Professor Frank Windmeijer

Professor Frank Windmeijer

Professor Frank Windmeijer
Professor of Econometrics

2B5,
The Priory Road Complex, Priory Road, Clifton
BS8 1TU
(See a map)

f.windmeijer@bristol.ac.uk

Telephone Number (0117) 928 8423

Department of Economics

Research

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.

Affiliations

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

Teaching

Applied Econometrics

MRES Econometrics 1

 

Fields of interest

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



Key publications

  1. Davies, N, Dickson, MR, Smith, GD, van den Berg, GJ & Windmeijer, F, 2018, ‘The causal effects of education on health outcomes in the UK Biobank’. Nature Human Behaviour, vol 2., pp. 117-125
  2. Pacini, D & Windmeijer, F, 2016, ‘Robust Inference for the Two-Sample 2SLS Estimator’. Economics Letters, vol 146., pp. 50-54
  3. 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
  4. Allen, R, Burgess, SM, Davidson, R & Windmeijer, F, 2015, ‘More reliable inference for the dissimilarity index of segregation’. Econometrics Journal, vol 18., pp. 40-66
  5. Smith, SL, Windmeijer, F & Wright, EW, 2015, ‘Peer effects in charitable giving: Evidence from the (running) field’. Economic Journal, vol 125., pp. 1053-1071
  6. 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
  7. Clarke, PS & Windmeijer, F, 2012, ‘Instrumental variable estimators for binary outcomes’. Journal of the American Statistical Association, vol 107., pp. 1638-1652
  8. 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
  9. Newey, W & Windmeijer, F, 2009, ‘Generalized Method of Moments With Many Weak Moment Conditions’. Econometrica, vol 77., pp. 687 - 719
  10. 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
  11. Windmeijer, F, Farbmacher, H, Davies, N & Smith, GD, 2018, ‘On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments’. Journal of the American Statistical Association.

Latest publications

  1. Taylor, G, Itani, T, Thomas, K, Rai, D, Jones, T, Windmeijer, F, Martin, R, Munafo, M, Davies, N & Taylor, A, 2019, ‘Prescribing prevalence, effectiveness, and mental health safety of smoking cessation medicines in patients with mental disorders’. Nicotine and Tobacco Research.
  2. Polanski, A, Stoja, E & Windmeijer, F, 2019, ‘Telling tales from the tails: High-dimensional tail interdependence’. Journal of Applied Econometrics.
  3. Windmeijer, F, 2019, ‘Two-Stage Least Squares as Minimum Distance’. Econometrics Journal, vol 0., pp. 1-9
  4. Davies, NM, Taylor, GM, Taylor, AE, Jones, T, Martin, RM, Munafò, MR, Windmeijer, F & Thomas, KH, 2018, ‘The effects of prescribing varenicline on two-year health outcomes: an observational cohort study using electronic medical records’. Addiction, vol 113., pp. 1105-1116
  5. Skeels, C & Windmeijer, F, 2018, ‘On the Stock-Yogo Tables’. Econometrics, vol 6.
  6. Sanderson, E, Smith, GD, Windmeijer, F & Bowden, J, 2018, ‘An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings’. International Journal of Epidemiology.
  7. Walker, V, Davies, N, Windmeijer, F, Burgess, S & Martin, R, 2017, ‘Power calculator for instrumental variable analysis in pharmacoepidemiology’. International Journal of Epidemiology, vol 46., pp. 1627-1632
  8. 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, vol 46., pp. 2067-2077
  9. Taylor, G, Taylor, A, Thomas, K, Jones, T, Martin, R, Munafo, M, Windmeijer, F & Davies, N, 2017, ‘The effectiveness of varenicline versus nicotine replacement therapy on long-term smoking cessation in primary care: a prospective cohort study of electronic medical records’. International Journal of Epidemiology.
  10. 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
  11. 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.
  12. 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
  13. 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
  14. 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
  15. 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.
  16. 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
  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. 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
  19. 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, vol 347., pp. f5704
  20. 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
  21. 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’. BMJ, vol 347.
  22. 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
  23. 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
  24. 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’. British Journal of General Practice, vol 63., pp. 274-82
  25. 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.
  26. 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
  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. 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
  30. 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. -
  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. 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
  33. 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
  34. Bun, M & Windmeijer, F, 2010, ‘The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models’. Econometrics Journal, vol 13., pp. 95 - 126
  35. 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
  36. Propper, C, Windmeijer, F, Whitnall, C & Sutton, M, 2008, ‘Did targets and terror reduce waiting times for hospital care in England’. BE Journal of Economic Analysis and Policy.
  37. 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
  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. 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
  41. Windmeijer, F, Laat, Ed, Douven, R & Mot, E, 2006, ‘Pharmaceutical promotion and GP prescription behaviour’. Health Economics, vol 15 (1)., pp. 5 - 18
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. Bond, S & Windmeijer, F, 2002, ‘Projection Estimators for Autoregressive Panel Data Models’. Econometrics Journal, vol 5(2)., pp. 457 - 479
  48. Blundell, R, Griffith, R & Windmeijer, F, 2002, ‘Individual effects and dynamics in count data models’. Journal of Econometrics, vol 108 (1)., pp. 113 - 131
  49. 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
  50. Silva, JS & Windmeijer, F, 2001, ‘Two-part multiple spell models for health care demand’. Journal of Econometrics, vol 104 (1)., pp. 67 - 89
  51. Blundell, R & Windmeijer, F, 2000, ‘Identifying demand for health resources using waiting times information’. Health Economics, vol 9., pp. 465-74
  52. 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
  53. Windmeijer, F, 2000, ‘Moment conditions for fixed effects count data models with endogenous regressors’. Economics Letters, vol 68., pp. 21-24
  54. 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
  55. Blundell, R & Windmeijer, F, 1997, ‘Cluster effects and simultaneity in multilevel models’. Health Economics, vol 6., pp. 439-443
  56. 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
  57. 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
  58. 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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