Econometrics estimates economic relationships from data. In this field it measures how shocks transmit, how policies affect outcomes and how variables move together over time, with uncertainty and model assumptions stated.
Econometrics applies statistical methods to economic data to estimate relationships, test theories and forecast. It provides empirical evidence on how shocks and policies affect economic outcomes. In economic development and development finance, econometric evidence estimates how disasters, pandemics and financial shocks affect output, employment and prices. It informs the economic case for risk reduction. The canonical description is on the STEM Commons.
Shock transmission models
Vector autoregressions and local projections estimate how commodity, financial and demand shocks affect output, inflation and public finances over time.
Panel analysis across countries
Panel methods compare countries over time to estimate relationships while controlling for fixed differences between them.
Nowcasting with alternative data
Night lights, mobile data and other high-frequency indicators are combined with published statistics to estimate current activity where data are lagged.
03Simulator
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction.
Controls
Growth of pressure
6.0% a year
Efficacy of capacity in reducing pressure
4.0% a year
Investment response to perceived pressure
20%
Perception delay
4.0 years
Tipping threshold of pressure
60 index
Amplification past the threshold
2.0 times
Years simulated
50 years
Readouts
Peak pressure
113 index
Capacity at the end
186 index
Years over the threshold
15.8 years
Numbers, methods and sources
Year
Pressure
Capacity
Perceived
0
20.0
50.0
20.0
4
23.3
44.9
21.2
8
27.2
42.2
24.0
12
31.7
42.3
27.7
16
36.8
45.5
32.2
20
42.3
51.5
37.2
24
47.9
60.3
42.6
28
53.2
71.3
48.0
32
57.8
84.3
53.1
36
66.4
98.4
58.4
40
82.7
115.9
69.7
44
97.8
139.7
83.8
48
109.1
169.4
97.3
Methods
System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme's own evidence.
Stocks, flows, delays and a tipping threshold. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: System dynamics (Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics); Critical transitions and tipping points (Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461); Causal loop diagram (Sterman (2000), Business Dynamics, McGraw-Hill).
Datasets and records used by econometrics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
Data
Role
Label
Macroeconomic time series
Transmission
Published statistics
Cross-country panels
Comparison
Published statistics
High-frequency proxies
Nowcasting
OBSERVED
05Methods and evidence
How econometrics for development is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Sensitivity analysis
Which inputs move the result for econometrics for development most, one at a time across their stated ranges.
Illustrative one-at-a-time sensitivity; a full analysis adds variance-based (Sobol) indices. Not results.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
06Validation
How results are checked.
Models are assessed through specification tests, robustness checks and out-of-sample performance. Results are compared across datasets and methods.
Check
How it is done
Specification robustness
Results are tested across model specifications and samples
Out-of-sample evaluation
Nowcasts are evaluated against later published data
Diagnostic tests
Models are tested for autocorrelation, stability and other assumptions
Endogeneity, measurement error and structural change can bias estimates. Relationships may shift under extreme conditions.
What are the limits of econometrics in this field?
Endogeneity, measurement error and structural change can bias estimates. Relationships may shift under extreme conditions.
How are results checked?
Models are assessed through specification tests, robustness checks and out-of-sample performance. Results are compared across datasets and methods.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIEconomic DevelopmentCountry Systems Risk and ResilienceGlobal Systems Risk and Resilience
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Economic Development
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