Dynamic Modelling

The econometric model that is used to interpret the relationship between the leverage of

UK companies and each variable is GMM dynamic panel data estimation (Arellano and

Bond, 1991). Data that contain time series data, which contain one or more lagged values

of the dependent variable amongst the explanatory variables is called an autoregressive

model, an alternative name is dynamic model (Gujarati and Porter, 2009). The ability of

the model to “portray the time path of the dependent variable in relation to its past

value(s)” (Gujarati and Porter, 2009:617) is a key advantage of this technique. 4.7.1 Generalised method of moments (GMM)

The classical theory of the method of moments was started by Fisher in 1935, the use of

sample moments as part of estimating equations are a fundamental part of econometrics

(Greene, 2011). GMM is an extension of the method of moments (MM), one example of

MM is OLS, and the estimators are derived from the so-called moment conditions. The

number of assumptions required to produce estimators are less in comparison to the OLS

model, the estimates that are generated are more robust but less efficient. In GMM there

are more sample moment conditions than parameters, in MM the number of sample

conditions and parameters are the same. GMM reduces to MM in situations whereby the

number of parameters equals the number of moment conditions. GMM estimators choose

the estimates that minimise the quadratic form of the moment conditions (Greene, 2011).

GMM provides an estimation framework that includes least squares, nonlinear least

squares, instrumental variables and maximum likelihood (Greene, 2011). GMM is more

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without the use of strong assumptions. The use of additional information that is beyond

what is necessary to identify the model is a key advantage, with some critics stating

GMM is too relaxed; however, if used in the correct model it can enable the incorporation

of more variables. GMM is able to overcome the issues surrounding the FEM; these

include controlling for the presence of unobserved company specific effects, and the

endogeneity of the explanatory variables.

The GMM technique has been used in previous corporate governance research (Conyon

and Peck, 1998) and in econometric analysis; it has the benefits of taking account of the

lag adjustments in companies leverage over time. Firstly, GMM is able to control for

variable simultaneity and unobserved heterogeneity. Ozkan (2001) find that GMM is

able to deal with highly persistent data, such as ownership data, one variable that is

included in this study. Secondly, GMM allows for control for the firm-specific fixed

effects, which are unobservable, yet can affect a company’s financial decisions. Thirdly,

the endogeneity issue can be overcome through choosing more efficient instruments

(Ozkan, 2001).

Dynamic equation:

Y_it = α + 𝛽 ₁X_it + ᵧY_t−1 +µ _i

The relationship between the dependent variable (Y) and independent variable (X) is

rarely instantaneous (Gujarati and Porter, 2009). The dependent variable responds to the

independent variable with a time lapse, called a lag. There are three main reasons for

why the lagged phenomena occur. Firstly, psychological reasons. People do not change

their habits immediately following a price change. For example, companies may not

respond immediately to corporate governance guidelines suggested by The Code

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transitory, studies over a long period of time are able to identify between the two. For

example, an increase in the percentage of debt in the capital structure will depend upon

whether the increase is permanent or not. If it is a nonrecurring increase, future periods

may see debt levels return to their previous level. The second reason for lags is for

technological reasons; imperfect knowledge is a key driver behind why lags occur. If a

change is expected to be temporary, companies will be unlikely to respond to the change.

Particularly if it is expected that after the temporary change, a further drop is expected

that reduces it below its previous level. This reason is linked to expectation theory,

encouraging a delay in behaviour due to companies hesitating when making decisions

surrounding their capital structure. The last reason is for institutional reasons, covenants

can be attached to loans which may prevent companies from changing the percentage of

debt for a period of time, once the covenant has expired, the company is no longer

‘locked into’ a particular type of debt or equity. Similarly, with regard to equity, shareholders may prevent future issues of shares in a financial year to avoid diluting their

ownership (Gujarati and Porter, 2009).

The length of the lag is defined by how far back into the past the model will go. There

are two types; infinite model and finite (lag) distributed-lag model (shown below), in this

study the time period will be specified and the second model will be used.

Yit = α + β0 Xt + β1Xt−1+ β2Xt−2+. .. +µ it

Endogeneity can present two key issues in analysing corporate governance panel data

(Wintoki et al., 2012). Firstly, unobserved heterogeneity can be present; this can occur

when capital structure and a specific corporate governance variable are jointly determined

by a company specific variable that is observed. Secondly, when a specific corporate

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simultaneous endogeneity. Eisenberg et al. (1998) employ instrumental variables to

overcome these issues; defining the instrumental variable is difficult in practise (Wintoki

et al., 2012). The use of FEM in the area of corporate governance can lead to bias; therefore, Wintoki et al. (2012) suggest the use of the GMM model.

Ozkan (2001) uses GMM in a study of UK companies, and finds that GMM can be used

to estimate a dynamic capital structure model. However, GMM only provides consistent

estimates if valid instruments are set up and used. Ozkan (2001) finds evidence of a

partial adjustment mechanism; companies adjust their leverage levels to move towards a

target level.