relationships among variables and facilitates hypothesis testing using econometric techniques. The study
utilises annual secondary data covering the period from 1970 to 2018, obtained from the World Bank database,
as reported in the attached paper. The long observation period allows for meaningful analysis of structural
changes and macroeconomic fluctuations in Malaysia. Imports are specified as the dependent variable,
reflecting Malaysia’s trade behaviour and external demand conditions. Inflation is measured using the
Consumer Price Index (CPI), while economic growth is proxied by Gross Domestic Product (GDP). These
variables are commonly employed in empirical studies on inflation and trade dynamics (Corrigan, 2005;
McCarthy, 2007; Liu & Chen, 2017). All variables are transformed into natural logarithms to stabilise variance
and allow coefficient estimates to be interpreted as elasticities. While more advanced time-series techniques,
such as cointegration or ARDL models, may provide insights into long-run dynamics, this study adopts the
Ordinary Least Squares (OLS) approach after first differencing to ensure stationarity and avoid spurious
regression. Given the study’s primary objective of examining the direction and significance of relationships
between inflation, economic growth, and imports, OLS remains an appropriate and widely used method in
similar empirical studies (Corrigan, 2005; McCarthy, 2007). The limitation regarding long-run inference is
acknowledged and discussed in the final section of the paper.
Econometric Model Specification
To examine the effects of inflation and economic growth on imports, the study specifies the following
Ordinary Least Squares (OLS) regression model:
ln 푀푡 = 훽0 + 훽1ln퐺퐷푃푡 + 훽2ln 퐶푃퐼푡 + 휀푡
where ln 푀푡represents imports, ln 퐺퐷푃푡denotes economic growth, ln 퐶푃퐼푡captures inflation, and 휀푡is the error
term. This specification is consistent with earlier empirical work analysing the impact of price and income
variables on trade flows (Corrigan, 2005; McCarthy, 2007). The estimated coefficients 훽1and 훽2correspond
directly to the study’s hypotheses. A statistically significant 훽2supports H1, indicating a relationship between
inflation and imports, while a statistically significant 훽1supports H2, confirming the relationship between
economic growth and imports.
Prior to estimation, the stationarity properties of the variables are examined using the Augmented Dickey–
Fuller (ADF) unit root test. Stationarity testing is essential to avoid spurious regression results in time-series
analysis. The ADF test is conducted under both intercept and intercept-with-trend specifications, consistent
with standard econometric practice. Variables found to be non-stationary at their levels differ until stationarity
is achieved, as in similar empirical studies reported in the attached paper. To ensure the reliability and validity
of the estimated OLS model, several diagnostic tests are performed. The Breusch–Godfrey Serial Correlation
LM test is applied to detect autocorrelation in the residuals, while the heteroskedasticity test is used to assess
the constancy of error variance. These diagnostic procedures are necessary to confirm that the model satisfies
classical regression assumptions and to ensure accurate statistical inference (McCarthy, 2007).
The methodological framework is explicitly designed to test the hypotheses derived from the Introduction and
Literature Review. By estimating the effects of inflation (CPI) and economic growth (GDP) on imports using
OLS regression, the study directly assesses whether these variables significantly influence import behaviour in
Malaysia. The use of long-term time-series data further strengthens the robustness of the findings and ensures
consistency with prior empirical research (Corrigan, 2005; Liu & Chen, 2017).
FINDINGS AND DISCUSSION
Empirical Findings
This study employs Ordinary Least Squares (OLS) to examine the relationship among inflation, economic
growth, and imports in Malaysia over the period 1970–2018. Prior to estimation, the Augmented Dickey–
Fuller (ADF) unit root tests confirm that all variables are stationary after first differencing, ensuring that the
regression results are not spurious and are suitable for empirical inference. The OLS regression results are
reported in Table 1, in which imports are the dependent variable and inflation (CPI) and economic growth
(GDP) are the independent variables.
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