An Econometric Analysis of Exigent Determinants of Trade Balance in Finland: An Autoregressive Distributed Lag (ARDL) Approach

Abstract

A number of research papers analyzed the factors that may have impacts on the balance of trade for the effective macroeconomic policies but the results of these studies have created ambiguity which implies that further research is needed as the worsening trade balance can limit the economic growth of any country. Hence the current paper is an effort to study the short-run and long-run relationships among trade balance, real effective exchange rate, GDP per capita, urbanization, unemployment and inflation rate for the Finnish economy. Autoregressive Distributed Lag (ARDL) bound testing methodology to co-integration along with error correction mechanism is applied by using time series data from 1990 to 2019 for checking the existence of long-run equilibrium between explained variable and its various determinants. Empirical findings of ARDL show that the long-run relationship among the factors of the estimated model holds. The results indicate that real effective exchange rate, urbanization and inflation are having a significant but negative impact on Finland’s trade balance for both the short-run and long-run while the impacts of GDP per capita and unemployment are significant and positive on the Finish balance of trade. In addition to above, the structural stability of mean and variance of the error term for the estimated ARDL model is verified with the help of CUSUM and CUSUM square graphs. Therefore, a reliable policy measure to improve the balance of trade by encouraging the domestic production and curtailing the imports is suggested in Finland.

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“…The dependent variable data in this research is the level of CPO export. Testing the estimation of the ARDL and ECM models analyzes the long-term and short-term equations between the relationship between the independent and dependent variables [2], [10], [16]. Before determining the selection of ARDL and ECM models, a step testing process is needed, namely the unit root test (stationarity) [11].…”

“…The spurious regression model in the time series analysis equation is a type of regression that looks at the relationship and influence between the dependent and independent variables, which shows significant results in terms of probability, but the magnitude value diagnosis does not display a regression coefficient that matches the residual value [2], [10], [11]. The step to avoid the problem of coaxing correlation in estimating the variables to be studied is through a stationarity test (unit root test) which is used to estimate whether the regression is stationary or not.…”

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“…Another study [17] focused on analyzing factors that affect Finland's trade balance using the ARDL methodology, aiming to investigate the short-term and long-term relationships between the trade balance, real effective exchange rate, GDP per capita, urbanization, unemployment, and inflation rate in the Finnish economy. It was found that the real effective exchange rate, urbanization, and inflation significantly and negatively impact Finland's trade balance in both the short and long term.…”

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“…The empirical findings of the study of Ditta et al (2020) on the determinants of Finland's trade balance showed that the real effective exchange rate, urbanization, and inflation have a significant but negative impact on Finland's trade balance in both the short and the long run, while GDP per capita and unemployment have a significant but positive impact. The absence of any structural break in the model is confirmed by the plots of recursive estimates on both CUSUM and CUSUM square.…”