# Methodology of econometrics

The **methodology of econometrics** is the study of the range of differing approaches to undertaking econometric analysis.^{[1]}

The econometric approaches can be broadly classified into nonstructural and

^{[3]}Structural models allow to perform calculations for the situations that are not covered in the data being analyzed, so called

**counterfactual analysis**(for example, the analysis of a monopolistic market to accommodate a hypothetical case of the second entrant).

^{[4]}

## Examples

Commonly distinguished differing approaches that have been identified and studied include:

- the Cowles Commission approach
^{[5]} - the vector autoregression (VAR) approach
^{[6]} - the LSE approach to econometrics - originated with Denis Sargan now associated with David Hendry (and his general-to-specific modeling). Also associated this approach is the work on integrated and cointegrated systems originating on the work of Engle and Granger and Johansen and Juselius (Juselius 1999)
- the use of calibration - Finn Kydland and Edward Prescott
^{[7]} - the
*experimentalist*or difference in differences approach - Joshua Angrist and Jörn-Steffen Pischke.^{[8]}

In addition to these more clearly defined approaches, Hoover^{[9]} identifies a range of *heterogeneous* or *textbook approaches* that those less, or even un-, concerned with methodology, tend to follow.

## Methods

Econometrics may use standard

One of the fundamental statistical methods used by econometricians is

^{[13]}

### Experimental economics

In recent decades, econometricians have increasingly turned to use of experiments to evaluate the often-contradictory conclusions of observational studies. Here, controlled and randomized experiments provide statistical inferences that may yield better empirical performance than do purely observational studies.^{[14]}

### Data

^{[15]}

### Instrumental variables

In many econometric contexts, the commonly used

^{[16]}

### Computational methods

^{[19]}

### Structural econometrics

Structural econometrics extends the ability of researchers to analyze data by using economic models as the lens through which to view the data. The benefit of this approach is that, provided that counter-factual analyses take an agent's re-optimization into account, any policy recommendations will not be subject to the Lucas critique. Structural econometric analyses begin with an economic model that captures the salient features of the agents under investigation. The researcher then searches for parameters of the model that match the outputs of the model to the data.

One example is

^{[20]}The second bypasses the full solution of the model and estimates models in two stages, allowing the researcher to consider more complicated models with strategic interactions and multiple equilibria.

^{[21]}

Another example of structural econometrics is in the estimation of first-price sealed-bid auctions with independent private values.^{[22]} The key difficulty with bidding data from these auctions is that bids only partially reveal information on the underlying valuations, bids shade the underlying valuations. One would like to estimate these valuations in order to understand the magnitude of profits each bidder makes. More importantly, it is necessary to have the valuation distribution in hand to engage in mechanism design. In a first price sealed bid auction the expected payoff of a bidder is given by:

where v is the bidder valuation, b is the bid. The optimal bid solves a first order condition:

which can be re-arranged to yield the following equation for

Notice that the probability that a bid wins an auction can be estimated from a data set of completed auctions, where all bids are observed. This can be done using simple

## References

**.****^**Engel, Ernst (1857). "Die Productions-und Consumptionsverhältnisse des Königreichs Sächsen".*Zeitschrift des Statischen Bureaus des Königlich Söchsischen Ministeriums des Inneren*(in German) (8, 9).- ^
^{a}^{b}Reiss & Wolak 2007, p. 4282. **^**Reiss & Wolak 2007, p. 4288.**^**Christ, Carl F. 1994. “The Cowles Commission Contributions to Econometrics at Chicago: 1939–1955”*Journal of Economic Literature*. Vol. 32.**^**Sims, Christopher (1980) Macroeconomics and Reality,*Econometrica*, January, pp. 1-48.**^**Kydland, Finn E & Prescott, Edward C, 1991. " The Econometrics of the General Equilibrium Approach to Business Cycles,"*Scandinavian Journal of Economics*, Blackwell Publishing, 93 (2), 161–178.**^**Angrist, J. D., & Pischke, J.-S. (2009).*Mostly harmless econometrics: An empiricist's companion*. Princeton: Princeton University Press.**^**Hoover, Kevin D. (2006). Chapter 2, "The Methodology of Econometrics." in T. C. Mills and K. Patterson, ed.,*Palgrave Handbook of Econometrics*, v. 1,*Econometric Theory*, pp. 61-87.- ISBN 978-1-111-53104-1.
**^**Herman O. Wold (1969). "Econometrics as Pioneering in Nonexperimental Model Building,"*Econometrica*, 37(3), pp. 369-381.**^**For an overview of a linear implementation of this framework, see linear regression.**^**Edward E. Leamer (2008). "specification problems in econometrics,"*The New Palgrave Dictionary of Economics*. Abstract.**^**• H. Wold 1954. "Causality and Econometrics,"*Econometrica*, 22(2), p p. 162-177.

• Kevin D. Hoover (2008). "causality in economics and econometrics,"*The New Palgrave Dictionary of Economics*, 2nd Edition. Abstract and galley proof.**^**Davies, A., 2006. A framework for decomposing shocks and measuring volatilities derived from multi-dimensional panel data of survey forecasts. International Journal of Forecasting, 22(2): 373-393.**^**Peter Kennedy (economist) (2003).*A Guide to Econometrics*, 5th ed. Description Archived 2012-10-11 at the Wayback Machine, preview, and TOC Archived 2012-10-11 at the Wayback Machine, ch. 9, 10, 13, and 18.**^**• Keisuke Hirano (2008). "decision theory in econometrics,"*The New Palgrave Dictionary of Economics*, 2nd Edition. Abstract.

• James O. Berger (2008). "statistical decision theory,"*The New Palgrave Dictionary of Economics*, 2nd Edition. Abstract.**^**B. D. McCullough and H. D. Vinod (1999). "The Numerical Reliability of Econometric Software,"*Journal of Economic Literature*, 37(2), pp. 633-665.- Ray C. Fair (1996). "Computational Methods for Macroeconometric Models,"-169.
*Handbook of Computational Economics*, v. 1, pp. [1] **.****.****.**

**
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*The Limits of Econometrics*. Aldershot: Edward Elgar. - Davis, George C. (2000) “A Semantic Conception of Haavelmo’s Structure of Econometrics”,
*Economics and Philosophy*, 16(2), 205–28. - Davis, George (2005) “Clarifying the ‘Puzzle’ Between Textbook and LSE Approaches to Econometrics: A Comment on Cook’s Kuhnian Perspective on Econometric Modelling”,
*Journal of Economic Methodology* - Epstein, Roy J. (1987)
*A History of Econometrics*. Amsterdam: North-Holland. - Fisher, I. (1933) “Statistics in the Service of Economics,”
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- Hoover, Kevin D. (ed.) (1995c)
*Macroeconometrics: Developments, Tensions, and Prospects*. Dordrecht: Kluwer. - Hoover, Kevin D. “The Methodology of Econometrics,” revised 15 February 2005
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*American Economic Review*73(1), 31-43. - Mizon, Grayham E. (1995) “Progressive Modelling of Economic Time Series: The LSE Methodology,” in Hoover (1995c), pp. 107–170.
- ISBN 978-0-521-37398-2.
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*Handbook of Econometrics*. Elsevier.ISSN 1573-4412. - Spanos, Aris. (1986)
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