Time-Varying Individual Effects,” Journal of Econometrics, 101, 219–255.CrossRefGoogle Scholar

Ahn, S.C., Lee, Y.H., and Schmidt, P. (2013). “Panel Data Models with Multiple Time-Varying Individual Effects,” Journal of Econometrics, 174, 1–14.CrossRefGoogle Scholar

Ai, C., and Li, Q. (2005). “Estimation of Partly Specified Panel Data Tobit Models,” mimeo.Google Scholar

Ai, C., and Li, Q. (2008). “Semi-Parametric and Non-Parametric Models in Panel Data Models,” in The Econometrics of Panel Data, 3rd ed., edited by Matyas, L. and Sevestre, P., pp. 451–478, Berlin: Springer-Verlag.CrossRefGoogle Scholar

Aigner, D.J., and Balestra, P. (1988). “Optimal Experimental Design for Error Components Models,” Econometrica, 56, 955–972.CrossRefGoogle Scholar

Aigner, D.J., Hsiao, C., Kapteyn, A., and Wansbeek, T. (1984). “Latent Variable Models in Econometrics,” in Handbook of Econometrics, vol. II, edited by Griliches, Z. and Intriligator, M., pp. 1322–1393, Amsterdam: North-Holland.Google Scholar

Akaike, H. (1973). “Information Theory and an Extension of the Maximum Likelihood Principle,” in Proceedings of the 2nd. International Symposium Information Theory, edited by Petrov, B.N. and Csaki, F., pp. 267–281, Budapest: Akademiai Kiado.Google Scholar

Akashi, K., and Kunitomo, N. (2011). “The Limited Information Maximum Likelihood Approach to Dynamic Structural Equation Models,” mimeo.Google Scholar

Akashi, K., and Kunitomo, N. (2012). “Some Properties of the LIML Estimator in a Dynamic Panel Structural Equation,” Journal of Econometrics, 166, 167–183.CrossRefGoogle Scholar

Alessi, L., Barigozzi, M., and Capasso, M. (2010). “Improved Penalization for Determining the Number of Factors in Approximate Factor Models,” Statistics Probability Letters, 80, 1806–1813.CrossRefGoogle Scholar

Almon, S. (1965). “The Distributed Lag Between Capital Approximations and Expenditures,” Econometrica, 33, 178–196.Google Scholar

Alvarez, J., and Arellano, M. (2003). “The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators,” Econometrica, 71, 1121–1159.Google Scholar

Amemiya, T. (1971). “The Estimation of the Variance in a Variance-Component Model,” International Economic Review, 12, 1–13.CrossRefGoogle Scholar

Amemiya, T. (1974). “Bivariate Probit Analysis: Minimum Chi-Square Methods,” Journal of the American Statistical Association, 69, 940–944.CrossRefGoogle Scholar

Amemiya, T. (1976). “The Maximum Likelihood, the Minimum Chi-Square and the Nonlinear Weighted Least Squares Estimator in the General Qualitative Response Model,” Journal of the American Statistical Association, 71, 347–351.CrossRefGoogle Scholar

Amemiya, T. (1978). “A Note on a Random Coefficients Model,” International Economic Review, 19, 793–796.CrossRefGoogle Scholar

Amemiya, T. (1980a). “Selection of Regressors,” International Economic Review, 21, 331–354.CrossRefGoogle Scholar

Amemiya, T. (1980b). “The n−2-Order Mean Squared Errors of the Maximum Likelihood and the Minimum Logit Chi-Square Estimator,” Annals of Statistics 8, 488–505.CrossRefGoogle Scholar

Amemiya, T. (1981). “Qualitative Response Models: A Survey,” Journal of Economic Literature, 19, 1483–1536.Google Scholar

Amemiya, T. (1984). “Tobit Models: A Survey,” Journal of Econometrics, 24, 3–62.CrossRefGoogle Scholar

Amemiya, T. (1985). Advanced Theory of Econometrics, Cambridge: Harvard University Press.Google Scholar

Amemiya, T., and Fuller, W.A. (1967). “A Comparative Study of Alternative Estimators in a Distributed-Lag Model,” Econometrica, 35, 509–529.Google Scholar

Amemiya, T., and MaCurdy, T.E. (1986). “Instrumental Variable Estimation of An Error Components Model,” Econometrica, 54, 869–880.CrossRefGoogle Scholar

Amemiya, T., and Wu, R.Y. (1972). “The Effect of Aggregation on Prediction in the Autoregressive Model,” Journal of the American Statistical Association, 67(339), 628–632.CrossRefGoogle Scholar

Amemiya, T., and Wu, R.Y. (1970). “Asymptotic Properties of Conditional Maximum Likelihood Estimators,” Journal of the Royal Statistical Society, Series B, 32, 283–301.Google Scholar

Amemiya, T., and Wu, R.Y. (1973). Conditional Inference and Models for Measuring, Københarn: Mental-hygiejnish Farlag.Google Scholar

Amemiya, T., and Wu, R.Y. (1959). “On Asymptotic Distributions of Estimates of Parameters of Stochastic Differences Equations,” Annals of Mathematical Statistics 30, 676–687.Google Scholar

Amemiya, T., and Wu, R.Y. (1969). “Statistical Inference for Covariance Matrices with Linear Structure,” in Multivariate Analysis, vol. 2, edited by Krishnaiah, P.R., pp. 55–66, New York: Academic Press.Google Scholar

Amemiya, T., and Wu, R.Y. (1970). “Estimation of Covariance Matrices Which Are Linear Combinations or Whose Inverses Are Linear Combinations of Given Matrices,” in Essays in Probability and Statistics, edited by Bose, R.C., pp. 1–24, Chapel Hill: University of North Carolina Press.Google Scholar

Amemiya, T., and Wu, R.Y. (1971). The Statistical Analysis of Time Series. New York: Wiley.Google Scholar

Amemiya, T., and Wu, R.Y. (1978). “Repeated Measurements on Autoregressive Processes,” Journal of the American Statistical Association, 73, 371–378.Google Scholar

Amemiya, T., and Wu, R.Y. (1985). An Introduction to Multivariate Analysis, 2nd ed., New York: John Wiley and Sons.Google Scholar

Anderson, T.W., and Hsiao, C. (1981). “Estimation of Dynamic Models with Error Components,” Journal of the American Statistical Association, 76, 598–606.CrossRefGoogle Scholar

Anderson, T.W., and Hsiao, C. (1982). “Formulation and Estimation of Dynamic Models Using Panel Data,”Journal of Econometrics, 18, 47–82.CrossRefGoogle Scholar

Anderson, T.W., and Rubin, H. (1950). “The Asymptotic Properties of Estimates of the Parameters of a Single Equation in a Complete System of Stochastic Equations,” Annals of Mathematical Statistics, 21(4), 570–582.Google Scholar

Anderson, T.W., and Rubin, H. (1956). “Statistical Inference in Factor Analysis,” Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 5, 111–150.Google Scholar

Ando, T., and Bai, J. (2020). “Quantile Co-Movement in Financial Markets: A Panel Quantile Model with Unobserved Heterogeneity,” Journal of the American Statistical Association, 115, 266–279.Google Scholar

Anselin, L. (1988). Spatial Econometrics: Methods and Models, Dordrecht: Kluwer Academic.CrossRefGoogle Scholar

Anselin, L., and Griffith, D.A. (1988). “Do Spatial Effects Really Matter in Regression Analysis?” Papers of the Regional Science Association, 65, 11–34.Google Scholar

Anselin, L., Le Gallo, J., and Jayet, H. (2008). “Spatial Panel Econometrics,” in The Econometrics of Panel Data, 3rd ed., edited by Matyas, L. and Sevestre, P., pp. 625–660, Berlin: Springer-Verlag.Google Scholar

Antweiler, W. (2001). “Nested Random Effects Estimation in Unbalanced Panel Data,” Journal of Econometrics, 101, 295–313.Google Scholar

Arellano, M. (2003). Panel Data Econometrics, Oxford: Oxford University Press.Google Scholar

Arellano, M., and Bond, S. (1991). “Some Tests of Specification for Panel Data: Monte Carlo Evidence and An Application to Employment Equations,” Review of Economic Studies, 58, 277–297.CrossRefGoogle Scholar

Arellano, M., and Bonhomme, S. (2009). “Robust Priors in Nonlinear Panel Data Models,” Econometrica, 77, 489–536.Google Scholar

Arellano, M., and Bonhomme, S. (2012). “Identifying Distributional Characteristics in Random Coefficients Panel Data Models,” Review of Economics Studies, 79, 987–1020.Google Scholar

Arellano, M., and Bover, O. (1995). “Another Look at the Instrumental Variable Estimation of Error-Components Models,” Journal of Econometrics, 68, 29–51.Google Scholar

Arellano, M., and Carrasco, R. (2003). “Binary Choice Panel Data Models with Predetermined Variables,” Journal of Econometrics, 357–381.Google Scholar

Arellano, M., and Honoré, B. (2001). “Panel Models: Some Recent Development,” in Handbook of Econometrics, vol. 5, edited by Heckman, J. and Leamer, E., pp. 3229–3296. Amsterdam: North-Holland.Google Scholar

Arellano, M., Bover, O., and Labeaga, J. (1999). “Autoregressive Models with Sample Selectivity for Panel Data,” in Analysis of Panels and Limited Dependent Variable Models, edited by Hsiao, C., Lahiri, K., Lee, L.F., and Pesaran, M.H., pp. 23–48, Cambridge: Cambridge University Press.Google Scholar

Ashenfelter, O., and Solon, G. (1982). “Longitudinal Labor Market Data-Sources, Uses and Limitations,” in What's Happening to American Labor Force and Productivity Measurements? pp. 109–126. Proceedings of a June 17, 1982, conference sponsored by the National Council on Employment Policy, W.E. Upjohn Institute for Employment Research.Google Scholar

Ashenfelter, O., Deaton, A., and Solon, G. (1984). “Does It Make Sense to Collect Panel Data in Developing Countries?” mimeo, World Bank.Google Scholar

Athey, S. (2018). “The Impact of Machine Learning on Economics,” mimeo, Stanford University.Google Scholar

Athey, S. (2019). “The Impact of Machine Learning on Economics.” In The Economics of Artificial Intelligence, University of Chicago Press.Google Scholar

Athey, S., and Imbens, G.W. (2019). “Machine Learning Methods We Economists Should Know About,” mimeo, Stanford University.CrossRefGoogle Scholar

Athey, S., Imbens, G., Pham, T., and Wager, S. (2017). “Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges,” ar Xiv: 1702.01250.Google Scholar

Avery, R.B. (1977). “Error Components and Seemingly Unrelated Regressions,” Econometrica, 45, 199–209.Google Scholar

Bai, J. (2009a). “Panel Data Models with Interactive Fixed Effects,” Econometrica, 77, 1229–1279.Google Scholar

Bai, J. (2009b). “Supplement to Panel Data Models with Interactive Fixed Effects, Technical Details and Proofs,” mimeo.Google Scholar

Bai, J., and Carrion-i-Silvestre, J. (2009). “Structural Changes, Common Stochastic Trends and Unit Roots in Panel Data,” Review of Economic Studies, 76, 471–501.CrossRefGoogle Scholar

Bai, J., and Ng, S. (2002). “Determining the Number of Factors in Approximate Factor Models,” Econometrica, 70, 191–221.Google Scholar

Bai, J., and Ng, S. (2004). “A Panic on Unit Root Tests and Cointegration,” Econometrica, 72, 1127–1177.CrossRefGoogle Scholar

Bai, J., and Ng, S. (2010). “Panel Unit Root Tests with Cross-Section Dependence: A Further Investigation,” Econometric Theory, 26, 1088–1114.CrossRefGoogle Scholar

Bai, Z.D., and Saranadasa, H. (1996). “Effect of High Dimension: By an Example of a Two Sample Problem,” Statistical Sinica, 6, 311–329.Google Scholar

Bai, Z.D., and Silverstein, J.W. (2004). “CLT for Linear Spectral Statistics of Large-Dimensional Sample Covariance Matrices,” Annals of Probability, 32(1A), 553–605.CrossRefGoogle Scholar

Bailey, T., Jaggars, S.S., and Jenkins, D. (2015). Redesigning American Community Colleges: A Clearer Path to Student Success. Cambridge: Cambridge Harvard University Press.CrossRefGoogle Scholar

Baillie, R.T., and Baltagi, B.H. (1999). “Prediction from the Regression Model with One-Way Error Components,” in Analysis of Panels and Limited Dependent Variable Models, edited by Hsiao, C., Lahiri, K., Lee, L.F., and Pesaran, M.H., pp. 255–267, Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Baker, S., Bloom, N., and Davis, S. (2016). “Measuring Economic Policy Uncertainty,” The Quarterly Journal of Economics, 131, 1593–1636.Google Scholar

Balazsi, L., Matyas, L., and Wansbeek, T. (2017). “Fixed Effects Models,” in The Econometrics of Multi-Dimensional Panels: Theory and Application, edited by Matyas, L., pp. 1–34, Springer, Switzerland.Google Scholar

Balestra, P., and Nerlove, M. (1966). “Pooling Cross-Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas,” Econometrica, 34, 585–612.Google Scholar

Baltagi, B.H. (1980). “On Seemingly Unrelated Regressions with Error Components,” Econometrica, 48, 1547–1551.Google Scholar

Baltagi, B.H. (1981a). “Simultaneous Equations with Error Components,” Journal of Econometrics 17, 189–200.CrossRefGoogle Scholar

Baltagi, B.H. (1981b). “Pooling: An Experimental Study of Alternative Testing and Estimation Procedures in a Two-Way Error Components Model,” mimeo, University of Houston.Google Scholar

Baltagi, B.H. (1995). Econometric Analysis of Panel Data, New York: Wiley.Google Scholar

Baltagi, B.H., and Griffin, J.M. (1983). “Gasoline Demand in the OECD: An Application of Pooling and Testing Procedures,” European Economic Review, 22, 117–137.Google Scholar

Baltagi, B.H., and Kao, C. (2000). “Nonstationary Panels, Cointegration in Panels and Dynamic Panels, A Survey in Nonstationary Panels, Panel Cointegration and Dynamic Panels,” Advances in Econometrics, vol. 15, edited by Baltagi, B., Amsterdam: JAI Press, 7–52.Google Scholar

Baltagi, B.H., and Li, Q. (1991). “A Transformation That Will Circumvent the Problem of Autocorrelation in an Error Component Model,” Journal of Econometrics, 48, 385–393.CrossRefGoogle Scholar

Baltagi, B.H., and Li, Q. (1992). “A Monotonic Property for Iterative GLS in the Two-Way Random Effects Model,” Journal of Econometrics, 53, 45–51.CrossRefGoogle Scholar

Baltagi, B.H., Song, S., and Jung, B. (2001). “The Unbalanced Nested Error Component Regression Model,” Journal of Econometrics, 101, 357–381.Google Scholar

Baltagi, B.H., Song, S., Jung, B., and Koh, W. (2007). “Testing for Serial Correlation, Spatial Autocorrelation and Random Effects Using Panel Data,” Journal of Econometrics, 140, 5–51.Google Scholar

Banerjee, A. (1999). “Panel Data Unit Roots and Cointegration: An Overview,” Oxford Bulletin of Economics and Statistics, 61, 607–629.Google Scholar

Banerjee, A., Marcellino, M., and Osbat, C. (2005). “Testing for PPP: Should We Use Panel Methods?” Empirical Economics, 30, 77–91.Google Scholar

Barro, R., and Sala-i-Martin, X. (1995). Economic Growth, New York: McGaw Hill.Google Scholar

Barth, J., Kraft, A., and Kraft, J. (1979). “A Temporal Cross-Section Approach to the Price Equation,” Journal of Econometrics 11, 335–351.CrossRefGoogle Scholar

Bartolucci, F., and Nigro, V. (2010). “A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting a Root-N Consistent Conditional Estimator,” Econometrica, 78, 719–733.Google Scholar

Bates, G., and Neyman, J. (1951). “Contributions to the Theory of Accident Proneness. II: True of False Contagion,” University of California Publications in Statistics, pp. 215–253.Google Scholar

Bates, J.M., and Granger, C.W.J. (1969). “The Combination of Forecasts,” Journal of the Operational Research Society, 20, 451–468.Google Scholar

Becketti, S., Gould, W., Lillard, L., and Welch, F. (1988). “The Panel Study of Income Dynamics after Fourteen Years: An Evaluation,” Journal of Labor Economics, 6, 472–492.Google Scholar

Beckwith, N. (1972). “Multivariate Analysis of Sales Response of Competing Brands to Advertising,” Journal of Marketing Research 9, 168–176.Google Scholar

Belloni, A., Chernozhukov, V., and Hansen, C. (2014a). “Structural and Treatment Effects,” The Journal of Economic Perspective, 28, 29–50.CrossRefGoogle Scholar

Belloni, A., Chernozhukov, V., and Hansen, C. (2014b). “Inference on Treatment Effects after Selection among High-Dimensional Controls,” The Review of Economic Studies, 81, 608–650.Google Scholar

Ben-Porath, Y. (1973). “Labor Force Participation Rates and the Supply of Labor,” Journal of Political Economy 81, 697–704.Google Scholar

Berkson, J. (1944). “Application of the Logistic Function to Bio-Assay,” Journal of the American Statistical Association, 39, 357–365.Google Scholar

Berkson, J. (1955). “Maximum Likelihood and Minimum χ² Estimates of the Logistic Function,” Journal of the American Statistical Association, 50, 130–162.Google Scholar

Berkson, J. (1957). “Tables for Use in Estimating the Normal Distribution Function by Normit Analysis,” Biometrika, 44, 411–435.Google Scholar

Berkson, J. (1980). “Minimum Chi-Square, Not Maximum Likelihood!” Annals of Statistics, 8, 457–487.Google Scholar

Bernard, A., and Jones, C. (1996). “Productivity across Industries and Countries: Time Series Theory and Evidence,” Review of Economics and Statistics, 78, 135–146.Google Scholar

Bester, C.A., and Hansen, C.B. (2012). “Grouped Effects in Fixed Effects Models,” Journal of Econometrics, 190, 197–208.Google Scholar

Bhargava, A., and Sargan, J.D. (1983). “Estimating Dynamic Random Effects Models from Panel Data Covering Short Time Periods,” Econometrica, 51, 1635–1659.CrossRefGoogle Scholar

Binder, M., Hsiao, C., and Pesaran, M.H. (2005). “Estimation and Inference in Short Panel Vector Autoregression with Unit Roots and Cointegration,” Econometric Theory, 21, 795–837.CrossRefGoogle Scholar

Biørn, E (1992). “Econometrics of Panel Data with Measurement Errors,” in Econometrics of Panel Data: Theory and Applications, edited by Mátyás, L. and Sevestre, P., pp. 152–195, Kluwer.Google Scholar

Biørn, E (2000). “Panel Data with Measurement Errors, Instrumental Variables and GMM Estimators Combining Levels and Differences,” Econometric Reviews, 19, 391–424.Google Scholar

Biørn, E., and Klette, T.J. (1998). “Panel Data with Errors-in-Variables: Essential and Redundant Orthogonality Conditions in GMM Estimation,” Econometrics Letters, 59, 275–282.CrossRefGoogle Scholar

Biørn, E., and Krishnakumar, J. (2008). “Measurement Errors and Simultaneity,” in The Econometrics of Panel Data, 3rd ed., edited by Matyas, L. and Sevestre, P., pp. 323–368, Berlin: Spring-Verlag.Google Scholar

Bishop, Y.M., Fienberg, S.E., and Holland, P.W. (1975). Discrete Multivariate Analysis, Theory and Practice, Cambridge: MIT Press.Google Scholar

Blundell, R., and Bond, S. (1998). “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, 87, 115–143.Google Scholar

Blundell, R., and Smith, R.J. (1991). “Conditions Initiales et Estimation Efficace dans les Modéles Dynamiques sur Donné es de Panel,” Annals d’Economies et de Statistique, 20–21, 109–124.Google Scholar

Blundell, R., Browning, M., and Meghir, C. (1994). “Consumer Demand and the Life Cycle Allocation of Household Expenditure,” Review of Economic Studies, 61, 57–80.Google Scholar

Blundell, R., Griffith, R., and Windmeijer, F. (2002). “Individual Effects and Dynamics in Count Data Models,” Journal of Econometrics, 102, 113–131.CrossRefGoogle Scholar

Bond, S., and Meghir, C. (1994). “Dynamic Investment Models and the Firm's Financial Policy,” Review of Economic Studies, 61, 197–222.Google Scholar

Bonhomme, S., and Manresa, E. (2015). “Grouped Patterns of Heterogeneity in Panel Data,” Econometrica, 83, 1147–1184.Google Scholar

Bonhomme, S., Lamadon, T., and Manresa, E. (2019). “Discretizing Unobserved Heterogeneity,” mimeo.Google Scholar

Borus, M.E. (1981). “An Inventory of Longitudinal Data Sets of Interest to Economists,” mimeo, Ohio State University.Google Scholar

Bound, J. (1991). “Self-Reported Versus Objective Measures of Health in Retirement Models,” Journal of Human Resources, 26, 106–138.Google Scholar

Box, G.E.P., and Jenkins, G.M. (1970). Time Series Analysis: Forecasting and Control, San Francisco: Holden-Day.Google Scholar

Box, G.E.P., and Tiao, G.C. (1968). “Bayesian Estimation of Means for the Random Effects Model,” Journal of the American Statistical Association, 63, 174–181.Google Scholar

Brainard, W.C., and Tobin, J. (1968). “Pitfalls in Financial Model Building,” American Economic Review, 58, 99–122.Google Scholar

Breiman, L. (1996). “Bagging Predictors,” Machine Learning, 24, 123–140.Google Scholar

Breiman, L. (2001). “Random Forests,” Machine Learning, 45, 5–32Google Scholar

Breitung, J., and Das, S. (2008). “Panel Unit Roots under Cross Sectional Dependence,” Statistica Nerrlandica, 59, 414–433.Google Scholar

Breitung, J., and Pesaran, M.H. (2008). “Unit Roots and Cointegration in Panels,” in The Econometrics of Panel Data, edited by Matyas, L. and Sevestre, P., pp. 279–322, Berlin: Spring-Verlag.Google Scholar

Bresson, G. (2020). “Comments on ‘An Econometrician's Perspective on Big Data’ by Cheng Hsiao,” Chapter 16, Advances in Econometrics, 41, 431–443.CrossRefGoogle Scholar

Bresson, G., and Hsiao, C. (2011). “A Functional Connectivity Approach for Modelling Cross-Sectional Dependence with an Application to the Estimation of Hedonic Housing Prices in Paris,” Advances in Statistical Analysis, 95, 501–509.Google Scholar

Bresson, G., Hsiao, C., and Pirotte, A. (2011). “Assessing the Contribution of R&D to Total Productivity – A Bayesian Approach to Account for Heterogeneity and Heteroscedasticity,” Advances in Statistical Analysis, 95, 435–452.CrossRefGoogle Scholar

Breusch, T.S. (1987). “Maximum Likelihood Estimation of Random Effects Models,” Journal of Econometrics, 36, 383–389.Google Scholar

Breusch, T.S., and Pagan, A.R. (1979). “A Simple Test for Heteroscedasticity and Random Coefficient Variation,” Econometrica, 47, 1287–1294.Google Scholar

Breusch, T.S., and Pagan, A.R. (1980). “The Lagrange Multiplier Test and His Application to Model Specification in Econometrics,” Review of Economic Studies, 47, 239–254.Google Scholar

Breusch, T.S., Mizon, G.E., and Schmidt, P. (1989). “Efficient Estimation Using Panel Data,” Econometrica, 51, 695–700.Google Scholar

Brock, W.A., and Durlauf, S.N. (2001). “Discrete Choice with Social Interactions,” The Review of Economics Studies, 68(2), 235–260.Google Scholar

Brock, W.A., and Durlauf, S.N. (2007). “Identification of Binary Choice Models with Social Interactions,” Journal of Econometrics, 140(1), 52–75.CrossRefGoogle Scholar

Burridge, P. (1980). “On the Cliff-Ord Test for Spatial Autocorrelation.” Journal of the Royal Statistical Society, Series B, 42, 107–108.Google Scholar

Butler, J.S., and Moffitt, R. (1982). “A Computationally Efficient Quadrature Procedure for the One Factor Multinominal Probit Model,” Econometrica, 50, 761–764.CrossRefGoogle Scholar

Cagan, P. (1958). “The Demand for Currency Relative to the Total Money Supply,” Journal of Political Economy, 66, 303–328.CrossRefGoogle Scholar

Cai, L., and Kalb, G. (2006). “Health Status and Labor Force Participation: Evidence from Australia,” Health Economics, 15, 241–256.Google Scholar

Cai, Z. (2007). “Trending Time-Varying Coefficient Time Series Models with Serially Correlated Errors,” Journal of Econometrics, 136, 163–188.Google Scholar

Cai, Z., Chen, L., and Fang, Y. (2018). “Quantile Panel Data Models with Partially Varying Coefficients,” Journal of Econometrics, 206, 531–553.Google Scholar

Campolieti, M. (2002). “Disability and the Labor Force Participation of Older Men in Canada,” Labor Economics, 9, 405–432.CrossRefGoogle Scholar

Canova, F. (1999). “Testing for Convergence Clubs in Income Per Capita: A Predictive Density Approach,” mimeo, Universitat Pompeu Fabra.Google Scholar

Card, D. (1996). “The Effect of Unions on the Structure of Wages: A Longitudinal Analysis,” Econometrica, 64, 957–979Google Scholar

Carro, J.M. (2007). “Estimating Dynamic Panel Data Discrete Choice Models with Fixed Effects,” Journal of Econometrics, 140, 503–528.Google Scholar

Case, A.C. (1991). “Spatial Patterns in Household Demand,” Econometrica, 59, 953–965.Google Scholar

Chamberlain, G. (1976). “Identification in Variance Components Models,” discussion paper No. 486, Harvard Institute of Economic Research.Google Scholar

Chamberlain, G. (1977a). “Education, Income, and Ability Revisited,” in Latent Variables in Socio-Economic Models, edited by Aigner, D.J. and Goldberger, A.S., pp. 143–161, Amsterdam: North-Holland.Google Scholar

Chamberlain, G. (1977b). “An Instrumental Variable Interpretation of Identification in Variance-Components and MIMIC Models,” in Kinometrics: Determinants of Social-Economic Success within and between Families, edited by Taubman, P., pp. 235–254, Amsterdam: North-Holland.Google Scholar

Chamberlain, G. (1978). “On the Use of Panel Data,” paper presented at the Social Science Research Council conference on life-cycle aspects of employment and the labor market, Mt. Kisco, New York.Google Scholar

Chamberlain, G. (1980). “Analysis of Covariance with Qualitative Data,” Review of Economic Studies, 47, 225–238.Google Scholar

Chamberlain, G. (1982). “Multivariate Regression Models for Panel Data,” Journal of Econometrics, 18, 5–46.Google Scholar

Chamberlain, G. (1984). “Panel Data,” in Handbook of Econometrics, vol. II, edited by Griliches, Z. and Intriligator, M., pp. 1247–1318, Amsterdam: North-Holland.Google Scholar

Chamberlain, G. (1992). “Efficiency Bounds for Semiparametric Regression,” Econometrica, 60, 567–596.Google Scholar

Chamberlain, G. (1993). “Feedback in Panel Data Models,” mimeo, Department of Economics, Harvard University.Google Scholar

Chamberlain, G. (2010). “Binary Response Models for Panel Data: Identification and Information,” Econometrica, 78, 159–168.Google Scholar

Chamberlain, G., and Griliches, Z. (1975). “Unobservables with a Variance-Components Structure: Ability, Schooling and the Economic Success of Brothers,” International Economic Review, 16, 422–450.Google Scholar

Chang, Y. (2002). “Nonlinear IV Unit Root Tests in Panels with Cross-Sectional Dependency,” Journal of Econometrics, 110, 261–292.Google Scholar

Chang, Y.S., Hu, B., and Park, J. (2018). “Econometric Analysis and Functional Dynamics in the Presence of Persistence,” paper presented at the conference in honor of P.C.B. Phillips, Yale University.Google Scholar

Charlier, E., Melenberg, B., and van Soest, A. (2000). “Estimation of a Censored Regression Panel Data Model Using Conditional Moment Restrictions Efficiently,” Journal of Econometrics, 95, 25–56.Google Scholar

Charlier, E., Melenberg, B., and van Soest, A. (2001). “An Analysis of Housing Expenditure Using Semiparametric Models and Panel Data,” Journal of Econometrics, 101, 71–108.Google Scholar

Chen, B., and Hong, Y. (2012). “Testing for Smooth Structural Changes in Time Series via Non Parametric Regression,” Econometrica, 80, 1157–1183.Google Scholar

Chen, J., Li, D., and Linton, O. (2019). “A New Semiparametric Estimation Approach for Large Dynamic Covariance Matrices with Multiple Conditioning Variables,” Journal of Econometrics, 212, 155–176.Google Scholar

Chen, K.M., Hsieh, Y.W., and Lin, M.J. (2020). “Prediction and Inequality in Two Sided Markets: An Experiment of Online Dating Recommender Systems,” mimeo.Google Scholar

Chen, S. (1999). “Distribution-Free Estimation of the Random Coefficient Dummy Endogenous Variable Model,” Journal of Econometrics, 91, 171–199.Google Scholar

Chen, S. (2000). “Efficient Estimation of Binary Choice Models Under Symmetry,” Journal of Econometrics, 96, 183–199.CrossRefGoogle Scholar

Chen, S. (2019). “Quantile Regression for Duration Models with Time-Varying Regressors,” Journal of Econometrics, 209, 1–17.Google Scholar

Chen, X. (2007). “Large Sample Sieve Estimation of Semi-nonparametric Models,” in Handbook of Econometrics, vol. 6, pp. 5549–5632, Amsterdam: Elsevier.Google Scholar

Chen, X. (2018). “Large Sample Sieve Estimation of Semi-nonparametric Models,” in Handbook of Econometrics, vol. 6, 5549–5632. North Holland.Google Scholar

Chen, X., and Shen, X. (1998). “Sieve Extremum Estimates for Weakly Dependent Data,” Econometrica, 66, 289–314.Google Scholar

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., and Newey, W. (2017). “Double/Debiased/Neyman Machine Learning of Treatment Effects,” American Economic Review Papers and Proceedings, 107, 261–265.CrossRefGoogle Scholar

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., and Newey, W. (2018). “Double/Debiased Machine Learning for Treatment and Structural Parameters,” The Econometrics Journal, 21, 1–68.Google Scholar

Chernozhukov, V., Hausman, J., and Newey, W. (2018). “Demand Analysis with Many Prices,” paper presented at the 2nd Annual Econometrics Forum, University of the Chinese Academy of Sciences, Beijing.Google Scholar

Chesher, A.D. (1983). “The Information Matrix Test: Simplified Calculation via a Score Test Interpretation,” Economics Letters, 13, 45–48.Google Scholar

Chesher, A.D. (1984). “Testing for Neglected Heterogeneity,” Econometrica, 52, 865–872.CrossRefGoogle Scholar

Chesher, A.D., and Lancaster, T. (1983). “The Estimation of Models of Labor Market Behavior,” Review of Economic Studies, 50, 609–624.Google Scholar

Chiang, C.L. (1956). “On Regular Best Asymptotically Normal Estimates,” Annals of Mathematical Statistics, 27, 336–351.Google Scholar

Chintagunta, P., Kyriazidou, E., and Perktold, J. (2001). “Panel Data Analysis of Household Brand Choices,” Journal of Econometrics, 101, 111–153.Google Scholar

Choi, I. (2001). “Unit Root Tests for Panel Data,” Journal of International Money and Finance, 20, 249–272.Google Scholar

Choi, I. (2002a). “Combination Unit Root Tests for Cross-Sectionally Correlated Panels,” in Econometric Theory and Practice: Frontiers of Analysis and Applied Research, Essays in Honor of P.C.B. Phillips, Cambridge: Cambridge University Press.Google Scholar

Choi, I. (2002b). “Instrumental Variable Estimation of a Nearly Nonstationary, Heterogeneous Error Components Model,” Journal of Econometrics, 109, 1–32.Google Scholar

Choi, I. (2006). “Nonstationary Panels,” in Palgrave Handbooks of Econometrics, Vol. I, edited by Patterson, K. and Mills, T.C., pp. 511–539, New York: Palgrave Macmillan.Google Scholar

Choi, I., and Chue, T.K. (2007). “Subsampling Hypothesis Tests for Nonstationary Panels with Applications to Exchange Rates and Stock Prices,” Journal of Applied Econometrics, 22, 223–264.Google Scholar

Choo, E., and Siow, A. (2006). “Who Marries Whom and Why,” Journal of Political Economy, 114, 175–201.Google Scholar

Chow, G.C. (1983). Econometrics, New York: McGraw-Hill.Google Scholar

Chow, G., and Lin, A.L. (1976). “Best Linear Unbiased Estimation of Missing Observations in an Economic Time Series,” Journal of the American Statistical Association, 71, 719–721.CrossRefGoogle Scholar

Chudik, C., and Pesaran, M.H. (2015). “Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors,” Journal of Econometrics 188, 393–420.Google Scholar

Chudik, C., and Pesaran, M.H. (2020). “An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels,” mimeo.Google Scholar

Chudik, A., Kapetanios, A., and Pesaran, M.H. (2018). “A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High Dimensional Linear Regression Models,” Econometrica, 86, 1479–1512.Google Scholar

Chudik, A., Pesaran, M.H., and Tosetti, E. (2011). “Weak and Strong Cross Section Dependence and Estimation of Large Panels,” The Econometrics Journal, 14, 45–90.Google Scholar

Chui, C.K. (1992). An Introduction to Wavelets, San Diego: Academic Press.Google Scholar

Clarkson, D.B. (1979). “Estimating the Standard Errors of Rotated Factor Loadings by Jackknifing, Psychometrika, 44, 297–314.Google Scholar

Coleman, J.S. (1964). Models of Change and Response Uncertainty, Englewood Cliffs, NJ: Prentice-Hall.Google Scholar

Collado, M.D. (1997). “Estimating Dynamic Models from Time Series of Independent Cross-Sections,” Journal of Econometrics, 82, 37–62.Google Scholar

Conley, T.G. (1999). “GMM Estimation with Cross-sectional Dependence,” Journal of Econometrics, 92, 1–45.Google Scholar

Cooley, T.F., and Prescott, E.C. (1976). “Estimation in the Presence of Stochastic Parameter Variation,” Econometrica, 44, 167–184.Google Scholar

Cornwell, C., and Schmidt, P. (1984). “Panel Data with Cross-Sectional Variation in Slopes as Well as Intercepts,” mimeo. Michigan State University.Google Scholar

Cosslett, S.R. (1981). “Maximum Likelihood Estimator for Choice-Based Samples,” Econometrica, 49, 1289–1316.Google Scholar

Cox, D.R. (1970). Analysis of Binary Data, London: Methuen.Google Scholar

Cox, D.R. (1972). “The Analysis of Multivariate Binary Data,” Journal of the Royal Statistical Society, Series C, Applied Statistics, 21, 113–120.Google Scholar

Cox, D.R. (1975). “Partial Likelihood,” Biometrika, 62, 269–276.Google Scholar

Crépon, B., and Duget, E. (1997). “Estimating the Innovation from Patent Numbers: GMM on Count Panel Data,” Journal of Applied Econometrics, 12, 243–263.Google Scholar

Crépon, B., and Mairesse, J. (1996). “The Chamberlain Approach,” in The Econometrics of Panel Data: A Handbook of the Theory with Applications, edited by Matyas, L. and Sevestre, P., pp. 323–391, Dordrecht: Kluwer Academic Publishers.Google Scholar

Cripps, T., and Tarling, R. (1974). “An Analysis of the Duration of Male Unemployment in Great Britain, 1932–1973,” Economic Journal, 84, 289–316.Google Scholar

Damrongplasit, K., and Hsiao, C. (2009). “Decriminalization Policy and Marijuana Smoking Prevalence: A Look at the Literature,” Singapore Economic Review, 59, 621–644.CrossRefGoogle Scholar

Damrongplasit, K., and Hsiao, C. (2021). “Heterogeneity and Dynamic Dependence in Panel Analysis of Individual Behavior,” in Advances in Econometrics, vol 43. Emerald Publishing (forthcoming).Google Scholar

Damrongplasit, K., Hsiao, C., and Zhao, X. (2010). “Decriminalization and Marijuana Smoking Prevalence: Evidence from Australia,” Journal of Business and Economic Statistics, 28, 344–356.Google Scholar

Damrongplasit, K., Hsiao, C., and Zhao, X. (2019). “Health Status and Labor Market Outcome: Empirical Evidence from Australia”, Pacific Economic Review, 24, 269–292.CrossRefGoogle Scholar

Davis, P. (2002). “Estimating Multi-Way Error Components Models with Unbalanced Data Structures,” Journal of Econometrics, 106, 67–95.Google Scholar

Deaton, A. (1985). “Panel Data from Time Series of Cross-Sections,” Journal of Econometrics, 30, 109–126.Google Scholar

Debarsy, N., Jin, F., and Lee, L.F. (2015). “Large Sample Properties of the Matrix Exponential Specification with an Application to FDI,” Journal of Econometrics, 188, 1–21.CrossRefGoogle Scholar

De Finetti, B. (1964). “Foresight: Its Logical Laws, Its Subjective Sources”, in Studies in Subjective Probability, edited by Krieger, R.E., Huntington, New York.Google Scholar

Dehejia, R.H., and Wahba, S. (1999). “Propensity Score-Matching Methods for Nonexperimental Causal Studies,” The Review of Economics and Statistics, 84, 151–161.Google Scholar

Dempster, A.P. (1958). “A High Dimensional Two Sample Significance Test,” Annals of Mathematical Statistics, 29, 995–1010.Google Scholar

Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). “Maximum Likelihood From Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society, Series B, 39, 1–38.Google Scholar

Dhaene, G., and Jochmans, K. (2015). “Split-Panel Jackknife Estimation of Fixed-Effect Models. Review of Economic Studies, 82(3), 991–1030.Google Scholar

Dhrymes, P. (1971). Distributed Lags: Problems of Estimation and Formulation, San Francisco: Holden-Day.Google Scholar

Dickey, D.A., and Fuller, W.A. (1979). “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, 427–431.Google Scholar

Dickey, D.A., and Fuller, W.A. (1981). “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, 49, 1057–1072.CrossRefGoogle Scholar

Diebold, F.X., and Mariano, R. (1985). “Comparing Predictive Accuracy.” Journal of Business and Economic Statistics, 13, 253–265.Google Scholar

Dielman, T., Nantell, T., and Wright, R. (1980). “Price Effects of Stock Repurchasing: A Random Coefficient Regression Approach,” Journal of Financial and Quantitative Analysis, 15, 175–189.Google Scholar

Donald, S., and Newey, W. (2001). “Choosing the Number of Instruments,” Econometrica, 69, 1161–1191.Google Scholar

Driscoll, J., and Kraay, A. (1998). “Consistent Covariance Matrix Estimation with Spatically Dependent Panel Data,” Review of Economics and Statistics, 80, 549–560.Google Scholar

Duan, J.C., and Wang, T. (2012). “Measuring Distance-to-Default for Financial and Non-Financial Firms,” Global Credit Review, 2, 95–108.Google Scholar

Duan, J.C., Sun, J., and Wang, T. (2012). “Multiperiod Corporate Default Prediction – A Forward Intensity Approach,” Journal of Econometrics, 170, 191–209.Google Scholar

Dufour, J.M., and Hsiao, C. (2008). “Identification,” in the New Palgrave Dictionary of Economics, 2nd ed., edited by Blume, L. and Durlauf, S., Palgrave Macmillan.Google Scholar

Duncan, G.M. (1980). “Formulation and Statistical Analysis of the Mixed Continuous/Discrete Dependent Variable Model in Classical Production Theory,” Econometrica, 48, 839–152.Google Scholar

Durbin, J. (1960). “Estimation of Parameters in Time-Series Regression Models,” Journal of the Royal Statistical Society, Series B, 22, 139–153.Google Scholar

Durlauf, S.N. (2001). “Manifesto for a Growth Econometrics,” Journal of Econometrics, 100, 65–69.Google Scholar

Durlauf, S.N., and Johnson, P. (1995). “Multiple Regimes and Cross-Country Growth Behavior,” Journal of Applied Econometrics, 10, 365–384.Google Scholar

Durlauf, S.N., and Quah, D.T. (1999). “The New Empirics of Economic Growth,” in Handbook of Macroeconomics, edited by Taylor, J. and Woodford, M., pp. 235–308, Amsterdam: North-Holland.Google Scholar

Eicker, F. (1963). “Asymptotic Normality and Consistency of the Least Squares Estimators for Families of Linear Regression,” Annals of Mathematical Statistics, 34, 447–456.Google Scholar

Efron, B. (1982). The Jackknife, the Bootstrap and Other Resampling Plans, SIAM, vol. 30.Google Scholar

Elliott, G., and Timmermann, A. (2017). Economic Forecasting, Princeton: Princeton University Press.Google Scholar

Engle, R.F., and Granger, C.W.J. (1987). “Cointegration and Error Correction: Representation, Estimation and Testing,” Econometrica, 55, 251–276.Google Scholar

Eurostat (1996). European Community Household Panel (ECHP), Office for Official Publications of the European Communities, Luxembourg.Google Scholar

Fan, J., and Gijbels, I. (1992). “Variable Bandwidth and Local Linear Regression Smoothers,” Annals of Statistics, 20, 2008–2036.Google Scholar

Fan, J., and Kim, D. (2018). “Robust High-Dimensional Volatility Matrix Estimation of High-Frequency Factor Model,” Journal of the American Statistical Association, 113, 1268–1283.Google Scholar

Fang, K.T. and Zhang, Y.T. (1990). Generalized Multivariate Analysis, Berlin: Springer.Google Scholar

Fan, Y., Lv, J., and Wang, J. (2018). “DNN: A Two-Scale Tale of Heterogenous Treatment Effects,” mimeo.Google Scholar

Fazzari, S.M., Hubbard, R.G., and Petersen, B.C. (1988). “Financing Constraints and Corporate Investment,” Brookings Papers on Economic Activity, 1, 141–195.Google Scholar

Ferguson, T.S. (1958). “A Method of Generating Best Asymptotically Normal Estimates with Application to the Estimation of Bacterial Densities,” Annals of Mathematical Statistics, 29, 1046–1162.Google Scholar

Firpo, S., Fortin, N., and Lemieux, T. (2007). “Decomposing Wage Distribution Using Recentered Influence Function Regressions,” unpublished manuscript.Google Scholar

Fisher, R.A. (1932). Statistical Methods for Research Workers, 4th ed., Edinburgh: Oliver and Boyd.Google Scholar

Florens, J.P., Fougére, D., and Mouchart, M. (1996). “Duration Models,” in The Econometrics of Panel Data, 2nd ed., edited by Matyas, L. and Sevestre, P., pp. 491–536, Dordrecht: Kluwer Academic Publishers.Google Scholar

Fomby, T.B. (2020). “Comments on ‘An Econometrician's Perspective on Big Data’ by Cheng Hsiao, Chapter 15, Advances in Econometrics, 41, 425–430.Google Scholar

Fougére, G., and Kamionka, T. (1996). “Individual Labour Market Transitions,” in The Econometrics of Panel Data: A Handbook of the Theory with Applications, 2nd ed., pp. 771–809, Dordrecht: Kluwer Academic Publishers.Google Scholar

Frankel, J.A., and Rose, A.K. (1996). “A Panel Project on Purchasing Power Parity: Mean Revision between and within Countries,” Journal of International Economics, 40, 209–244.Google Scholar

Freeman, R.B., and Medoff, J.L. (1981). “The Impact of Collective Bargaining: Illusion or Reality?” mimeo, Harvard University.Google Scholar

Friedman, M. (1953). Essays in Positive Economics, Chicago: University of Chicago Press.Google Scholar

Fujiki, H., and Hsiao, C. (2015). “Disentangling Multiple Treatment Effects – Measuring the Net Economic Impact of the 1995 Great Hanshin-Awaji Earthquake,” Journal of Econometrics 186, 66–73.Google Scholar

Fuller, W.A., and Battese, G.E. (1974). “Estimation of Linear Models with Cross-Error Structure,” Journal of Econometrics, 2, 67–78.Google Scholar

Gardeazabal, J., and Vega-Bayo, A. (2016). “An Empirical Comparison between the Synthetic Control Method and Hsiao et al.'s Panel Data Approach to Program Evaluation. Journal of Applied Econometrics, DOI: 10.1002/jae.2557.Google Scholar

Gelfand, A.E., and Smith, A.F.M. (1990). “Sampling-Based Approaches to Calculating Marginal Densities,” Journal of the American Statistical Association, 85, 398–409.Google Scholar

Geweke, J. (1991). “Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints,” Computer Science and Statistics: Proceedings of the Twenty Third Symposium on the Interface, 571–578.Google Scholar

Girma, S. (2000). “A Quasi-differencing Approach to Dynamic Modelling from a Time Series of Independent Cross-Sections,” Journal of Econometrics, 98, 365–383.Google Scholar

Goldberger, A.S. (1972). “Maximum Likelihood Estimation of Regressions Containing Unobservable Independent Variables,” International Economic Review, 13, 1–15.Google Scholar

Goodman, L.A. (1961). “Statistical Methods for the Mover-Stayer Model,” Journal of the American Statistical Association, 56, 841–868.Google Scholar

Goodrich, R.L., and Caines, P.E. (1979). “Linear System Identification from Nonstationary Cross-Sectional Data,” IEEE Transaction on Automatic Control, AC-24, 403–411.Google Scholar

Gorseline, D.E. (1932). The Effect of Schooling upon Income, Bloomington: Indiana University Press.Google Scholar

Gourieroux, C., and Monfort, A. (1996). Simulation-Based Econometric Methods, Oxford: Oxford University Press.Google Scholar

Gourieroux, C., Monfort, A., and Trognon, A. (1984). “Pseudo Maximum Likelihood Methods Applications to Poisson Models,” Econometrica, 52, 701–720.Google Scholar

Graham, B.S. (2016). Homophily and Transitivity in Dynamic Network Formation (No. w22186).Google Scholar

Graham, B.S., and Powell, J.L. (2012). “Identification and Estimation of Average Partial Effects in ‘Irregular’ Correlated Random Coefficient Panel Data Models,” Econometrica, 80, 2105–2152.Google Scholar

Graybill, F.A. (1969). Introduction to Matrices with Applications in Statistics, Belmont: Wadsworth.Google Scholar

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

Granger, C.W.J., Maasoumi, E., and Racine, J.C. (2004). “A Dependence Metric for Possibly Nonlinear Processes,” Journal of Time Series Analysis, 25, 649–669.Google Scholar

Grassetti, L. (2011). “A Note on Transformed Likelihood Approach in Linear Dynamic Panel Models,” Statistical Methods and Applications, 20, 221–240.Google Scholar

Greenaway-McGrevy, R., Han, C., and Sul, D. (2012). “Asymptotic Distribution of Factor Augmented Estimators for Panel Regression,” Journal of Econometrics, 169, 48–53.Google Scholar

Griliches, Z. (1977). “Estimating the Returns to Schooling: Some Econometric Problems,” Econometrica, 45, 1–22.Google Scholar

Griliches, Z. (1979). “Sibling Models and Data in Economics: Beginning of a Survey,” Journal of Political Economy, 87 (Suppl. 2), S37–S64.Google Scholar

Griliches, Z., and Hausman, J. A. (1986). “Errors-in-Variables in Panel Data,” Journal of Econometrics, 31, 93–118.Google Scholar

Griliches, Z., Hall, B., and Hausman, J.A. (1978). “Missing Data and Self-selection in Large Panels,” Annales de l’INSEE, 30–31, 137–176.Google Scholar

Groen, J.J.J., and Kleibergen, F. (2003). “Likelihood-Based Cointegration Analysis in Panels of Vector Error-Correction Models,” Journal of Business and Economic Statistics, 21, 295–318.Google Scholar

Gronau, R. (1976). “The Allocation of Time of Israeli Women,” Journal of Political Economy, 84, 4, Part II.Google Scholar

Grunfeld, Y. (1958). “The Determinants of Corporate Investment,” unpublished Ph.D. thesis, University of Chicago.Google Scholar

Grunfeld, Y., and Griliches, Z. (1960). “Is Aggregation Necessarily Bad?” The Review of Economics and Statistics, 42, 1–13.Google Scholar

Gupta, A.K., and Varga, T. (1993). Elliptically Contour Models in Statistics, Kluwer.Google Scholar

Hadri, K. (2000). “Testing for Stationarity in Heterogeneous Panel Data,” Econometrics Journal, 3, 148–161.Google Scholar

Hadri, K., and Larsson, R. (2005). “Testing for Stationarity in Heterogeneous Panel Data Where the Time Dimension Is Fixed,” Econometric Journal, 8, 55–69.Google Scholar

Hahn, J. (1999). “How Informative Is the Initial Condition in a Dynamic Panel Model with Fixed Effects?” Journal of Econometrics, 93, 309–326.Google Scholar

Hahn, J., and Kuersteiner, G. (2002). “Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects When Both n and T Are Large,” Econometrica, 70, 1639–1657.Google Scholar

Hahn, J., and Kuersteiner, G. (2011). “Bias Reduction in Dynamic Nonnlinear Panel Data Models,” Econometric Theory, 27, 1152–1191.Google Scholar

Hahn, J., and Moon, H.R. (2006). “Reducing Bias of MLE in a Dynamic Panel Model,” Econometric Theory, 22, 499–512.Google Scholar

Hahn, J., and Newey, W. (2004). “Jackknife and Analytical Bias Reduction for Nonlinear Panel Models,” Econometrica, 72, 1295–1319.Google Scholar

Hahn, J., Hausman, J., and Kuersteiner, G. (2007). “Long Difference Instrumental Variable Estimation for Dynamic Models with Fixed Effects,” Journal of Econometrics, 140, 574–617.Google Scholar

Hall, P., Fisher, N.I., and Hoffman, B. (1992). “On the Nonparametric Estimation of Covariance Functions,” working paper, Australian National University.Google Scholar

Han, A., and Hausman, J.A. (1990). “Flexible Parametric Estimation of Duration and Competing Risk Models,” Journal of Applied Econometrics, 5, 1–28.Google Scholar

Han, C., and Phillips, P.C.B. (2013). “First Difference Maximum Likelihood and Dynamic Panel Estimation,” Journal of Econoemtrics, 175, 35–45.Google Scholar

Han, C., Phillips, P.C.B., and Sul, D. (2017). “Lag Length Selection in Panel Autoregression,” Econometric Reviews, 36(1–3), 225–240, DOI:10.1080/07474938.2015.1114313.Google Scholar

Han, X., Hsieh, C., and Ko, S. (2019). “Spatial Modeling Approach for Dynamic Network Formation and Interaction,” Journal of Business and Economic Statistics, DOI:1080/07350015.2019.1639395.Google Scholar

Hansen, B. (1982). “Efficient Estimation and Testing of Cointegrating Vectors in the Presence of Deterministic Trends,” Journal of Econometrics, 53, 87–121.Google Scholar

Hajivassiliou, V. (1990). “Smooth Simulation Estimation of Panel Data LDV Models,” mimeo, Yale University.Google Scholar

Härdle, W. (1990). Applied Nonparametric Regression, Econometric Society Monograph Series, 19, Cambridge: Cambridge University Press.Google Scholar

Harris, R.D.F., and Tzavalis, E. (1999). “Inference for Unit Roots in Dynamic Panels Where the Time Dimension Is Fixed,” Journal of Econometrics, 91, 201–226.Google Scholar

Hartley, H.O., and Rao, J.N.K. (1967). “Maximum Likelihood Estimation for the Mixed Analysis of Variance Model,” Biometrika, 54, 93–108.Google Scholar

Harvey, A.C. (1978). “The Estimation of Time-Varying Parameters from Panel Data,” Annales de l’INSEE, 30–31, 203–206.Google Scholar

Harvey, A.C., and Phillips, G.D.A. (1982). “The Estimation of Regression Models with Time-Varying Parameters,” in Games, Economic Dynamics, and Time Series Analysis, edited by Deistler, M., Fürst, E., and Schwödiauer, G.S., pp. 306–321, Cambridge: Physica-Verlag.Google Scholar

Hausman, J.A. (1978). “Specification Tests in Econometrics,” Econometrica, 46, 1251–1271.Google Scholar

Hausman, J.A., and McFadden, D. (1984). “Specification Tests for the Multinominal Logit Models,” Econometrica, 52, 1219–1240.Google Scholar

Hausman, J.A., and Taylor, W.E. (1981). “Panel Data and Unobservable Individual Effects,” Econometrica, 49, 1377–1398.Google Scholar

Hausman, J.A., and Wise, D. (1977). “Social Experimentation, Truncated Distributions, and Efficient Estimation,” Econometrica, 45, 919–938.Google Scholar

Hausman, J.A., and Wise, D. (1978). “A Conditional Probit Model for Qualitative Choice: Discrete Decisions Recognizing Interdependence and Heterogeneous Preferences,” Econometrica, 46, 403–426.Google Scholar

Hausman, J.A., and Wise, D. (1979). “Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment,” Econometrica, 47, 455–473.Google Scholar

Hayashi, F. (1982). “Tobin's Marginal q and Average q: A Neoclassical Interpretation,” Econometrica, 50, 213–224.Google Scholar

Hayashi, K., and Sen, P.K. (1998). “On Covariance Estimators of Factor Loadings in Factor Analysis,” Journal of Multivariate Analysis, 66, 38–45.Google Scholar

Heckman, J.J. (1976). “The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models,” Annals of Economic and Social Measurement, 5, 475–492.Google Scholar

Heckman, J.J. (1978). “Simple Statistical Models for Discrete Panel Data Developed and Applied to Test the Hypothesis of True State Dependence Against the Hypothesis of Spurious State Dependence,” Annales e l’INSEE, 30–31, 227–269.Google Scholar

Heckman, J.J. (1979). “Sample Selection Bias as a Specification Error,” Econometrica, 47, 153–61.Google Scholar

Heckman, J.J. (1981a). “Statistical Models for Discrete Panel Data,” in Structural Analysis of Discrete Data with Econometric Applications, edited by Manski, C.F. and McFadden, D., pp. 114–178, Cambridge: MIT Press..Google Scholar

Heckman, J.J. (1981b). “The Incidental Parameters Problem and the Problem of Initial Conditions in Estimating a Discrete Time-Discrete Data Stochastic Process,” in Structural Analysis of Discrete Data with Econometric Applications, edited by Manski, C.F. and McFadden, D., pp. 179–195, Cambridge: MIT Press..Google Scholar

Heckman, J.J. (1981c). “Heterogeneity and State Dependence,” in Studies in Labor Markets, edited by Rosen, S., pp. 91–139, Chicago: University of Chicago Press.Google Scholar

Heckman, J.J. (1997). “Constructing Counterfactuals Under Different Assumptions,” mimeo, University of Chicago.Google Scholar

Heckman, J.J., and Robb, R. (1985). “Alternative Methods for Evaluating the Impact of Interventions,” in Longitudinal Analysis of Labor Market Data, edited by Heckman, J. and Singer, B., New York: Cambridge University Press.Google Scholar

Heckman, J.J., and Singer, B. (1984). “Econometric Duration Analysis,” Journal of Econometrics, 24, 63–132.Google Scholar

Heckman, J.J., and Vytlacil, E. (1998). “Instrumental Variables Methods for the Correlated Random Coefficient Model: Estimating the Average Rate of Return to Schooling When the Return Is Correlated with Schooling,” Journal of Human Resources, 33, 974–987.Google Scholar

Heckman, J.J., and Vytlacil, E. (2001). “Local Instrumental Variables,” in Nonlinear Statistical Inference, edited by Hsiao, C., Morimune, K., and Powell, J.L, New York: Cambridge University Press, 1–46.Google Scholar

Heckman, J.J., and Vytlacil, E. (2005). “Structural Equations, Treatment Effects and Economic Policy Evaluation,” Econometrica, 69, 669–738.Google Scholar

Heckman, J.J., and Vytlacil, E. (2007). “Econometric Evaluation of Social Programs,” in Handbook of Econometrics, vol. 6B, Amsterdam: North-Holland.Google Scholar

Heckman, J.J., and Willis, R. (1977). “A Beta-Logistic Model for the Analysis of Sequential Labor Force Participation by Married Women,” Journal of Political Economy, 85, 27–58.Google Scholar

Heckman, J.J., Schmierer, D.A., and Urzua, S.S. (2010). “Testing the Correalted Random Coefficient Model,” Journal of Econometrics, 158, 177–203.Google Scholar

Heckman, J.J., Urzua, S., and Vytlacil, E. (2006). “Understanding Instrumental Variables in Models with Essential Heterogeneity,” The Review of Economics and Statistics, 88, 389–432.Google Scholar

Henderson, C.R. Jr. (1971). “Comment on ‘The Use of Error Components Models in Combining Cross-Section with Time Series Data,” Econometrica, 39, 397–401.Google Scholar

Henderson, D.J., and Ullah, A. (2008). “Nonparametric Estimation in a One-Way Error Component Model: A Monte Carlo Analysis,” mimeo.Google Scholar

Hendricks, W., Koenker, R., and Poirier, D.J. (1979). “Residential Demand for Electricity: An Econometric Approach,” Journal of Econometrics, 9, 33–57.Google Scholar

Hildreth, C., and Houck, J.P. (1968). “Some Estimators for a Linear Model with Random Coefficients,” Journal of the American Statistical Association, 63, 584–595.Google Scholar

Hirano, K., Imbens, G.W., Ridder, G., and Rubin, D.B. (2001). “Combining Panel Data Sets with Attrition and Refreshment Samples,” Econometrica, 69, 1645–1660.Google Scholar

Hoch, I. (1962). “Estimation of Production Function Parameters Combining Time-Series and Cross-Section Data,” Econometrica, 30, 34–53.Google Scholar

Holly, A. (1982). “A Remark on Hausman's Specification Test,” Econometrica, 50, 749–759.Google Scholar

Holly, A., and Gardiol, L. (2000). “A Score Test for Individual Heteroscedasticity in a One-Way Error Components Model,” in Panel Data Econometrics, edited by Krishnakumkar, J. and Ronchetti, E., pp. 199–211, Amsterdam: North-Holland.Google Scholar

Holtz-Eakin, D., Newey, W., and Rosen, H.S. (1988). “Estimating Vector Autoregressions with Panel Data,” Econometrica, 56, 1371–1395.Google Scholar

Hood, W.C., and Koopmans, T.C. (eds) (1953). Studies in Econometric Method, Cowles Foundation Monographs 14, New York: Wiley.Google Scholar

Hong, Y., and Kao, C. (2004). “Wavelet-Based Testing for Serial Correlation of Unknown Form in Panel Models,” Econometrica, 72, 1519–1563.Google Scholar

Hong, Y., and White, H. (1995). “Consistent Specification Testing via Nonparametric Series Regression,” Econometrica, 63, 1133–1159.Google Scholar

Honoré, B.E. (1992). “Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects,” Econometrica, 60, 533–567.Google Scholar

Honoré, B.E. (1993). “Orthogonality Conditions for Tobit Models with Fixed Effects and Lagged Dependent Variables,” Journal of Econometrics, 59, 35–61.Google Scholar

Honoré, B.E., and Kesina, M. (2017). “Estimation of Some Nonlinear Panel Data Models with Both Time-Varying and Time-Invariant Explanatory Variables,” Journal of Business and Economic Statistics, 35, 543–558.Google Scholar

Honoré, B.E., and Kyriazidou, E. (2000). “Panel Data Discrete Choice Models with Lagged Dependent Variables,” Econometrica, 68, 839–874.Google Scholar

Honoré, B.E., and Tamer, E. (2006). “Bounds on Parameters in Panel Dynamic Discrete Choice Models,” Econometrica, 74, 611–629.Google Scholar

Horowitz, J.L. (1992). “A Smoothed Maximum Score Estimator for the Binary Response Model,” Econometrica, 60, 505–531.Google Scholar

Hotelling, H. (1931). “The Generalization of Student's Ratio,” Annals of Mathematical Statistics, 2, 360–378.Google Scholar

Hsiao, C. (1974a). “Statistical Inference for a Model with Both Random Cross-Sectional and Time Effects,” International Economic Review, 15, 12–30.Google Scholar

Hsiao, C. (1974b). “The Estimation of Labor Supply of Low Income Workers – Some Econometric Considerations,” Working Paper 970–1, The Urban Institute, Washington, DC.Google Scholar

Hsiao, C. (1975). “Some Estimation Methods for a Random Coefficients Model,” Econometrica, 43, 305–325.Google Scholar

Hsiao, C. (1976). “Regression Analysis with Limited Dependent Variable,” 1P-186, IBER and CRMS, University of California, Berkeley.Google Scholar

Hsiao, C. (1979a). “Causality Tests in Econometrics,” Journal of Economic Dynamics and Control, 1, 321–346.Google Scholar

Hsiao, C. (1979b). “Autoregressive Modelling of Canadian Money and Income Data,” Journal of the American Statistical Association, 74, 553–560.Google Scholar

Hsiao, C. (1979c). “Linear Regression Using Both Temporally Aggregated and Temporally Disaggregated Data,” Journal of Econometrics, 10, 243–252.Google Scholar

Hsiao, C. (1982). “Autoregressive Modelling and Causal Ordering of Economic Variables,” Journal of Economic Dynamics and Control, 4, 243–259.Google Scholar

Hsiao, C. (1983). “Identification,” in Handbook of Econometrics, vol. I, edited by Griliches, Z. and Intriligator, M., pp. 223–283, Amsterdam: North-Holland.Google Scholar

Hsiao, C. (1985a). “Benefits and Limitations of Panel Data,” Econometric Reviews, 4, 121–174.Google Scholar

Hsiao, C. (1985b). “Minimum Chi-Square,” in the Encyclopedia of Statistical Science, vol. 5, edited by Kotz, S. and Johnson, N., pp. 518–522, New York: Wiley.Google Scholar

Hsiao, C. (1989). “Consistent Estimation or Some Nonlinear Errors-in-Variables Models,” Journal of Econometrics, 41, 159–185.Google Scholar

Hsiao, C. (1991a). “A Mixed Fixed and Random Coefficients Framework for Pooling Cross-Section and Time Series Data,” paper presented at the Third Conference on Telecommunication Demand Analysis with Dynamic Regulation, Hilton Head, S Carolina, in New Development in Quantitative Economics, edited by Lee, J.W. and Zhang, S.Y., Beijing: Chinese Academic of Social Science.Google Scholar

Hsiao, C. (1991b). “Identification and Estimation of Latent Binary Choice Models Using Panel Data,” Review of Economic Studies, 58, 717–731.Google Scholar

Hsiao, C. (1992a). “Random Coefficients Models,” in The Econometrics of Panel Data, edited by Matyas, L. and Sevestre, P., Kluwer; 1st ed., pp. 223–241, 2nd ed. (1996), pp. 410–428.Google Scholar

Hsiao, C. (1992b). “Logit and Probit Models,” in The Econometrics of Panel Data, edited by Matyas, L. and Sevestre, P., Dordrecht: Kluwer Academic Publishers, 223–241.Google Scholar

Hsiao, C. (1995). “Panel Analysis for Metric Data,” in Handbook of Statistical Modelling in the Social and Behavioral Sciences, edited by Arminger, G., Clogg, C.C., and Sobel, M.Z., pp. 361–400, New York: Plenum.Google Scholar

Hsiao, C. (2001). “Economic Panel Data,” in International Encyclopedia of the Social and Behavioral Sciences, edited by Snelser, N.J. and Bates, P.B., vol. 6, 4114–4121, Oxford: Elsevier.Google Scholar

Hsiao, C. (2007). “Panel Data Analysis-Advantages and Challenges,” TEST, 16, 1–22.Google Scholar

Hsiao, C. (2014). Analysis of Panel Data, 3rd edition, Cambridge University Press.Google Scholar

Hsiao, C. (2018). “Panel Models with Interactive Effects,” Journal of Econometrics 206, 645–673.Google Scholar

Hsiao, C. (2020a). “An Econometrician's Perspective on Big Data,” Advances in Econometrics, 41, 413–423.Google Scholar

Hsiao, C. (2020b). “Estimation of Fixed Effects Dynamic Panel Data Models: Linear Differencing or Conditional Expectation,” Econometric Reviews, 39, 858–874.Google Scholar

Hsiao, C., and Pesaran, M.H. (2008). “Random Coefficients Models,” in The Econometrics of Panel Data, 3rd ed., edited by Matayas, L. and Sevestre, P., pp. 187–216, Berlin: Springer.Google Scholar

Hsiao, C., and Sun, B.H. (2000). “To Pool or Not to Pool Panel Data,” in Panel Data Econometrics: Future Directions, Papers in Honor of Professor Pietro Balestra, edited by Krishnakumar, J. and Ronchetti, E., pp. 181–198, Amsterdam: North-Holland.Google Scholar

Hsiao, C., and Tahmiscioglu, A.K. (1997). “A Panel Analysis of Liquidity Constraints and Firm Investment,” Journal of the American Statistical Association, 92, 455–465.Google Scholar

Hsiao, C., and Tahmiscioglu, A.K. (2008). “Estimation of Dynamic Panel Data Models with Both Individual and Time Specific Effects,” Journal of Statistical Planning and Inference, 138, 2698–2721.Google Scholar

Hsiao, C., and Taylor, G. (1991). “Some Remarks on Measurement Errors and the Identification of Panel Data Models,” Statistica Neerlandica, 45, 187–194.Google Scholar

Hsiao, C., and Wan, S.K. (2014). “Is There an Optimal Forecast Combination?” Journal of Econometrics 178, 294–309.Google Scholar

Hsiao, C., and Wang, K.Q. (2000). “Estimation of Structural Nonlinear Errors-in-Variables Models by Simulated Least Squares Method,” International Economic Review, 41, 523–542.Google Scholar

Hsiao, C., and Zhang, J. (2015). “IV, GMM or Likelihood Approach to Estimate Dynamic Panel Models When Either N or T or Both Are Large,” Journal of Econometrics, 187, 312–322.Google Scholar

Hsiao, C., and Zhou, Q. (2015). “Statistical Inference for Panel Dynamic Simultaneous Equations Models,” Journal of Econometrics, 189, 383–396.Google Scholar

Hsiao, C., and Zhou, Q. (2018). “Incidental Parameters, Initial Conditions and Sample Size in Statistical Inference for Dynamic Panel Data Models,” Journal of Econometrics, 207, 114–128.Google Scholar

Hsiao, C., and Zhou, Q. (2019). “Panel Parametric, Semi-parametric and Nonparametric Construction of Counterfactuals,” Journal of Applied Econometrics, 34, 463–481.Google Scholar

Hsiao, C., and Zhou, Q. (2020). “Estimation of Dynamic Panel Data Models with Interactive Effects: Pairwise or Quasi-differencing over Time,” mimeo.Google Scholar

Hsiao, C., and Zhou, Q. (2021). “Statistical Inference for the Low Dimensional Parameters of Linear Regression Models in the Presence of High Dimensional Data: An Orthogonal Projection Approach,” mimeo.Google Scholar

Hsiao, C., Appelbe, T.W., and Dineen, C.R. (1993). “A General Framework for Panel Data Analysis – with an Application to Canadian Customer Dialed Long Distance Service,” Journal of Econometrics, 59, 63–86.Google Scholar

Hsiao, C., Ching, H.S., and Wan, S. (2012). “A Panel Data Approach for Program Evaluation – Measuring the Benefits of Political and Economic Integration of Hong Kong with Mainland China,” Journal of Applied Econometrics, 27, 705–740.Google Scholar

Hsiao, C., Li, Q., Liang, Z., and Xie, W. (2019). “Panel Data Estimation for Correlated Random Coefficients Models,” Econometrics, 7(7).Google Scholar

Hsiao, C., Pesaran, M.H., and Pick, A. (2012). “Diagnostic Tests of Cross-Section Independence for Limited Dependent Variable Panel Data Models,” Oxford Bulletin of Economics and Statistics, 74, 253–277.Google Scholar

Hsiao, C., Pesaran, M.H., and Tahmiscioglu, A.K. (1999). “Bayes Estimation of Short-Run Coefficients in Dynamic Panel Data Models,” in Analysis of Panels and Limited Dependent Variables Models, edited by Hsiao, C., Lee, L.F., Lahiri, K., and Pesaran, M.H., pp. 268–296, Cambridge: Cambridge University Press.Google Scholar

Hsiao, C., Pesaran, M.H., and Tahmiscioglu, A.K. (2002). “Maximum Likelihood Estimation of Fixed Effects Dynamic Panel Data Models Covering Short Time Periods,” Journal of Econometrics, 109, 107–150.Google Scholar

Hsiao, C., Shen, Y., and Fujiki, H. (2005). “Aggregate vs. Disaggregate Data Analysis – A Paradox in the Estimation of Money Demand Function of Japan,” Journal of Applied Econometrics, 20, 579–601.Google Scholar

Hsiao, C., Shen, Y., and Zhou, Q. (2021). “Panel Data Approach for Measuring the Average Treatment Effects with Multiple Treated Units: To Aggregate or Not,” Advance in Econometrics, forthcoming.Google Scholar

Hsiao, C., Shi, Z., and Zhou, Q. (2021). “Transformed Estimation for Panel Interactive Effects Models,” Journal of Business and Economic Statistics (forthcoming).Google Scholar

Hsiao, C., Wang, L.Q., and Wang, K.Q. (1997). “Estimation of Nonlinear Errors-in-Variables Models – An Approximate Solution,” Statistical Papers, 38, 1–28.Google Scholar

Hsiao, C., Xie, Y., and Zhou, Q. (2021). “Factor Dimension Determination for Panel Interactive Effects Model: An Orthogonal Projection Approach,” Computational Statistics, 36, 1481–1497.Google Scholar

Hsiao, C., Li, Q., Liang, Z., and Xie, W. (2019). “Panel Data Estimation for Correlated Random Coefficients Models,” Econometrics, 7.Google Scholar

Hsiao, C., Mountain, D., Tsui, K.Y., and Chan, M.W.L. (1989). “Modeling Ontario Regional Electricity System Demand Using a Mixed Fixed and Random Coefficients Approach,” Regional Science and Urban Economics, 19, 567–587.Google Scholar

Hsiao, C., Nugent, J., Perrigne, I., and Qiu, J. (1998). “Shares versus Residual Claimant Contracts: The Case of Chinese TVEs,” Journal of Comparative Economics, 26, 317–337.Google Scholar

Hu, L. (2002). “Estimation of a Censored Dynamic Panel Data Model,” Econometrica, 70, 2499–2517.Google Scholar

Hurvich, C.M., and Tsai, C.L. (1989). “Regression and Time Series Model Selections in Small Samples,” Biometrika, 76, 297–307.Google Scholar

Hurwicz, L. (1950). “Systems with Nonadditive Disturbances,” in Statistical Inference in Dynamic Economic Models, edited by Koopmans, T.C., pp. 330–372, New York: Wiley.Google Scholar

Hyndman, R.J., Lee, A.J., and Wang, E. (2016). “Fast Computation of Reconciled Forecasts for Hierarchical and Grouped Time Series,” Computational Statistics and Data Analysis, 97, 16–32.Google Scholar

Hyslop, D. (1999). “State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women,” Econometrica, 52, 363–389.Google Scholar

Im, K.S., Lee, J., and Tieslau, M. (2005). “Panel LM Unit Root Tests with Level Shifts,” Oxford Bulletin of Economics and Statistics, 67, 393–419.Google Scholar

Im, K.S., Pesaran, M.H., and Shin, Y. (2003). “Testing for Unit Roots in Heterogeneous Panels,” Journal of Econometrics, 115, 53–74.Google Scholar

Imbens, G.W., and Angrist, J.D. (1994). “Identification and Estimation of Local Average Treatment Effects,” Econometrica, 62, 467–475.Google Scholar

Imbens, G.W., and Lemieux, T. (2008). “Regression Discontinuity Designs: A Guide to Practice,” Journal of Econometrics, 142, 615–635.Google Scholar

Inoue, A. (2008). “Efficient Estimation and Inference in Linear Pseudo-Panel Data Models,” Journal of Econometrics, 148, 449–466.Google Scholar

Intriligator, M.D., Bodkin, R.G., and Hsiao, C. (1996). Econometric Models, Techniques, and Applications, Upper Saddle River: Prentice-Hall.Google Scholar

Izan, H.Y. (1980). “To Pool or not to Pool? A Reexamination of Tobin's Food Demand Problem,” Journal of Econometrics, 13, 391–402.Google Scholar

Janz, N., Ebling, G., Gottshalk, S., and Niggemann, H. (2001). “The Mannheim Innovation Panels (MIP and MIP-S) of the Centre for European Economic Research (ZEW),” Schmollers Jahrbuch, 121, 123–129.Google Scholar

Jennrich, R.L., and Thayer, D.T. (1973). “A Note on Lawley's Formulas for Standard Errors in Maximum Likelihood Factor Analysis,” Psychometrica, 38, 571–580.Google Scholar

Jiang, B., Yang, Y., Gao, J., and Hsiao, C. (2020). “Recursive Estimation in Large Panel Data Models: Theory and Practice,” Journal of Econometrics, 224, 439–465.Google Scholar