This book is in Open Review. We want your feedback to make the book better for you and other students. You may annotate some text by selecting it with the cursor and then click the on the pop-up menu. You can also see the annotations of others: click the in the upper right hand corner of the page


Allaire, J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., … Iannone, R. (2018). rmarkdown: Dynamic Documents for R (Version 1.11). Retrieved from

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.

Card, D. (1993). Using geographic variation in college proximity to estimate the return to schooling. National Bureau of Economic Research.

Card, D., & Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. The American Economic Review, 84(4), 772–793.

Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591–605.

Cochrane, D., & Orcutt, G. H. (1949). Application of Least Squares Regression to Relationships Containing Auto-Correlated Error Terms. Journal of the American Statistical Association, 44(245), 32–61. doi:10.1080/01621459.1949.10483290

Croissant, Y., Millo, G., & Tappe, K. (2017). plm: Linear Models for Panel Data (Version 1.6-6). Retrieved from

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), pp. 427–431.

Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for an Autoregressive Unit Root. Econometrica, 64(4), 813–836.

Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50(4), 987–1007.

Engle, R., & Granger, C. (1987). Co-integration and Error Correction: Representation, Estimation and Testing. Econometrica, 55(2), 251–76.

Genz, A., Bretz, F., Miwa, T., Mi, X., & Hothorn, T. (2018). mvtnorm: Multivariate Normal and t Distributions (Version 1.0-8). Retrieved from

Granger, C. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37(3), 424–438.

Heiss, F. (2016). Using R for Introductory Econometrics. CreateSpace Independent Publishing Platform. Retrieved from

Hlavac, M. (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables (Version 5.2.2). Retrieved from

Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’Hara-Wild, M., … Yasmeen, F. (2018). forecast: Forecasting Functions for Time Series and Linear Models (Version 8.4). Retrieved from

Kleiber, C., & Zeileis, A. (2008). Applied Econometrics with R. Springer.

Kleiber, C., & Zeileis, A. (2018). AER: Applied Econometrics with R (Version 1.2-6). Retrieved from

MacKinnon, J. G., & White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29(3), 305–325.

Newey, W. K., & West, K. D. (1987). A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55(3), 703–08.

Pfaff, B. (2016). urca: Unit Root and Cointegration Tests for Time Series Data (Version 1.3-0). Retrieved from

Pfaff, B. (2018). vars: VAR Modelling (Version 1.5-3). Retrieved from

Pinheiro, J., Bates, D., & R-core. (2018). nlme: Linear and Nonlinear Mixed Effects Models (Version 3.1-137). Retrieved from

Quandt, R. E. (1960). Tests of the Hypothesis That a Linear Regression System Obeys Two Separate Regimes. Journal of the American Statistical Association, 55(290), 324–330. doi:10.1080/01621459.1960.10482067

R Core Team. (2018). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from

Ripley, B. (2018). MASS: Support Functions and Datasets for Venables and Ripley’s MASS (Version 7.3-51.1). Retrieved from

Ryan, J. A., & Ulrich, J. M. (2018). quantmod: Quantitative Financial Modelling Framework (Version 0.4-13). Retrieved from

Spada, S. (2018). orcutt: Estimate Procedure in Case of First Order Autocorrelation (Version 2.3). Retrieved from

Stigler, M., & Quast, B. (2015). rddtools: Toolbox for Regression Discontinuity Design (’RDD’) (Version 0.4.0). Retrieved from

Stock, J., & Watson, M. (2015). Introduction to Econometrics, Third Update, Global Edition. Pearson Education Limited.

Venables, W. N., & Smith, D. M. (2010). An Introduction to R. Retrieved from

White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica, 48(4), pp. 817–838.

Wickham, H. (2018). scales: Scale Functions for Visualization (Version 1.0.0). Retrieved from

Wickham, H., & Bryan, J. (2018). readxl: Read Excel Files (Version 1.2.0). Retrieved from

Wickham, H., François, R., Henry, L., & Müller, K. (2018). dplyr: A Grammar of Data Manipulation (Version 0.7.8). Retrieved from

Wickham, H., & Henry, L. (2018). tidyr: Easily Tidy Data with ’spread()’ and ’gather()’ Functions (Version 0.8.2). Retrieved from

Wooldridge, J. (2016). Introductory Econometrics (Sixth). Cengage Learning.

Wuertz, D., Setz, T., Chalabi, Y., Boudt, C., Chausse, P., & Miklovac, M. (2017). fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling (Version 3042.83). Retrieved from

Xie, Y. (2018a). bookdown: Authoring Books and Technical Documents with R Markdown (Version 0.9). Retrieved from

Xie, Y. (2018b). knitr: A General-Purpose Package for Dynamic Report Generation in R (Version 1.21). Retrieved from

Zeileis, A. (2016). dynlm: Dynamic Linear Regression (Version 0.3-5). Retrieved from