Methods Used in the Study of Stock Market Development and

The review of literature on stock market development and economic growth relationship in both developed and developing countries has shed some light on

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Identify the issue of questions of interest

Review relevant literature and theories

Develop questions and hypothesis

Identify independent and dependent variables

Accept or reject hypotheses

Use inferential statistics to evaluate statistical hypotheses Use descriptive statistics to describe data

Conduct the study

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the link between stock market and economic growth. This section, however, specifies the methodology applied to find the relationship between stock market and economic growth in Nigeria.

The bulk of the studies which investigated the stock market and economic growth relationship in a single country used some form of time-series study or other. This research examines the relationship between stock market development and economic growth over time for a single country (Nigeria), similar to the studies explored in the literature and as such will use time-series study, as it is a suitable tool to answer the research question. In addition, it is an accepted method within the literature for this kind of investigation. Authors such as Bahadur and Neupane (2006) as well as Tuchinda (2011) have also used this time series method in their studies of the stock market development and economic growth relationship in different single country studies, with a high degree of success and reliability in their result.

It is expected that this method will give insights into the relationships between the variables and how they behave as a system. In the present analysis, this method is particularly relevant because the research aims to investigate not only the strength but also the direction of any such relationships. In the past, there have been several time-series methods utilised within the literatures, as identified from the review of single country studies on stock market and economic growth relationship in Chapter 3. The methods include: correlation analysis (Oke and Mokuolu 2005), ordinary least square (OLS) test (Osinubi and Amaghionyeodiwe 2003, Azarmi et al 2005, Augustine and Salami 2010),

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autoregressive distributed lag (ARDL) tests (Ang 2008, Odhiambo 2010), co- integration and VECM (Riman, et al 2008, Nowbutsing and Odit 2009, Oskooe 2010) and Granger causality test (Bahadur and Neupane 2006, Antonios 2010, Tuchinda 2011).

The correlation analysis method is a simple yet useful method of detecting the trend in any dataset and, as it was used by Oke and Mokuolu, (2005), it provides a rich source of information. However, it has several flaws which limit its use; correlation analysis detects the trend in the movement between two variables but it does not provide any information about a relationship other than the indication of a possible relationship. It simply shows if the trend of the direction of movement of the variable in relation to the other variable. Any inference drawn from any such co- movement as a possible association between the variables may in most cases be spurious (misleading).

In the same vein, the ordinary least square (OLS) test method, as was utilised by Osinubi and Amaghionyeodiwe (2003), Azarmi et al (2005) and Augustine and Salami (2010), is quite useful in detecting a relationship, as well as the magnitude of any detected relationship; however, it requires an apriori specification of the direction of the relationship and thus it is subject to misspecification errors. It is not able to capture the direction of the relationships, as it examines only the relationship in one direction. Also, it is not possible to use this method if the variables are not stationary, as is the case with most financial data.

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The autoregressive distributed lag (ARDL) tests method, as utilised by Ang (2008) and Odhiambo (2010), is quite good for detecting the existence of a relationship and has very good small sample properties; however, its use becomes extremely complicated when the variables under analysis are integrated to order of 1 or above. Pesaran and Shin (1997) argue that, owing to this fundamental weakness in the traditional ARDL approach, it ceases to be applicable in the presence of integration in the data set of order 1 or higher. It is also not able to capture the direction of the relationships as it examines only the relationship in one direction. Consequently, the development of alternative estimation methods has been undertaken to mitigate against this problem; these include the Johansen (1991) method.

The Johansen and VECM method, like the ARDL method, is a type of co- integration analysis method. It was developed as an alternative estimation method. It avoids the flaws of the traditional ARDL method. According to Ang and McKibbin (2007), with the VAR it is easy to distinguish between short-run dynamics and long-run causality once the variables under analysis are co- integrated. The studies by Riman et al (2008), Nowbutsing and Odit (2009) as well as Oskooe (2010) utilised this method, with a good result in detecting the existence of a relationship between stock market and economic growth. This method has excellent small sample properties and it is possible to use it even where the variables are integrated of order 1. Also, where there is evidence of co-integration, it allows for the estimation of an error correction model.

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While the studies that are interested in investigating the direction of casualty have all used different variations of a vector autoregressive (VAR) models, the Granger causality test in particular, the Granger causality test method as used by Bahadur and Neupane (2006), Antonios (2010) as well as Tuchinda (2011) allows the researcher to test for the actual direction of a relationship between stock market and economic growth without having a prior specification in his/her model. Enisan and Olufisayo (2009) explain the Granger causality tests as being based on prediction and add that the concept of causality is statistical; it tests to see if one time series can be useful in predicting another time series data. According to Granger (1969), causality exists when past values of X1 can

predict (Granger-causes) present values of X2, better than past values of X2 and

vice versa. A major weakness of the other methods discussed is that these methods are unable to capture the direction of the relationships that existed between variables and that they rely heavily on prior specification of the direction of the relationship.

The objective of this study is to examine the stock market economic growth relationship, in particular, to check for the existence of a relationship and, where one exists, the nature of the long-run causality between stock market development and economic growth. Given the debate on the impact of stock market on economic growth in developing countries as well as the unresolved conclusions on the nature of causality in the time-series studies, this study applies the Johansen and VECM method and also uses the Granger causality test to examine the causality.

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Riman et al (2008) use a bivariate vector autoregression (VAR) to investigate the relationship between stock market development and economic growth for Nigeria. Their study does not control other factors within the economy that can impact on economic growth, leaving their study open to simultaneous bias (Gujarati (2004)). Although both studies are based on VAR models and examine the Nigerian economy, this study departs from this and other earlier works in Nigeria and thereby contributes to the knowledge by applying a multivariate VAR as such controls for other factors within the economy that might impact on economic growth, like the impact of government expenditure, openness of the economy, banking sector and capital stock. This approach minimises the omitted variable bias problem identified by Gujarati (2004) in VAR model specification.

This choice in method is similar to that of Tuchinda (2011), who examined the impact of stock market development on economic growth for Thailand. However, this study departs from their study by controlling a larger number of variables, as well as the longer time span of the data. The following hypothesis is tested in the co-integration analysis:

H1: There is no relationship between the stock market and economic growth in

Nigeria.

whereas the Granger causality analysis tests the two hypotheses

H2: The stock market development does not Granger cause economic growth in

Nigeria. and

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H3: Economic growth does not Granger cause the stock market development in

Nigeria.