A full summary of all notes needed for the exam of economic methodology. This summary includes both book notes, knowledge clips and lectures. The final grade from studying with this summary was 9.
ECONOMIC METHODOLOGY
WEEK 1
CHAPTER 1
- Ontology → how reality is formed
- Epistemology → theory of knowledge
- Method → techniques how to get knowledge
- Sociology of science → what scientists do
- Positive science → facts (describing, predicting, explaining)
- Normative science → norms (prescriptions, recommendations)
History – the Wiener Kreis
- Sticking to the facts
- Science = meaningful
o Economically verifiable – synthetic a posteriori (by observation)
o Logically verifiable – analytic (by logical analysis)
o Not meaningful – synthetic a priori (cannot be verified)
Two foundations of logical empiricism
- Logical analysis → formal mathematic term
- Empiricism → observation and measurement (observable in practice)
- 2 steps
o 1. Statement has to be empirically verifiable
o 2. Statement has been verified
- Operationalisation → defining theory in observable terms, theoretical concept =
definitions of observable theories
o Different ways of measuring unemployment
- Demarcation → dividing between scientific and non-scientific
o Scientific if – logically consistent, operationalised
- Context of discovery (irrelevant) → how a law was discovered
- Context of justification → the way it is verified
- Logical positivists can only prove things but never disprove
Symmetry thesis → scientific predictions and scientific explanations meet the same
requirements of the deductive nomological model (DN) → explanation and prediction have
the same logical structure
Deductive nomological model – Hampel
- Explanans
o 1. Lawlike proposition
o 2. Conditions
- Explanandum
, o 3. Phenomenon to explain
- Symmetry thesis
o Explaining (3) requires finding (1) and (2)
o If (1) and (2) → predicting (3)
▪ Domain is not limited by finite space, time, group
- Example:
o Law → quantity theory: in the long run the growth rate of the nominal money
supply is equal to the growth rate of nominal GDP
o Conditions → In the US the annualized growth rate of M1 between 1971 and
2017 was 6%
o Prediction → in the US the annualized growth rate of nominal GDP between
1971 and 2017 was 6%
Humean problem of induction
- Laws are not scientific (black swans)
o Scientific – can be observed
o Non-scientific – not checkable
- Instrumentalism (Schlick)
o Universal statement ≠ meaningful
o Meaningful = certain
o Laws are not scientific
o Laws judged by usefulness to explain
o Universal statements are not scientific
- Confirmationism (Carnap)
o Probability instead of certainty
o Degree of confirmation (x%)
o Laws are scientific but never certain
o Universal statements are scientific but with a certain probability
Why is a law subjective?
- 1. Not clear how to define what is a scientific law → semantically verified
unrestricted generalisation (not accidental)
- 2. Operationalism → how theoretical concepts can be measured (but it can be done
in different ways)
- 3. Hume’s problem of induction → induction derives conclusions from experiences
and observations (somewhat subjective)
- Problem
o Scientific laws require unverified background theory (theoretical terms cannot
be operationalised)
o Scientific theories are never completely neutral
- Logical positivism has failed
- Scientific requirements (meaning) → gain meaning through operationalization
- Syntactic requirements (form) → for generalisation to be law it needs to be
unrestricted generalisation (not restricted to space/time)
,CHAPTER 2
Methodologies of positive economics
- Econometrics – Tinbergen’s approach
o 1. Model must be specified in explicit and functional form (linear)
o 2. Decide appropriate data definitions (operationalization)
o 3. Bridge between theory and data (verify key stats)
Keynes-Tinbergen debate (Keynes’ criticism of Tinbergen) → is it going to hold for the
future?
- Tinbergen’s method → a lot of complicated equations (mathematical model)
- Criticism by Keynes
o Not testing just measuring
o Measurements are only correct with complete model (all causalities are
known)
- 3 points of scepticism
o 1. Omitted variable bias → estimates of the causal factors are biased unless
all causal factors are taken into account – impossible
o 2. Data mining → too much trial and error to make sure the data fits the
equations
o 3. Invariance problems → is it going to hold in the future
Haavelmo – Probability approach
- We can continue measuring → if model is complete enough → law
- Problem of passive observations → we do not know whether the variable has
potential influences in the future or not
- The problem is that in economic variables are strongly interdependent with many
variables involved and we cannot do controlled experiments
o Variable may not be significant now → but might be after certain threshold
o If we wait → model is incomplete
o If we include everything → model is too complex
o Keynes is right → we can only measure
Measurement-without-theory debate
- Cowles Commission (Koopmans) vs NBER (Vinning)
- Koopmans criticised NBER → theory is based on observations (tells us what is
important)
- NBER → descriptive statistics, not model based, inductive → no observations
- Cowles Commission → empirical research based on theory
o 1. A priori math model
o 2. Estimated with simultaneous equations econometrics
o 3. Hypothesis testing
- Koopmans – arguments against measurement without theory
, o There are many variables and relations that may play a role that we need
theory to know which relations have to be tested empirically in a meaningful
way
o Theory is needed to tell us which relations are time invariant
o Statistical analysis presupposes assumptions which cannot be tested with the
same data
o Theory is needed to clarify how the empirical research have to be interpreted
o Without theory it is not clear what a researcher means by the applied
concepts and terms
- Response of NBER – Vinning
o Cowles Commission’s model is ‘pretty skinny felow’ → too simple to capture
the diversity of data
o Inductive approach – descriptive statistics is useful to discover hypothesis
Problems of econometrics
- No lab
- More variables → more certainty but too complex
- Starting big (NBER x Tinbergen) x start small theoretical basis and expand (Koopmans
x Cowles C, Haavelmo)
- Complexity unavoidable
Methodology of positive economics – Friedman
- Dealing with complexity – critical of large mathematical models
- Friedman’s arguments against macroeconomic models
o Too much trial and error → invariance problem
- Naïve models work better
o 𝑦𝑡+1 = 𝑦𝑡 + 𝜀𝑡
o 𝑦𝑡+1 = 𝑦𝑡 + (𝑦𝑡 − 𝑦𝑡−1 ) + 𝜀𝑡
o Marshal → simple model first
o Friedman → model should predict well
- A model is scientific if it predicts well and in well-specified domains
- We should start with small parts (models) and later combine them into one large
macroeconomic model
Interpretation of Friedman:
- 1. In favour of economic instrumentalism
o Whether assumptions are realistic or not does not matter as long as they
predict well
o More radical than Schlick
- 2. Anti-realisticness – Friedman opposing realism
o Realism → all lawlike statements are considered scientific (no matter how
bad the correlation is)
o Realism of assumptions does not matter
o Problem: anything goes
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