{"id":11439,"date":"2015-11-08T10:02:35","date_gmt":"2015-11-08T15:02:35","guid":{"rendered":"http:\/\/marcfbellemare.com\/wordpress\/?page_id=11439"},"modified":"2026-04-03T12:37:50","modified_gmt":"2026-04-03T17:37:50","slug":"metrics-mondays","status":"publish","type":"page","link":"https:\/\/marcfbellemare.com\/wordpress\/metrics-mondays","title":{"rendered":"Metrics Mondays"},"content":{"rendered":"

(Note: The most recent posts will be added to the top of the list, and posts are generally listed from most recent to oldest.)<\/p>\n

    \n
  1. Claude Montmarquette (1942-2021)<\/a><\/li>\n
  2. New Version of “The Paper of How: Estimating Treatment Effects with the Front-Door Criterion”<\/a><\/li>\n
  3. Peter Kennedy, Judea Pearl, or Both?<\/a><\/li>\n
  4. Assessing the Extent of SUTVA Violations<\/a><\/li>\n
  5. Advanced Econometrics–Recent Methods and Issues<\/a><\/li>\n
  6. It’s Written in the Stars<\/a><\/li>\n
  7. The Paper of How: Estimating Treatment Effects with the Front-Door Criterion<\/a><\/li>\n
  8. Lagged Variables as Instruments<\/a><\/li>\n
  9. Least Squares is But One Approach to Linear Regression<\/a><\/li>\n
  10. When in Doubt, Standardize<\/a><\/li>\n
  11. New Version of “Elasticities and the Inverse Hyperbolic Sine Transformation”<\/a><\/li>\n
  12. Recoding Dummy Variables<\/a><\/li>\n
  13. Learning Machine Learning<\/a><\/li>\n
  14. Front-Door Criterion Follow-Up<\/a><\/li>\n
  15. Using the Front-Door Criterion in a Regression Context<\/a><\/li>\n
  16. Goodness of Fit with Panel Data in Stata<\/a><\/li>\n
  17. Identification by Functional Form<\/a><\/li>\n
  18. Copenhagen Course, Lecture 8 (Tricks of the Trade II)<\/a><\/li>\n
  19. Copenhagen Course, Lecture 7 (Tricks of the Trade I)<\/a><\/li>\n
  20. Copenhagen Course, Lecture 6 (Nonstandard Dependent Variables)<\/a><\/li>\n
  21. Copenhagen Course, Lecture 5 (Regression Discontinuity Designs)<\/a><\/li>\n
  22. Elasticities and the Inverse Hyperbolic Sine Transformation<\/a><\/li>\n
  23. Copenhagen Course, Lecture 4 (Panel Data and Difference-in-Differences)<\/a><\/li>\n
  24. Copenhagen Course, Lecture 3 (Instrumental Variables)<\/a><\/li>\n
  25. Causality and Copenhagen Course, Lectures 1 and 2<\/a><\/li>\n
  26. Don’t Overcontrol<\/a><\/li>\n
  27. Survivorship Bias<\/a><\/li>\n
  28. Identification Is Not Causality, Causality Is Not Identification<\/a><\/li>\n
  29. What to Do Instead of log(x + 1)<\/a><\/li>\n
  30. Advanced Econometrics–Causal Inference with Observational Data<\/a><\/li>\n
  31. Useless Hausman Tests<\/a><\/li>\n
  32. The Dogit Model<\/a><\/li>\n
  33. 2SLS–Chronicle of a Death Foretold<\/a>?<\/li>\n
  34. You Can’t Compare OLS with 2SLS<\/a><\/li>\n
  35. When (Not) to Cluster<\/a><\/li>\n
  36. Generated Regressors, or Why Regressing on \\hat{X} Can Be a Problem<\/a><\/li>\n
  37. Good Things Come to Those Who Weight–Part I<\/a><\/li>\n
  38. Regression and Causality for Dummies<\/a><\/li>\n
  39. Achieving Statistical Significance with Covariates<\/a><\/li>\n
  40. Dealing with Imperfect Instruments III<\/a><\/li>\n
  41. We Wrote a Paper About Lagged Explanatory Variables. Here’s What Happened Next<\/a>.<\/li>\n
  42. Interactions as IVs and Spurious Findings<\/a><\/li>\n
  43. One IV for Two Endogenous Variable, and Testing for Mechanisms<\/a><\/li>\n
  44. How Should Econometrics Be Taught<\/a>?<\/li>\n
  45. Combining Bits and Pieces of Likelihood to Study Behavior<\/a><\/li>\n
  46. Fixed Effects, Random Effects, and (Lack of) External Validity<\/a><\/li>\n
  47. Dealing with Duration Data<\/a><\/li>\n
  48. Heteroskedasticity and Its Content<\/a><\/li>\n
  49. Dealing with Imperfect Instruments II<\/a><\/li>\n
  50. Dealing with Imperfect Instruments I<\/a><\/li>\n
  51. Testing for Mechanisms (and Possibly Ruling Out All Other Mechanisms)<\/a><\/li>\n
  52. How to Systematically Think about Selection<\/a><\/li>\n
  53. Simpson’s Paradox, or Why “Determinants of …” Papers are Problematic<\/a><\/li>\n
  54. Lagged Explanatory Variables and the Estimation of Causal Effects<\/a><\/li>\n
  55. Estimating Nonlinear Relationships<\/a><\/li>\n
  56. Nothing Compares 2 U<\/a><\/li>\n
  57. Robustness Check or Data Mining?<\/a><\/li>\n
  58. What to Do with Repeated Cross Sections?<\/a><\/li>\n
  59. Interpreting Coefficients II<\/a><\/li>\n
  60. “Are Those Two Distributions Alike?” Redux<\/a><\/li>\n
  61. Interpreting Coefficients I<\/a><\/li>\n
  62. Are Those Two Distributions Alike?<\/a><\/li>\n
  63. What to do When You Have the Whole Distribution Instead of a Sample?<\/a><\/li>\n
  64. Statistical vs. Economic Significance<\/a><\/li>\n
  65. Type III Errors<\/a><\/li>\n
  66. The Tobit Temptation<\/a><\/li>\n
  67. There Is More than One Source of Endogeneity<\/a><\/li>\n
  68. Why You Should Show a Regression of Y on Z<\/a><\/li>\n
  69. Fads and Fashions in Econometrics<\/a><\/li>\n
  70. Multicollinearity<\/a><\/li>\n
  71. Friends Do Let Friends Do IV<\/a><\/li>\n
  72. Regressions as Ecosystems<\/a><\/li>\n
  73. When Is Heteroskedasticity (Not) a Problem?<\/a><\/li>\n
  74. Hypothesis Testing in Theory and in Practice<\/a><\/li>\n
  75. Statistical Literacy<\/a><\/li>\n
  76. Data Cleaning<\/a><\/li>\n
  77. Outliers<\/a><\/li>\n
  78. Proxy Variables<\/a><\/li>\n
  79. What to Do with Missing Data<\/a><\/li>\n
  80. What to Do with Endogenous Control Variables<\/a><\/li>\n
  81. Control Variables: More Isn’t Necessarily Better<\/a><\/li>\n
  82. You Can’t Test for Exogeneity: Uninformative Hausman Tests<\/a><\/li>\n
  83. “Do Both”<\/a><\/li>\n
  84. You Keep Using that Instrumental Variable; I Do Not Think It Does What You Think It Does<\/a><\/li>\n
  85. PSA: p-Values Are Thresholds, Not Approximations<\/a><\/li>\n
  86. The Use and Misuse of R-Square<\/a><\/li>\n
  87. Big Dumb Data?<\/a><\/li>\n
  88. Rookie Mistakes in Empirical Analysis<\/a><\/li>\n
  89. Goodness of Fit in Binary Choice Models<\/a><\/li>\n
  90. A Nifty Fix for When Your Treatment Variable Is Measured with Error<\/a><\/li>\n
  91. A Rant on Estimation with Binary Dependent Variables<\/a><\/li>\n
  92. Love It or Logit, or: Man, People *Really* Care about Binary Dependent Variables<\/a><\/li>\n
  93. In Defense of the Cookbook Approach to Econometrics<\/a><\/li>\n
  94. More on the Cookbook Approach to Econometrics<\/a><\/li>\n
  95. Econometrics Teaching Needs an Overhaul<\/a><\/li>\n
  96. Hipstermetrics<\/a><\/li>\n
  97. On the (Mis)Use of Regression Analysis: Country Music and Suicide<\/a><\/li>\n
  98. Methodological Convergence in the Social Sciences<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    (Note: The most recent posts will be added to the top of the list, and posts are generally listed from most recent to oldest.) Claude…<\/p>\n

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