Cover......Page 1
Half-title......Page 3
Title......Page 5
Copyright......Page 6
Contents......Page 7
Introduction......Page 9
1 Introduction......Page 13
2 Humean scepticism......Page 14
3 Naturalism and pragmatism......Page 16
4 Apriorism......Page 18
5 Conjecturalism......Page 19
5.1.1 Falsification and verification......Page 20
5.1.2 The crucial test......Page 22
5.1.3 Critical rationalism......Page 25
5.1.4 Historicism......Page 26
5.2 Lakatos and conjecturalism......Page 27
5.2.1 Research programmes......Page 28
5.2.2 Growth and garbage......Page 29
6 Probabilism......Page 30
Notes......Page 31
1 Introduction......Page 33
2.1 Indifference......Page 34
2.2 Objections......Page 35
3 The rule of succession......Page 37
4 Summary......Page 38
Notes......Page 39
1 Probability functions and Kolmogorov's axioms......Page 40
2.2 Bayes' theorem, prior and posterior probability......Page 42
2.4 Distribution and density functions......Page 43
Notes......Page 44
1 Introduction......Page 45
2.1 The primacy of the collective......Page 46
2.2 Inference and the collective......Page 48
2.3 Appraisal......Page 50
3 R. A. Fisher's frequency theory......Page 52
3.1 Fisher on the aim and domain of statistics......Page 53
Consistency......Page 55
Maximum likelihood......Page 56
Sufficiency......Page 57
Significance test and P-value......Page 58
3.3 Randomization and experimental design......Page 59
3.4 Fiducial inference......Page 61
4 The NeymanโPearson approach to statistical inference......Page 62
4.1 Inductive behaviour......Page 63
4.2 The Neyman-Pearson lemma......Page 64
4.3 Fisher's critique......Page 65
4.4 NeymanโPearson and philosophy of science......Page 67
5 Popper on probability......Page 68
5.1 Popper and subjective probability......Page 69
5.2 The propensity theory of probability......Page 71
5.3 Corroboration and verisimilitude......Page 72
6 Summary......Page 74
Notes......Page 76
2 Keynes and the logic of probability......Page 79
2.1 A branch of logic......Page 80
2.2 Analogy and the principle of limited independent variety......Page 81
2.3 Measurable probability......Page 84
2.4 Statistical inference and econometrics: Keynes vs Tinbergen......Page 85
3.1 Confirmation......Page 89
3.2 The generalized rule of succession......Page 90
3.3 An assessment......Page 92
4.1 De Finetti, bookmaker of science......Page 93
4.2 Exchangeability......Page 95
4.4 Savage's worlds......Page 96
4.5 Inference, decision and Savage's impact on econometrics......Page 98
5.1 Informative priors......Page 99
5.2 Indifference again: non-informative priors and the uniform distribution......Page 100
5.3 The maximum entropy principle......Page 102
5.4 Universal priors......Page 104
6 Appraisal......Page 105
Notes......Page 107
1 Introduction......Page 110
2.1 Background: Ockham's razor and the law of parsimony......Page 111
2.2 Simplicity and a priori probability......Page 114
2.3 Simplicity and information theory......Page 116
2.4 Minimum description length (MDL) and maximum likelihood......Page 119
3.1 Simplicity and induction......Page 122
3.2 Simplicity and the DuhemโQuine thesis......Page 124
3.3 The purpose of a model......Page 125
3.4 Bounded rationality......Page 126
Notes......Page 128
2 Sampling theory......Page 131
3.1 Quetelismus......Page 134
3.2 Curve fitting: least squares, regression and a fallacy......Page 135
3.3 Spurious and nonsense correlation......Page 139
3.4 Multiple regression and ceteris paribus......Page 140
4 Samples and populations in econometrics......Page 142
4.1 A priori arguments......Page 143
4.2 Analytical arguments......Page 144
4.3 Metaphorical arguments......Page 146
5 Conclusion......Page 150
Notes......Page 152
1 Introduction......Page 154
2.1 An experimental method?......Page 155
2.2 Structure and reduced form......Page 158
2.3 Theory and practice......Page 159
3.1 Incredible restrictions......Page 160
3.2 Profligate modelling......Page 162
3.3 Innovation accounting......Page 163
3.4 VAR and inference......Page 164
4.1 Reduction and experimental design......Page 165
4.2 The Data Generation Process......Page 166
4.3 General to specific......Page 168
4.4 Falsificationism and the three cheers for testing......Page 171
4.5 Model design and inference......Page 175
5.1 Extreme Bounds Analysis......Page 177
5.2 Sense and sensitivity......Page 180
6.1 Elements of calibration......Page 182
6.2 The quest for deep parameters......Page 183
6.3 Calibration and probabilistic inference......Page 185
7 Conclusion......Page 187
Notes......Page 189
2.1 Introduction......Page 192
2.2.1 Axioms and restrictions......Page 193
2.2.2 Demand functions and their properties......Page 195
2.2.3 Homogeneity and the systems approach......Page 197
2.3.2 Invalid restrictions......Page 200
2.3.4 Invalid aggregation......Page 202
2.3.5 Invalid stochastics......Page 203
3.1.1 Goodness of fit......Page 205
3.1.3 Stone and the measurement of consumer behaviour......Page 207
3.2 Empirical work based on demand systems......Page 208
4.1 Rejection without falsification......Page 211
4.2 Popper's rationality principle......Page 213
4.3 Does Lakatos help?......Page 214
4.4 Auxiliary hypotheses and the DuhemโQuine thesis......Page 215
5.1 Statistical testing using frequency concepts......Page 217
5.2 The lesson from Monte Carlo......Page 218
5.3 The epistemological perspective......Page 219
5.4 In search of homogeneity......Page 220
6 Summary......Page 223
Notes......Page 224
1 Introduction......Page 225
2.1 Maxims of positivism......Page 226
2.2 Scientific realism......Page 227
3.1 From measurement to statistics......Page 229
3.2 Robbins contra the statisticians......Page 230
3.3 Facts and theory......Page 232
3.4 Constants and testing......Page 233
4 Causality, determinism and probabilistic inference......Page 234
4.1.2 Necessary and sufficient causes......Page 236
4.1.3 Hume's critique of causality......Page 238
4.1.4 Peirce on the doctrine of necessity......Page 239
4.2.1 Laplace revisited......Page 240
4.2.2 Frequency and cause......Page 241
4.2.3 Keynes' causa cognoscendi......Page 242
4.2.4 From Fisher's dictum to the simplicity postulate......Page 245
4.3.1 Recursive systems......Page 246
4.3.2 Causality and conditional expectation......Page 249
4.3.3 Wiener-Granger causality......Page 251
4.3.4 Cause and intervention......Page 252
5.1.1 Prediction and inference......Page 253
5.1.2 Fishing for red herrings: prediction, novel facts and old evidence......Page 254
5.2 The Austrian critique on prediction and scientism......Page 256
6.1.1 Theory testing......Page 258
6.1.2 Validity testing......Page 260
6.2.1 General remarks......Page 261
6.2.2 Frequentist approaches to testing rival theories......Page 262
6.2.3 Bayesian approaches to testing rival theories......Page 267
7 Summary......Page 270
Notes......Page 271
1 Introduction......Page 274
2.1 Popperians without falsifications......Page 275
2.2 Econometrics in the methodological literature......Page 276
3.1 Frequentist interpretations of probability and econometrics......Page 278
3.2 Frequentists without frequencies......Page 279
3.3 The frequentistโBayes compromise......Page 280
4.1 Truth and probabilistic inference......Page 281
4.3 Limits to probabilism......Page 283
4.4 Econometrics and positivism......Page 285
5 Conclusion......Page 286
Notes......Page 287
Personalia......Page 288
References......Page 293
Name Index......Page 311
Subject index......Page 319