FACULTAD DE CIENCIAS SOCIALES

SílaboSemestre 2012-II

1 Información General

Nombre del curso: Econometría 2Código del curso : ECO 330Carácter: ObligatorioCréditos: 5Número de horas de teoría: 4 (Martes de 6-8 pm y Jueves de 6-8 pm)Número de horas de práctica : 2 (Sábados 12-2 pm y Sábados 2-4 pm)Profesores del curso: Guillermo Moloche (guillermo.moloche@pucp.pe)Profesores del curso: Gabriel Rodríguez (gabriel.rodriguez@pucp.edu.pe)Horario: 0722

2 Sumilla

Ecuaciones simultáneas: identi�cación, estimación e inferencia. Aplicaciones. Series detiempo univariadas. Modelos AR, MA, ARMA y ARIMA: identi�cación, estimación ypredicción. Modelos dinámicos: modelos con variables dependientes rezagadas, modelos conretardos distribuidos, modelos de volatilidad estocástica (ARCH, GARCH). El Método de logeneral a lo especí�co. Series de tiempo multivariadas: modelos de vectores autoregresivos(VAR). Raíces unitarias, integración y cointegración. Modelo de corrección de errores.Análisis de datos de corte transversal y de Panel Data. Modelos de efectos �jos y deefectos aleatorios. Modelos Probit, Logit y Tobit. Datos censurados y truncados. Sesgo deselección.

3 Objetivos

Los objetivos del curso son los siguientes: 1) ofrecer a los estudiantes los elementos teórico-prácticos sobre recientes tópicos desarrollados en la literatura econométrica; 2) contribuiral analisis empírico en economía. En lo posible, dichas aplicaciones serán hechas para elcaso peruano; 3) uso del programa econométrico Eviews e introducción al uso de programaseconométricos como Gauss, JMulti.

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4 Requisitos del Curso

El curso supone que los estudiantes han seguido y aprobado satisfactoriamente el curso deEconometria 1 o los respectivos cursos que se juzguen equivalentes. El desarrollo satisfac-torio del curso supone conocimientos fundamentales de estadística, cálculo matemático ymanipulación de matrices. Una rápida revisión de algunos conceptos será hecha cuando seanecesario. En general, una revision del apéndice matemático de algún libro de econometríacomo Greene (2005), Hamilton (1994) o Wooldridge (2000) es aconsejable.

5 Evaluación

La evaluación del curso se basa en tres elementos: prácticas cali�cadas, un examen parcialy un examen �nal. Dado el carácter práctico del curso, habrán 4 prácticas cali�cadas. Dosprácticas cali�cadas serán realizadas en el horario respectivo y las otras dos prácticas seránrealizadas en grupos en un plazo de dos semanas. Ninguna de las prácticas será anulada.Los grupos serán formados en las primeras semanas de clases. Las ponderaciones son lassiguientes:

1. Prácticas Cali�cadas (4): 40%

2. Examen Parcial: 30% (Fecha: Sábado 13 de Octubre 2012, 11:00 am)

3. Examen Final: 30% (fecha: Sábado 15 de Diciembre 2012, 11:00 am)

6 Computador

Unos de los objetivos del curso es el analisis empírico univariado y/o multivariado deseries económicas. En este sentido, el uso del computador es un elemento importanteen el desarrollo del curso. En general, haremos uso del programa econométrico Eviewsaunque veremos a nivel introductorio algunos ejemplos utilizando el programa Gauss. De-bido a las restricciones de tiempo, es imposible dedicar mucho tiempo al aprendizaje dedicho programa. Por tal motivo, el Profesor distribuirá programas ya existentes paralas respectivas aplicaciones que se indiquen en clases. Se recomienda leer alguna guíaintroductoria o práctica relacionada con los programas Eviews y Gauss. La direcciónhttp://faculty.washington.edu/ezivot contiene algunas direcciones que pueden ser útiles aeste respecto. Es bueno mencionar que el programa JMulti es gratuito y se recomienda quelos estudiantes instalen dicho programa en sus respectivas computadoras. Este programapuede ser obtenido gratuitamente entrando a la página web del Profesor Helmut Lütkepohl.Otro programa gratuito recomendable para el análisis multivariado es denominado SVAR.

7 Contenido

1. Introducción

2. Revisión del Modelo Clásico Múltiple de Regresión Lineal (Estimación, Inferencia,Propiedades).

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3. Teoría Asintótica y el Modelo Clásico Múltiple de Regresión Lineal.

4. Modelos ARMA

5. Raíces Unitarias

6. Forma Espacio-Estado, Filtro de Kalman y Descomposición de Tendencia y Ciclo

7. Análisis Multivariado (Vectores AutoRegresivos-VAR)

8. VAR y Cointegración

9. Volatilidad: Modelos ARCH, GARCH y otros

10. Modelos con Variables Dependientes Limitadas

11. Econometría de Datos en Panel

8 Referencias

La econometría ha evolucionado de manera importante en los últimos 15-20 años y conse-cuentemente debemos hacer algunas priorizaciones debido al corto tiempo del curso. Unalista de referencias (no exhaustiva) es otorgada con la �nalidad de completar detalles o pro-fundizar en ciertos temas de mayor interés del estudiante. Ningún libro es obligatorio comomanual del curso. Sin embargo, el material dictado en las clases teóricas y prácticas es elmaterial fundamental para la comprensión y el éxito del curso. A continuación se presentauna lista de referencias (libros y papers). Es necesario notar que la lista de papers incluyeaplicaciones empíricas en la mayoría de los casos.

8.1 Libros

1. Anderson, T. W. (1971), The Statistical Analysis of Time Series, John Wiley & Sons.

2. Arellano, M. 2003), Panel-Data Econometrics, Advanced Texts in Econometrics, Ox-ford: Oxford University Press.

3. Baltagi, B. H. (1995), Econometric Analysis of Panel Data, John Wiley & Sons Ltd.

4. Banerjee, A., J. J. Dolado, J. W. Galbraith and D. F. Hendry (1993), Cointegra-tion, Error Correction and the Econometric Analysis of Non Stationary Data, OxfordUniversity Press.

5. Brooks, C. (2008), Introductory Econometrics for Finance, Second Edition, Cam-bridge University Press.

6. Cameron A. C. y P. K. Trivedi (2005), Microeconometrics. Methods and Applications,Cambridge: Cambridge University Press.

7. Davidson, R. and J. G. MacKinnon (1993), Estimation and Inference in Econometrics,Oxford University Press.

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8. Enders, W. (2004), Applied Econometric Time Series, John Wiley Second Edition.

9. Franses, P. H. (1999), Time Series Models for Business and Economic Forecasting,Cambridge University Press.

10. Granger, C. W. J. and T. D. Teräsvirta (1993), Modelling Nonlinear Economic Rela-tionships, Advanced Texts in Econometrics, Oxford: Oxford University Press.

11. Greene, W. (2007), Econometric Analysis, 6th ed., Prentice-Hall.

12. Greene, W. H., Hensher, D. A. (2010),Modeling ordered choices: A primer, CambridgeUniversity Press.

13. Hamilton, J. D. (1994), Time Series Analysis, Princeton University Press.

14. Harvey, A. C. (1981), Time Series Models, MIT Press.

15. Harvey, A. C. (1999), Forecasting, Structural Time Series Models and the KalmanFilter, Cambridge University Press.

16. Hatanaka, M. (1998), Time Series-Based Econometrics, Oxford University Press.

17. Hendry, D. F. (1997), Dynamic Econometrics, Oxford University Press.

18. Hsiao, Ch. (2003), Analysis of Panel Data, 2da Edición, Cambridge: CambridgeUniversiy Press.

19. Johansen, S. (1999), Likelihood-Based Inference in Cointegrated Vector AutoregressiveModels, Oxford University Press.

20. Juselius, K. (2006), The Cointegrated VAR Model: Methodology and Applications,Oxford University Press

21. Kim, Ch.-J. and Ch. R. Nelson (1999), State-Space Models with Regime Switching,MIT Press.

22. Long, J. S. (1997), Regression Models for Categorical and Limited Dependent Vari-ables, Advanced Quantitative Techniques in the Social Sciences Series 7, Sage Publi-cations.

23. Long, J. S. y J. Freese (2006), Regression Models for Categorical Dependent VariablesUsing Stata, 2da Edición, Stata Press.

24. Maddala, G. S. (1983), Limited-Dependent and Qualitative Variables in Econometrics,Cambridge: Cambridge University Press.

25. Maddala, G. S. and I. M. Kim (1998), Unit Roots, Cointegration and StructuralChange, Cambridge University Press.

26. Mills, T. C. (1990), Time Series Techniques for Economists, Cambridge UniversityPress.

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27. Mills, T. C. (1993), The Econometric Modelling of Financial Time Series, CambridgeUniversity Press.

28. Winkelmann, R. (2008), Econometric Analysis of Count Data, 5ta Edición, Springer.

29. Winkelmann, R. and Boes, S. (2006), Analysis of Microdata, Springer.

30. Wooldridge, J. M. (2000), Introduction to Econometrics, A Modern Approach, South-Western College Publishing, Thompson Learning.

8.2 Papers

8.2.1 Tests de Raiz Unitaria

1. Banerjee, A., R. Lunsdaine, and J. H. Stock (1992), �Recursive and Sequential Testsof the Unit Root and Trend Break Hypothesis,� Journal of Business and EconomicStatistics 10, 271-288.

2. Campbell, J. Y. and P. Perron (1991), �Pitfalls and Opportunities: What Macro-economists Should Know About Unit Roots,� in NBER Macroeconomics Annual, O.J. Blachard and S. Fisher, Editors, Vol. 6, 141-201.

3. Christiano, L. (1992), �Searching for Breaks in GNP,�Journal of Business and Eco-nomic Statistics 10, 237-250.

4. Elliott , G., T. J. Rothenberg and J. H. Stock (1996), �E¢ cient Tests for an Autore-gressive Unit Root,�Econometrica 64, 813-836.

5. Niels Haldrup, and Morten Ørregaard Nielsen, 2007, �Estimation of Fractional Inte-gration in the Presence of Data Noise�, Computational Statistics and Data Analysis51, 3100-3114.

6. Kwiatkowski, D., P. C. B. Phillips, P. Schmidt, and Y. Shin (1992), �Testing the NullHypothesis of Stationarity against the Alternative of a Unit Root: How sure are wethat economic time series have a unit root,�Journal of Econometrics 54, 159-178.

7. Nelson, C. R. and C. I. Plosser (1982), �Trends and Random Walks in MacroeconomicTime Series: Some Evidence and Implications,�Journal of Monetary Economics 10,139-162.

8. Ng, S. and P. Perron (1995), �Unit Root tests in ARMA Models with Data DependentMethods for the Selection of the truncation Lag,�Journal of the American StatisticalAssociation 90, 268-281.

9. Ng, S. and Perron, P. (2001), � Lag Length Selection and the Construction of UnitRoot Tests with Good Size and Power,�Econometrica 69, 1519-1554.

10. Perron, P. (1989), �The Great Crash, the Oil Price Shock and the Unit Root Hypoth-esis,�Econometrica 57, 1361-1401.

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11. Perron, P. (1990), �Testing for a Unit Root in a Time Series with a Changing Mean,�Journal of Business and Economic Statistics 8, 153-162.

12. Perron, P. (1994), �Trend, Unit Root and Structural Change in Macroeconomic TimeSeries,� in Cointegration for the Applied Economist, B. B. Rao (Editor), MacmillanPress, 113-146.

13. Perron, P.(1997), �Further Evidence on Breaking Trend Functions in MacroeconomicVariables,�Journal of Econometrics 80, 355-385.

14. Perron, P. and S. Ng (1996), �Useful Modi�cations to Some Unit Root Tests with De-pendent Errors and their Local Asymptotic Properties,�Review of Economic Studies63, 435-463.

15. Perron, P. and G. Rodríguez (2003), �E¢ cient Unit Root Tests and Structural Change,�Journal of Econometrics 115, 1-27.

16. Perron, P. and G. Rodríguez (2003), �Searching for Additive Outliers in Nonstation-arity Time Series,�Journal of Time Series Analysis, 24(2), 193-220.

17. Perron, P. and T. Vogelsang (1992), �Nonstationarity and Level Shifts with an Ap-plication to Purchasing Power Parity,�Journal of Business and Economic Statistics12, 471-478.

18. Phillips, P. C. B. and P. Perron (1988), �Testing for a Unit Root in Time SeriesRegression,�Biometrika 75, 335-346.

19. Phillips, P. C. B. and Z. Xiao (1998), �A Primer on Unit Roots,�Journal of EconomicSurveys, 12 (5), 423469.

20. Rodríguez, G. (2004), �An Empirical Note about Additive Outliers in Latin AmericanIn�ation Series,�Empirical Economics 29 (2), 361-372.

21. Said, S. E. and D. A. Dickey (1984), �Testing for Unit Root in Autoregressive-MovingAverage Models of Unknown Order,�Biometrika 71, 599-607.

22. Stock, J. H. (1994), �Unit Roots and Trend Breaks,� in Handbook of Econometrics,Vol. 4, R. F. Engle and D. MacFaden, Editors, Elsevier.

23. Vogelsang, T. J. (1999), �Two Simple Procedures for Testing for a Unit Root whenthere are Additive Outliers,�Journal of Time Series Analysis 20, 237-252.

24. Zivot, E. and D. W. Andrews (1992), �Furhter Evidence on the Great Crash, theOil Price Shock and the Unit Root Hypothesis,�Journal of Business and EconomicStatistics 10, 251-270.

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8.2.2 Forma Espacio Estado, Filtro de Kalman y Descomposición de Tendenciay Ciclo

1. Beveridge, S. and C. R. Nelson (1981), �A New Approach to Decomposition of Eco-nomic Time Series into Permanent and Transitory Components with particular at-tention to measurement of the business cycle,� Journal of Monetary Economics 7,151-174.

2. Clark, P. K. (1987), �The Cyclical Component of U.S. Economic Activity,�QuaterlyJournal of Economics 102, 798-814.

3. Engle, R. F. and M. W. Watson (1987), �The Kalman Filter: Applications to Fore-casting and rational Expectations Models,� In Advances in Econometrics, Vol. 1,Fifth World Congress, T. F. Bewley (Editor), Econometric Society Monograph # 13,245-285 (more references in this paper).

4. Harvey, A. C. (1987), �Applications of the Kalman Filter in Econometrics,� in Ad-vances in Econometrics, Vol. 1, T. F. Bewley (Editor), Econometric Society Mono-graph # 13, 285-313.

5. Rodríguez, G. (2005), �Estimates of Permanent and Transitory Components for Cana-dian Regions using the Friedman�s Plucking Model of Business Fluctuations,�Cana-dian Journal of Regional Science 27 (1), 61-78.

6. Kim, C.-J. and C. R. Nelson (1999), �Friedman�s Plucking Model of Business Fluctu-ations: Tests and Estimates of Permanent and Transitory Components,� Journal ofMoney, Credit and Banking 31, 317-334.

7. Mills, T. C. and P. Wang (2002), �Plucking Models of Business Cycle Fluctuations:Evidence from the G-7 Countries,�Empirical Economics 25, 225-276.

8.2.3 Modelos VAR

1. Blanchard, O. J. and D. Quah (1989), �The Dynamic E¤ects of Aggregate Demandand Supply Disturbances,�American Economic Review 79, 655-673.

2. Bernanke, B. (1986), �Alternative Explanations of the Money-Income Correlation,�Carnegie Rochester Conference Series on Public Policy 25, 45-49.

3. Galí, J. (1992), �How well does the IS-LM Model Fit Postwar Data?,�Quaterly Jour-nal of Economics 107, 709-735.

4. Lutkepohl, H. (1999), �Vector Autoregression,�Unpublished manuscript, Institut forStatistik und Okonometrie, Humboldt-Universitat Zu Berlin.

5. Watson, M. W. (1994), �Vector Autoregression and Cointegration,� in Handbook ofEconometrics, Vol. 4, R. F. Engle and D. MacFaden, Editors, Elsevier.

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8.2.4 VAR y Cointegracion

1. Engle, R. F. and C. W. J. Granger (1987), �Co-Integration and Error Correction:Representation, Estimation and Testing,�Econometrica 55, 251-276.

2. Granger, C. W. J. and P. Newbold (1974), �Spurious Regression in Econometrics,�Journal of Econometrics 2, 111-120.

3. Hansen, B. E. (1992), �E¢ cient Estimation and Testing of Cointegration Vectors inthe Presence of Deterministic Trends,�Journal of Econometrics 53, 87-121.

4. Hubrich, K., H. Lütkepohl and P. Saikkonen (1998), �A Review of Systems Coin-tegration Tests,� Unpublished manuscript, Institut for Statistik und Okonometrie,Humboldt-Universitat Zu Berlin.

5. Johansen, S. (1988), �Statistical Analysis of Cointegration Vectors,�Journal of Eco-nomics, Dynamics and Control 12, 231-254.

6. Johansen, S. (1991), �Estimation and Hypothesis Testing of Cointegration Vectors inGaussian Vector Autoregressive Models,�Econometrica 59, 87-121.

7. Johansen, S. and K. Juselius (1990), �Maximum Likelihood Estimation and Inferenceon Cointegration with an Application to the Demand for Money,�Oxford Bulletin ofEconomics and Statistics 52, 169-210.

8. Johansen, S. and K. Juselius (1992), �Testing Structural Hypotheses in a MultivariateCointegration Analysis of the PPP and the UIP for UK,� Journal of Econometrics53, 221-244.

9. King, R., C. I. Plosser, J. H. Stock and M. W. Watson (1991), �Stochastic Trendsand Economic Fluctuations,�American Economic Review 81, 819-840.

10. Perron, P. and G. Rodríguez. (2002), �Residual-Based Tests for Cointegration withGLS Detrended Data,�manuscript.

11. Phillips, P. C. B. and S. Ouliaris (1990), �Asymptotic Properties of Residual BasedTests for Cointegration,�Econometrica 58, 165-193.

12. Stock, J. H. (1987), �Asymptotic Properties of Least Squares Estimates of Cointegra-tion Vectors,�Econometrica 55, 1035-1056.

13. Stock, J. H. (1999): �A Class of Tests for Integration and Cointegration,� in Engle,R.F. and H. White (eds.), Cointegration, Causality and Forecasting. A Festschrift inHonour of Clive W.J. Granger, Oxford University Press, 137-167.

14. Stock, J. H. and M. W. Watson (1989), �Testing for Common Trends,�Journal of theAmerican Statistical Association 83, 1097-1107.

15. Sims, C. A., J. H. Stock and M. W. Watson (1990), �Inference in Linear Time SeriesModels with some Unit Roots,�Econometrica 58, 113-144.

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16. Watson, M. W. (1994), �Vector Autoregression and Cointegration,� in Handbook ofEconometrics, Vol. 4, R. F. Engle and D. MacFaden, Editors, Elsevier.

8.2.5 Volatilidad: Modelos ARCH, GARCH y otros

1. Andersen, T.G., and Bollerslev T., (1998), �Answering the skeptics: yes, standardvolatility models do provide accurate forecasts,� International Economic Review 39,885-905.

2. Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2001a), �The dis-tribution of realized exchange rate volatility,� Journal of the American StatisticalAssociation 96, 42-55.

3. Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2001b), �The distrib-ution of realized stock return volatility,�Journal of Financial Economics 61, 43-76.

4. Bollerslev, T. (1986), �Generalized Autoregressive Conditional Heteroskedasticity,�Journal of Econometrics 31(3), 307-27.

5. Bollerslev, T. and Wooldridge J.M. (1992), �Quasi-Maximum Likelihood Estimationand Inference in Dynamic Models with Time-Varying Covariances,�Econometric Re-views 11(2), 143-72.

6. Engle, R. F. (1982), �Autoregressive Conditional Heteroskedasticity with Estimatesof the Variance of United Kingdom In�ation,�Econometrica 50(4), 987-1007.

7. Engle, R.F. and Lee, G.G. J. (1999), �A Permanent and Transitory Component Modelof Stock Return Volatility,�in Cointegration, Causality, and Forecasting: A Festschriftin Honour of Clive W. J. Granger, Robert F. Engle and Halbert White, eds. Oxford:Oxford University Press, 475-97.

8. Engle, R. F. and Joseph Mezrich. (1996), �GARCH for Groups,�RISK 9(8), 36-40.

9. Engle, R. F. and Victor Ng. (1993), �Measuring and Testing the Impact of News onVolatility,�Journal of Finance 48(5), 1749-78.

10. Engle, R., Takatoshi Ito and Wen-Ling Lin. (1990), �Meteor Showers or Heat Waves?Heteroskedastic Intra-Daily Volatility in the Foreign Exchange Market,�Econometrica58(3), 525-42.

11. Engle, R., Ng V. and Rothschild M. (1992), �A Multi-Dynamic Factor Model for StockReturns,�Journal of Econometrics 52(12), 245-66.

12. Garman, M. B. and Klass, M. J. (1980), �On the estimation of security price volatilitiesfrom historical data,�Journal of Business 53, 67-78.

13. Glosten, L. R., Jagannathan R. and Runkle D.E. (1993), �On the Relation Betweenthe Expected Value and the Volatility of the Nominal Excess Returns on Stocks,�Journal of Finance 48(5), 1779-801.

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14. Hansen, P., and Lunde A. (2005), �A forecast comparison of volatility models: Doesanything beat a GARCH(1,1)?,�Journal of Applied Econometrics 20, 873-89.

15. Nelson, D. B. (1990), �Stationarity and persistence in the GARCH(1,1) model,�Econometric Theory 6, 318-334.

16. Nelson, D. B. (1991), �Conditional Heteroscedasticity in Asset Returns: A New Ap-proach,�Econometrica 59(2), 347-70.

17. Pagan, A., and Schwert W. (1990), �Alternative models for conditional stock volatil-ity,�Journal of Econometrics 45, 267-90.

18. Rabemananjara, R. and Zakoian J. M. (1993), �Threshold Arch Models and Asym-metries in Volatility,�Journal of Applied Econometrics 8(1), 31-49.

19. Rogers, L. C. G. and Satchell, S. E. (1991), �Estimating variance from high, low andclosing prices.�Annals of Applied Probability 1, 504-512.

20. West, K., and Cho D. (1995), �The predictive ability of several models of exchangerate volatility,�Journal of Econometrics 69, 367-91.

21. Yang, D. and Zhang, Q. (2000), �Drift-independent volatility estimation based onhigh, low, open, and close prices,�Journal of Business 73, 477-491.

22. Zakoian, J. M. (1994), �Threshold heteroscedastic models,�Journal of Economic Dy-namics and Control 18, 931-955.

8.2.6 Modelos con Variables Dependientes Limitadas

1. Atallah, G., and G. Rodríguez (2006), �Indirect Patent Citations,� Scientometrics67(3) 437-465.

2. Cameron, A. C., Trivedi, P. K. (1986), �Econometric models based on count data:Comparisons and applications of some estimators and tests,� Journal of AppliedEconometrics 1(1), 29-53.

3. Chay, K. Y., & Powell, J. L. (2001), �Semiparametric censored regression models,�The Journal of Economic Perspectives 15(4), 29-42.

4. Dhrymes, P. J. (1986), �Limited dependent variables,�in Z. Griliches & M. D. Intrili-gator (eds.), Handbook of Econometrics, volume 3, chapter 27, 1567-1631 Elsevier.

5. Ger�n, M. (1996), �Parametric and semiparametric estimation of the binary responsemodel of labour market participation,�Journal of Applied Econometrics 11(3), 321-339.

6. Greene, W. (2006), �Censored data and truncated distributions,� in T. C. Mills &Kerry Patterson (eds.), Palgrave Handbook of Econometrics, Volume 1, Chapter 20,695-736, Palgrave, London.

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7. Heckman, J. J. (1979), �Sample selection bias as a speci�cation error,�Econometrica47(1), 153-161.

8. Horowitz, J. L., & Savin, N. (2001), �Binary response models: Logits, probits andsemiparametrics,�The Journal of Economic Perspectives, 15(4), 43-56.

9. Kiefer, N. M. (1988), �Economic duration data and hazard functions,� Journal ofEconomic Literature 26(2), 646-679.

10. Lancaster, T. (1979), �Econometric methods for the duration of unemployment,�Econometrica 47(4) , 939-956.

11. McFadden, D. (1984), �Econometric analysis of qualitative response models,� in Z.Griliches & M. D. Intriligator (eds.), Handbook of Econometrics, Volume 2, Chapter24, 1395-1457, Elsevier.

12. Meng, X. y P. Miller (1995), �Occupational Segregation and Its Impact on GenderWage Discrimimation in China�s Rural Industrial Sector,�Oxford Economic Papers47, 136-155.

13. Miller, P. W. y P. A. Volker (1985), �On the Determination of Occupational Attain-ment and Mobility,�Journal of Human Resources 20, 197-213.

14. Tobin, J. (1958), �Estimation of relationships for limited dependent variables,�Econo-metrica 26(1), 24-36.

15. Vella, F. (1998), �Estimating models with sample selection bias: A survey,� TheJournal of Human Resources 33(1), 127-169.

8.2.7 Econometría de Datos de Panel

1. Arellano, M. (1987), �Computing Robust Standard Errors for Within-Group Estima-tors,�Oxford Bulletin of Economics and Statistics 49, 431-434.

2. Arellano, M. (1990), �Testing for Autocorrelated in Dynamic Random E¤ects Mod-els,�Review of Economic Studies 57, 127-134.

3. Arellano, M. y S. R. Bond (1991), �Some Tests of Speci�cation for Panel Data: MonteCarlo Evidence and an Application to Employment Equation,�Review of EconomicStudies 58, 277-297.

4. Baltagi, B. y C. Kao (2000), �Nonstationary Panel, Cointegration in Panels and Dy-namic Panels: A Survey,�manuscrito.

5. Judson, R. A. y A. L. Owen (1999), �Estimating dynamic panel data models: a guidefor macroeconomists,�Economics Letters 65, 9-15.

6. Phillips, P. C. B. y H. R. Moon (1999), �Nonstationary Panel data Analysis: AnOverview of Some Recent Developments,�Manuscript of the Cowles Foundation atYale University.

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7. Phillips, P. C. B. y H. R. Moon (1999), �Linear Regression Limit Theory for Nonsta-tionary Panel Data�Econometrica 67 (5), 1057-1111.

8. Westerlund, J. (2003), �A Panel Data Test of the Bank Lending Channel in Sweden,�manuscrito.

9 Plagio

Todo acto de plagio en el desarrollo del curso (prácticas o laboratorios cali�cados, trabajo desesión, exámenes parcial y �nal) será castigado de manera severa y de acuerdo al reglamentode la Universidad. Una guia relacionada con las reglas de elaboración de trabajos y otrosasuntos relacionados puede ser distribuida al comienzo del curso.

Lima, Agosto 2012

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