Professor Frank Windmeijer

Professor Frank Windmeijer

Professor Frank Windmeijer
Professor of Econometrics

The Priory Road Complex, Priory Road, Clifton
(See a map)

Telephone Number (0117) 928 8423

Department of Economics


Microeconometrics, Panel Data Econometrics, Economics of Health Care. Current research projects include the use of genetic markers as instrumental variables, identification of causal effects on binary outcomes, and weak instruments in crosssection and panel data models.


  • Research Fellow, Centre for Microdata Methods and Practice, Institute for Fiscal Studies
  • Associate Editor, Journal of Applied Econometrics


MRES Econometrics

Fields of interest

Microeconometrics, Causal Inference, Panel Data Econometrics, Economics of Health Care.

Latest publications

  1. Davies, N, Thomas, K, Taylor, A, Taylor, G, Martin, R, Munafo, M & Windmeijer, F, 2017, ‘How to compare instrumental variable and conventional regression analyses using negative controls and bias plots’. International Journal of Epidemiology.
  2. Terris-Prestholt, F & Windmeijer, F, 2016, ‘How to sell a condom? The impact of demand creation tools on male and female condom sales in resource limited settings’. Journal of Health Economics, vol 48., pp. 107-120
  3. Pacini, D & Windmeijer, F, 2016, ‘Robust Inference for the Two-Sample 2SLS Estimator’. Economics Letters, vol 146., pp. 50-54
  4. Scholder, SVHK, Smith, GD, Lawlor, DA, Propper, C & Windmeijer, F, 2016, ‘Genetic markers as instrumental variables’. Journal of Health Economics, vol 45., pp. 131-148
  5. Sanderson, E & Windmeijer, F, 2016, ‘A Weak Instrument F-Test in Linear IV Models with Multiple Endogenous Variables’. Journal of Econometrics, vol 190., pp. 212-221
  6. Davies, NM, Scholder, SVHK, Farbmacher, H, Burgess, S, Windmeijer, F & Smith, GD, 2015, ‘The many weak instruments problem and Mendelian randomization’. Statistics in Medicine, vol 34., pp. 454-468
  7. Smith, SL, Windmeijer, F & Wright, EW, 2015, ‘Peer effects in charitable giving: Evidence from the (running) field’. The Economic Journal, vol 125., pp. 1053-1071
  8. Davies, NM, Taylor, GMJ, Taylor, AE, Thomas, KH, Windmeijer, F, Martin, RM & Munafo, MR, 2015, ‘What are the effects of varenicline compared with nicotine replacement therapy on long-term smoking cessation and clinically important outcomes?: Protocol for a prospective cohort study’. BMJ Open, vol 5.
  9. Davies, NM, Hemani, G, Timpson, NJ, Windmeijer, F & Smith, GD, 2015, ‘The role of common genetic variation in educational attainment and income: evidence from the National Child Development Study’. Scientific Reports, vol 5.
  10. Allen, R, Burgess, SM, Davidson, R & Windmeijer, F, 2015, ‘More reliable inference for the dissimilarity index of segregation’. The Econometrics Journal, vol 18., pp. 40-66
  11. Clarke, PS, Palmer, TM & Windmeijer, F, 2015, ‘Estimating Structural Mean Models with Multiple Instrumental Variables using the Generalised Method of Moments’. Statistical Science, vol 30., pp. 96-117
  12. Silva, JS, Tenreyro, S & Windmeijer, F, 2015, ‘Testing Competing Models for Non-Negative Data with Many Zeros’. Journal of Econometric Methods, vol 4., pp. 29-46
  13. Brilleman, S, Gravelle, H, Hollinghurst, S, Purdy, S, Salisbury, C & Windmeijer, F, 2014, ‘Keep it simple? Predicting primary health care costs with clinical morbidity measures’. Journal of Health Economics, vol 35., pp. 109-122
  14. Thomas, KH, Martin, RM, Davies, NM, Metcalfe, C, Windmeijer, F & Gunnell, D, 2013, ‘Authors' reply to Davies’. British Medical Journal, vol 347., pp. f7068
  15. Thomas, KH, Martin, RM, Davies, NM, Metcalfe, C, Windmeijer, F & Gunnell, D, 2013, ‘Still not clear that smoking cessation drugs do not cause psychiatric symptoms Reply’. British Medical Journal, vol 347.
  16. Thomas, KH, Davies, N, Metcalfe, C, Windmeijer, F, Martin, RM & Gunnell, D, 2013, ‘Validation of suicide and self-harm records in the Clinical Practice Research Datalink’. British Journal of Clinical Pharmacology, vol 76., pp. 145-157
  17. Davies, NM, Smith, GD, Windmeijer, F & Martin, RM, 2013, ‘Issues in the Reporting and Conduct of Instrumental Variable Studies A Systematic Review’. Epidemiology, vol 24., pp. 363-369
  18. Scholder, SMLvHK, Smith, GD, Lawlor, DA, Propper, C & Windmeijer, F, 2013, ‘Child height, health and human capital: Evidence using genetic markers’. European Economic Review, vol 57., pp. 1-22
  19. Brilleman, SL, Purdy, S, Salisbury, C, Windmeijer, F, Gravelle, H & Hollinghurst, S, 2013, ‘Implications of comorbidity for primary care costs in the UK: a retrospective observational study’. The British journal of general practice : the journal of the Royal College of General Practitioners, vol 63., pp. 274-82
  20. Thomas, KH, Martin, RM, Davies, NM, Metcalfe, C, Windmeijer, F & Gunnell, D, 2013, ‘Smoking cessation treatment and risk of depression, suicide, and self harm in the Clinical Practice Research Datalink: prospective cohort study’. BMJ (Clinical research ed.), vol 347., pp. f5704
  21. Davies, NM, Gunnell, D, Thomas, KH, Metcalfe, C, Windmeijer, F & Martin, RM, 2013, ‘Physicians' prescribing preferences were a potential instrument for patients' actual prescriptions of antidepressants’. Journal of Clinical Epidemiology, vol 66., pp. 1386-1396
  22. Davies, NM, Smith, GD, Windmeijer, F & Martin, RM, 2013, ‘COX-2 Selective Nonsteroidal Anti-inflammatory Drugs and Risk of Gastrointestinal Tract Complications and Myocardial Infarction An Instrumental Variable Analysis’. Epidemiology, vol 24., pp. 352-362
  23. Scholder, SvHK, Smith, GD, Lawlor, DA, Propper, C & Windmeijer, F, 2012, ‘The effect of fat mass on educational attainment: Examining the sensitivity to different identification strategies’. Economics and Human Biology.
  24. Clarke, PS & Windmeijer, F, 2012, ‘Instrumental variable estimators for binary outcomes’. Journal of the American Statistical Association, vol 107., pp. 1638-1652
  25. Scholder, S, Smith, GD, Lawlor, DA, Propper, C & Windmeijer, F, 2011, ‘Mendelian randomization: the use of genes in instrumental variable analyses’. Health Economics, vol 20., pp. 893-896
  26. Faulkner, GEJ, Grootendorst, P, Nguyen, [VVH, Andreyeva, T, Arbour-Nicitopoulos, K, Auld, MC, Cash, SB, Cawley, J, Donnelly, P, Drewnowski, A, Dube, L, Ferrence, R, Janssen, I, LaFrance, J, Lakdawalla, D, Mendelsen, R, Powell, LM, Traill, WB & Windmeijer, F, 2011, ‘Economic instruments for obesity prevention: results of a scoping review and modified delphi survey’. International Journal of Behavioral Nutrition and Physical Activity, vol 8., pp. -
  27. Bun, M & Windmeijer, F, 2011, ‘A Comparison of Bias Approximations for the Two-Stage Least Squares (2SLS) Estimator’. Economics Letters, vol 113(1)., pp. 76 - 79
  28. Tilling, K, Davies, N, Windmeijer, F, Kramer, M, Bogdanovich, N, Matush, L, Patel, R, Smith, GD, Ben-Shlomo, Y, Martin, R & study, gftPoBIT(, 2011, ‘Is infant weight associated with childhood blood pressure? Analysis of the Promotion of Breastfeeding Intervention Trial (PROBIT) cohort’. International Journal of Epidemiology, vol 40(5)., pp. 1227 - 1237
  29. Davies, NM, Windmeijer, F, Martin, RM, Abdollahi, MR, Smith, GD, Lawlor, DA, Ebrahim, S & Day, INM, 2011, ‘Use of Genotype Frequencies in Medicated Groups to Investigate Prescribing Practice: APOE and Statins as a Proof of Principle’. Clinical Chemistry, vol 57., pp. 502-510
  30. Gregg, P, Grout, P, Ratcliffe, A, Smith, S & Windmeijer, F, 2011, ‘How important is pro-social behaviour in the delivery of public services?’. Journal of Public Economics, vol 95., pp. 758 - 766
  31. Clarke, P & Windmeijer, F, 2010, ‘Identification of causal effects on binary outcomes using structural mean models’. Biostatistics, vol 11 (4)., pp. 756 - 770
  32. Bun, M & Windmeijer, F, 2010, ‘The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models’. The Econometrics Journal, vol 13., pp. 95 - 126
  33. Propper, C, Sutton, M, Whitnall, C & Windmeijer, F, 2010, ‘Incentives and Targets in Hospital Care: Evidence from a Natural Experiment’. Journal of Public Economics, vol 94., pp. 318 - 335
  34. Hong, J, Reed, C, Novick, D, Haro, J, Windmeijer, F & Knapp, M, 2010, ‘The Cost of Relapse for Patients with a Manic/Mixed episode of Bipolar Disorder in the EMBLEM Study’. PharmacoEconomics, vol 28(7)., pp. 555 - 566
  35. Tilling, K, Davies, N, Windmeijer, F, Smith, GD, Ben-Shlomo, Y, Kramer, M, Oken, E & Martin, R, 2010, ‘Associations of growth trajectories in infancy and childhood with cardiovascular and cognitive outcomes’. in: International Conference on Developmental Origins of Health and Disease, Munich, Germany.
  36. Hong, J, Windmeijer, F, Novick, D, Haro, J & Brown, J, 2009, ‘The cost of relapse in patients with schizophrenia in the European SOHO (Schizophrenia Outpatient Health Outcomes) study’. Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol 33(5)., pp. 835 - 841
  37. Newey, W & Windmeijer, F, 2009, ‘Generalized Method of Moments With Many Weak Moment Conditions’. Econometrica, vol 77., pp. 687 - 719
  38. Lawlor, D, Windmeijer, F & Smith, GD, 2008, ‘Is Mendelian randomization ‘lost in translation?’: Comments on ‘Mendelian randomization equals instrumental variable analysis with genetic instruments’ by Wehby et al’. Statistics in Medicine, vol 27(15)., pp. 2750 - 2755
  39. Knapp, M, Windmeijer, F, Brown, J, Kontodimas, S, Tzivelekis, S, Haro, JM, Ratcliffe, M, Hong, J, Novick, D & , 2008, ‘Cost-utility analysis of treatment with olanzapine compared with other antipsychotic treatments in patients with schizophrenia in the pan-European SOHO study’. Pharmacoeconomics, vol 26., pp. 341-358
  40. Windmeijer, F, 2008, ‘GMM for Panel Count Data Models’. in: M Laszlo, P Sevestre (eds) The Econometrics of Panel Data: Fundamentals and Recent Developments in Theory and Practice. Springer, pp. 603 - 624
  41. Propper, C, Windmeijer, F, Whitnall, C & Sutton, M, 2008, ‘Did targets and terror reduce waiting times for hospital care in England’. The BE Journal of Economic Analysis and Policy.
  42. Haro, JM, Kontodimas, S, Negrin, MA, Ratcliffe, M, Suarez, D & Windmeijer, F, 2006, ‘Methodological Aspects in the Assessment of Treatment Effects in Observational Health Outcomes Studies’. Applied Health Economics and Health Policy, vol 5(1)., pp. 11 - 25
  43. Windmeijer, F, Kontodimas, S, Knapp, M, Brown, J & Haro, JM, 2006, ‘Methodological approach for assessing the cost-effectiveness of treatments using longitudinal observational data: The SOHO study’. International Journal of Technology Assessment in Health Care, vol 22(4)., pp. 460 - 468
  44. Windmeijer, F, Laat, Ed, Douven, R & Mot, E, 2006, ‘Pharmaceutical promotion and GP prescription behaviour’. Health Economics, vol 15 (1)., pp. 5 - 18
  45. Windmeijer, F, 2005, ‘A finite sample correction for the variance of linear efficient two-step GMM estimators’. Journal of Econometrics, vol 126 (1)., pp. 25 - 51
  46. Bond, S & Windmeijer, F, 2005, ‘Reliable inference for GMM estimators? Finite sample properties of alternative test procedures in linear panel data models’. Econometric Reviews, vol 24 (1)., pp. 1 - 37
  47. Laporte, A & Windmeijer, F, 2005, ‘Estimation of Panel Data Models with Binary Indicators when Treatment Effects are not Constant over Time’. Economics Letters, vol 88(3)., pp. 389 - 396
  48. Gravelle, H, Hoonhout, P & Windmeijer, F, 2005, ‘Waiting Lists, Waiting Times and Admissions: an Empirical Analysis at Hospital and General Practice Level’. Health Economics, vol 14(9)., pp. 971 - 985
  49. Gravelle, H, Sutton, M, Morris, S, Windmeijer, F, Leyland, A, Dibben, C & Muirhead, M, 2003, ‘Modelling supply and demand influences on the use of health care: implications for deriving a needs-based capitation formula’. Health Economics, vol 12., pp. 985-1004
  50. Bond, S & Windmeijer, F, 2002, ‘Projection Estimators for Autoregressive Panel Data Models’. Econometrics Journal, vol 5(2)., pp. 457 - 479
  51. Blundell, R, Griffith, R & Windmeijer, F, 2002, ‘Individual effects and dynamics in count data models’. Journal of Econometrics, vol 108 (1)., pp. 113 - 131
  52. Bond, S, Bowsher, C & Windmeijer, F, 2001, ‘Criterion-based inference for GMM in autoregressive panel data models’. Economics Letters, vol 73(3)., pp. 379 - 388
  53. Silva, JS & Windmeijer, F, 2001, ‘Two-part multiple spell models for health care demand’. Journal of Econometrics, vol 104 (1)., pp. 67 - 89
  54. Windmeijer, F, 2000, ‘Moment conditions for fixed effects count data models with endogenous regressors’. Economics Letters, vol 68., pp. 21-24
  55. Blundell, R, Bond, S & Windmeijer, F, 2000, ‘Estimation in dynamic panel data models: Improving on the performance of the standard GMM estimator’. in: ADVANCES ECOOMETRICS, VOL 15, 2000. JAI-ELSEVIER SCIENCE INC, NEW YORK, pp. 53-91
  56. Blundell, R & Windmeijer, F, 2000, ‘Identifying demand for health resources using waiting times information’. Health Economics, vol 9., pp. 465-74
  57. Cameron, A & Windmeijer, F, 1997, ‘An R-squared measure of goodness of fit for some common nonlinear regression models’. Journal of econometrics, vol 77., pp. 329-342
  58. Windmeijer, F & Silva, J, 1997, ‘Endogeneity in count data models: An application to demand for health care’. Journal of Applied Econometrics, vol 12., pp. 281-294
  59. Blundell, R & Windmeijer, F, 1997, ‘Cluster effects and simultaneity in multilevel models’. Health Economics, vol 6., pp. 439-443
  60. Cameron, A & Windmeijer, F, 1996, ‘R-Squared measures for count data regression models with applications to health-care utilization’. Journal of business & economic statistics, vol 14., pp. 209-220
  61. Windmeijer, F, 1994, ‘THE MAXIMUM RANK CORRELATION ESTIMATOR AND THE RANK ESTIMATOR IN BINARY CHOICE MODELS’. Econometric Theory, vol 10., pp. 442-443

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