Method to examine the role of determinants in affecting the current account balance of India?
The macroeconomic condition of India reveals that the current account balance of India is in deficit due to the existence of a trade imbalance. As the government is formulating policies to target the economic performance of the country, there is a need to identify the determinants that influence the current account balance of India.
The overall aim of this study is to identify the determinant’s role that affects the current account balance of India. The variables representing the economic status of India have been identified in the previous article. This article explains the ideal methodology to statistically examine the determinants of the current account balance of India.
The research strategy for estimating the current account balance
The country’s economic progress may benefit from the current account deficit. While in other countries current account deficits may indicate a greater reliance on borrowing. So demonstrating its incapability to carry out tasks and produce economic value. The relationship between the current account deficit and other economic elements must be understood in order to manage the current account deficit’s contribution to the economy.
Hence, in this case, understanding data mobility is the first step in learning about the whole situation. This is followed by statistical analysis to identify the factors affecting the balance. Secondary data could be gathered using a quantitative research methodology in order to assess the effects and identify the elements that contribute to India’s current account deficit.
Variables that indicate the current account balance of India
The main variables in the study are the factors taken into consideration and the current account deficit for India because the goal of the study is to ascertain how various factors affect the current account deficit. While the impact needs to be assessed on the current account deficit, which is the dependent variable, all the factors that are included in this analysis are the independent variables.
The previous article identified that factors like GDP, private investment, trade openness, fiscal deficit, broad money supply, external debt, trade balance, government expenditure, FDI, and foreign exchange reserve; affect the current account deficit. Thus, these variables will be the independent variables of the study.
Hypothesis
The purpose of the study is to determine the impact of factors on the current account deficit, thus, based on this aim, the hypothesis can be:
H0: Factors have no significant role in influencing the current account balance in India
HA: Factors have a significant role in influencing the current account balance in India
Taking the period from 1993-2021
The necessity of studying recent data is necessary in order to develop significant conclusions. The dataset chosen in this study is from 1993 to 2021. The linkage-building model also needs the existence of more than 10 years of data to generate significant linkages between variables.
The evaluation of this time period includes both the period when India’s emphasis on exports and commerce rose and the period during which tracking the global recession is possible. As a result, the movement of the current account balance throughout the period 1993–2021 may be easily tracked, together with the composition of other economic elements over time.
As the study is based on examining the economic factors that impact the current account deficit of India, thus, herein secondary data courses are selected. Majorly in India, the data is published on government websites like the Reserve bank of India or India.gov.in. Thus, the data is derived from open databases like World Bank, India.gov.in, and RBI. From 1 April 1993 to 31 March 2021, annual data for the period 1993–2021 are gathered. In this, annual data is gathered from public databases based on fiscal years.
Methodology to identify the role of factors
Analyzing the flow of data is the first step in the analysis of the Indian economy’s data. Here, based on the data gathered, trend analysis—a graphical analysis technique—is used to calculate the economic variation of all the elements. Following the observation of the movement and the extraction of basic data information, the data may be statistically examined in order to establish a relationship between the variables. A time series analysis would be performed in this case to evaluate the linkage. STATA would be used as the analytic tool for looking at the data.
Step 1: Overview of the data using trend analysis, where all economic variable movements are analysed graphically and the factors or economic components that had an impact on the trend are identified.
Step 2: Before incorporating the linkage between variables, assumption testing would be carried out. The occurrence of non-uniformity and inconsistency in the secondary data under consideration for the analysis raises the possibility of a failure in the linkage formation between variables. The essential assumptions of stationarity, normality, heteroskedasticity, multicollinearity, and autocorrelation are assessed at a 5% level of significance in order to address this problem.
Step 3: The OLS (Ordinary least square) regression model could be used to establish a connection between variables and the current account balance after each variable’s effectiveness has been evaluated. The model would be useful in determining how various factors contributed. A 5% threshold of significance would be used to test the hypothesis in this case.
Step 4: Finally, the assumption issue would be solved using a GLM (Generalized Linear Model). As a result, the model aids in reducing the inefficiency existing in variables, allowing for the development of a more effective link between components and current account balance. Here, a 5% threshold of significance would be used for the hypothesis testing to increase the effectiveness of the linkage results.
Here, the emphasis is on performing statistical analysis for linkage development utilising regression models in addition to visual assessment of secondary data. The goal of the chosen analysis techniques is
| Tests | Purpose |
|---|---|
| Trend analysis | To track the movement in economic factors over the time period of 1993-2021 |
| Assumption testing | To verify that the selected variables are effective for impact assessment model building |
| OLS model | To examine the impact of factors on the current account balance for the effective data fulfilling all assumptions |
| GLM model | To assess the impact of factors on the current account balance for the data not fulfilling all assumptions |
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I am a Senior Analyst at Project Guru, a research and analytics firm based in Gurugram since 2012. I hold a master’s degree in economics from Amity University (2019). Over 4 years, I have worked on worked on various research projects using a range of research tools like SPSS, STATA, VOSViewer, Python, EVIEWS, and NVIVO. My core strength lies in data analysis related to Economics, Accounting, and Financial Management fields.
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