ࡱ;   !"#$%&'(Root Entry FJCompObjbWordDocument DObjectPoolJJ 4@   FMicrosoft Word 6.0 Document MSWordDocWord.Document.6;  Oh+'0$ H l   D h(C:\MSOFFICE\WINWORD\TEMPLATE\NORMAL.DOTEC322 APPLIED ECONOMETRICSGateway 2000 Licensed UserGateway 2000 Licensed User@-ܥe3 e=, D(@@@@@@@DADADADADAXAnA(DA CGAA"AAAAAA8B:B:B:B+eBDBDBRCTCc C@AAAAA CA@@AAAAAA@A@A8B@&A@@@@@A8BAAEC322 APPLIED ECONOMETRICS Panel Data 2 READING LIST This is a full reading list, intended to include all the material to which the lectures refer. Handouts will detail which of the reading is most essential. Course Schedule 1. Dynamic panel data models 2. Selection bias 3. Measurement error 4. Combining different levels of aggregation 5. Pseudo panels Reading Baltagi (1995) is Baltagi, B (1995), The Econometrics of Panel Data, Wiley. Hsiao (1986) is Hsiao, C (1986), Analysis of Panel Data, Cambridge University Press. 1. Dynamic panel data models Baltagi (1995), ch.8 Dynamic panel data models. Hsiao (1986), ch.4 Dynamic models with variable intercepts. Bias in fixed effects estimator Baltagi (1995), sec.8.1 Introduction. - gives very brief overview Hsiao (1986), sec.4.2 Fixed-effects models. Nickell (1981), Biases in dynamic models with fixed effects, Econometrica, vol.49, 1417-26. or Ridder and Wansbeek (1990), Dynamic models for panel data, pp.557-82 in ed. van der Ploeg, Advanced Lectures in Quantitative Economics, North-Holland. [HH8500.A3] - derive the bias for large N, small T Kiviet (1995), On bias, inconsistency and efficiency of some estimators in dynamic panel data models, Journal of Econometrics, vol.68, 53-78. - shows magnitude of bias Beggs and Nerlove (1988), Biases in dynamic models with fixed effects, Economics Letters, vol.26, pp.29-31. - examine bias for small N, small T Balestra and Nerlove (1966), Pooling cross-section and time series data in the estimation of dynamic models: the demand for natural gas, Econometrica, vol.34, 585-612. - a famous application of OLS, LSDV and GLS to a dynamic model Proposed solutions Baltagi (1995), secs.8.1-8.5. - covers Anderson-Hsiao (briefly), Arellano-Bond GMM estimator, Ahn & Schmidts critiques, Keane & Runkle Hsiao (1986), sec.4.2. - goes through Anderson-Hsiao. Anderson and Hsiao (1981), Estimation of dynamic models with error components, Journal of the American Statistical Association, vol.76, 598-606. - the origin of the Anderson-Hsiao estimator, the idea of differencing then instrumenting lagged dependent variable with second lag of level or difference Arellano and Bond (1991), Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies, vol.58, 277-97. - the authors of the DPD (Dynamic Panel Data) regression package expound their view on how to instrument in the presence of lagged dependent variables Ahn and Schmidt (1995), Efficient estimation of models for dynamic panel data, Journal of Econometrics, vol.56, 955-71. - explain why Arellano-Bond is inefficient Bias with heterogeneous parameters, in the cases of stationarity and nonstationarity Baltagi (1995), sec.10.6.3 Stationary vs nonstationary regressors and parameter heterogeneity - concise survey Robertson and Symons (1992), Some strange properties of panel data estimators Journal of Applied Econometrics, vol.7, 175-89. - derive biases for large T, small N and small N, large T; Monte Carlo; implications for Anderson-Hsiao dynamic panel estimator Pesaran and Smith (1995), Estimating long-run relationships from dynamic heterogeneous panels, Journal of Econometrics, vol.68, 79-113. - compare estimation methods for long-T panels: aggregate time series, cross-section, pooled with fixed or random effects, average of individual time series Application: The demand for cigarettes Baltagi (1995), sec.8.6. Application: Employment equations for UK companies Arellano and Bond (1991) (see above). Application: Real wage determination in 13 OECD countries, 1958-86 Robertson and Symons (1992) (see above). Application: Labour demand across UK industries Pesaran and Smith (1995) (see above). Application: Multi-country analysis of growth and convergence Lee, Pesaran and Smith (1996), Growth and convergence: a multi country empirical analysis of the Solow growth model, mimeo (forthcoming Journal of Applied Econometrics). 2. Selection bias Baltagi (1995), sec.10.5 Selection bias in panel data. Also pp.6-7. Hsiao (1986), sec.8.3 Nonrandomly missing data. Heckman (1979), Sample selection bias as a specification error, Econometrica, vol.47, 153-61. Verbeek and Nijman (1992), Incomplete panels and selection bias, ch.13 in eds. Matyas and Sevestre, The Econometrics of Panel Data: handbook of theory and applications, Kluwer Academic Publishers. Nijman and Verbeek (1992), Nonresponse in panel data: the impact on estimates of a life cycle consumption function, Journal of Applied Econometrics, vol.7, 243-57. Verbeek (1990), On the estimation of a fixed effects model with selectivity bias, Economics Letters, vol.34, 267-70. Ridder (1992), An empirical evaluation of some models for non-random attrition in panel data, Structural Change and Economic Dynamics, vol.17, 77-84. Wooldridge (1995), Selection corrections for panel data models under conditional mean independence assumptions, Journal of Econometrics, vol.68, 115-32. Zabel (1992), Estimating fixed and random effects models with selectivity, Economics Letters, vol.40, 269-72. Application: The Gary income-maintenance experiment Hsiao (1986), sec.8.3.3 An example: attrition in the Gary income-maintenance experiment. Hausman and Wise (1979), Attrition bias in experimental and panel data: the Gary income maintenance experiment, Econometrica, vol.47, 455-73. Application: Cyclicality of wages and composition bias Solon, Bartsky and Parker (1994), Measuring the cyclicality of wages: how important is composition bias?, Quarterly Journal of Economics, vol.109, 1-25. 3. Measurement error Baltagi (1995), sec.10.1 Measurement error and panel data. Also p.6. Hsiao (1986), sec.3.9 Errors of measurement. Biorn (1992), Panel data with measurement errors, ch.8 in eds. Matyas and Sevestre, The Econometrics of Panel Data: handbook of theory and applications, Kluwer Academic Publishers. Jakubson (1986), Measurement error in binary explanatory variables in panel data models: why do cross-section and panel estimates of the union wage effect differ?, Princeton Industrial Relations Section Working Paper no.209. Pischke (1995), Measurement error and earnings dynamics: some estimates from the PSID validation study, Journal of TV and Economic Statistics, vol.13, 305-14. Bound and Krueger (1991), The extent of measurement error in longitudinal earnings data: do two wrongs make a right?, Journal of Labor Economics, vol.9, 1-24. Bound, Brown, Duncan and Rogers (1990), pp.1-19 in eds. Hartog, Ridder and Theeuwes, Measurement error in cross-sectional and longitudinal labor market surveys: validation study evidence, Panel Data and Labor Market Studies, North-Holland. Duncan and Hill (1985), An investigation of the extent and consequence of measurement error in labor economic survey data, Journal of Labor Economics, vol.3, 508-32. - validation study on PSID finds error variance in annual earnings is 15% of true variance, 37% for hours, 184% for average hourly earnings (for one-year recall; more than twice these for two-year recall). 4. Combining different levels of aggregation Imbens and Lancaster (1994), Combining micro and macro data in microeconometric models, Review of Economic Studies, 61, 655-80. Stoker (1985), Aggregation, structural change, and cross-section estimation, Journal of the American Statistical Association, 80, 720-29. Application: Individual wage equations with regional regressors Moulton (1990), An illustration of a pitfall in estimating the effects of aggregate variables on micro unit, Review of Economics and Statistics, 72 (2), 334-38. - illustrates the danger of spurious regression when different levels of aggregation are combined, estimating an individual-level wage equation including region-level regressors 5. Pseudo panels from repeated cross sections Baltagi (1995), sec.10.3 Pseudo-panels. Verbeek (1992), Pseudo panel data, ch.14 in eds. Matyas and Sevestre, The Econometrics of Panel Data: handbook of theory and applications, Kluwer Academic Publishers. Moffitt (1993), Identification and estimation of dynamic models with a time series of repeated cross-sections, Journal of Econometrics, vol.59, 99-123. Deaton (1985), Panel data from time series of cross-sections, Journal of Econometrics, vol.30, 109-26. Verbeek and Nijman (1992), Can cohort data be treated as genuine panel data?, Empirical Economics, vol.17, 9-23. Application: Occupational wage differentials in the UK Meghir and Whitehouse (1996), The evolution of wages in the United Kingdom: evidence form micro data, Journal of Labor Economics, vol.14, 1-25. 1. Identification (a) aggregate wage equations (b) effect of unions on wages 1. Identification Gujarati (1995), chs.18-20 (Simultaneous equation models, The identification problem and Simultaneous-equation methods) in Basic Econometrics, 3rd edition, McGraw-Hill. or Stewart and Wallis (1981), ch.4 The identification problem, in Introductory Econometrics, 2nd edition, Basil Blackwell. *Hsiao (1983), Identification, pp.223-83 in eds. Griliches and Intriligator, Handbook of Econometrics, vol.1, North-Holland. 1(a). Aggregate wage equations *Manning (1993), Wage bargaining and the Phillips Curve: the identification and specification of aggregate wage equations, Economic Journal, vol.103, no.416, 98-118. - the ultimate sceptical statement Henry and Lee (1996), Identification and estimation of wage and employment equations: an example of the structural VAR modelling approach, mimeo. - a defence of identifiability ?Westaway (1996), What determines the natural rate of unemployment? And what does not?, mimeo. - includes a restatement of the problem? 1(b). The effect of unions on wages Baltagi (1995), p.5. Hsiao (1986), pp.2-3. Application 1: The effects of trade unions ?Freeman and Medoff (1981), The impact of collective bargaining: illusion or reality, Harvard University mimeo. *Freeman (1984), Longitudinal analyses of the effects of trade unions, Journal of Labor Economics, vol.2, 1-26. Application 2: Labour supply *MaCurdy (1981), An empirical model of labor supply in a life cycle setting, Journal of Political Economy, vol.89, 1059-85. PAGE  Applied Econometrics - Panel Data 2  PAGE 6 Jennifer Smith - University of TV Applied Econometrics - Panel Data 2 Jennifer Smith - University of TV .A ࡱ; SummaryInformation(@@-@Microsoft Word 6.02ࡱ; ()*79pwx:+Hndu9 E X 3 L k % ? 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