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

• 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.

Econometrics may use standard

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]

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

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:

${\displaystyle (v-b)\Pr(b\ {\textrm {wins}})}$

where v is the bidder valuation, b is the bid. The optimal bid ${\displaystyle b^{*}}$ solves a first order condition:

${\displaystyle (v-b^{*}){\frac {\partial \Pr(b^{*}\ {\textrm {wins}})}{\partial b}}-\Pr(b^{*}\ {\textrm {wins}})=0}$

which can be re-arranged to yield the following equation for ${\displaystyle v}$

${\displaystyle v=b^{*}+{\frac {\Pr(b^{*}\ {\textrm {wins}})}{\partial \Pr(b^{*}\ {\textrm {wins}})/\partial b}}}$

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