Macroeconomic Performance and its Impact to Container Throughput at Port of Colombo

Services for freight transport have become an increasingly important aspect of global trade. The transport of freight cargo as a result of the shipping industry has very unique characteristics. The creation of container throughput volumes for this research is described in the sense of macroeconomic effect variables. This research will lead to the understanding of how the economic variables of Sri Lanka affect the volume rate of container throughput at Port of Colombo. The study question answered using hypothesis testing using data obtained from the Department of Census and Estimates, Central Bank Reports and Port Annual Reports, and the data is evaluated using a multiple linear regression model of E-Views. There is a linear relation with the defined economic variables for container throughput capacity, except for the inflation rate. There is also a clear correlation, except for the rate of inflation, between the economic variables and all the response variables. At Colombo Port, the Number of Imports & Exports of Sri Lanka were the most explanatory variables for container throughput ratios, where it is seen that Number of exports has a greater effect compared to the other macro-economic variables. In a more macroeconomic context, this analysis adds to the interpretation of container throughput volume trends. Further studies should concentrate on the evolution of broader datasets.


Introduction
Efficient logistics management and the full use of available services to increase container throughput performance are the key objectives of any port. Growing the port throughput capacity, which is mentioned, is also contributing to sustaining the economy of the country positively. At the centre of commercial activity, ports have always been. It is primarily dependent on the quantity of port throughput over a given span of time. Port throughput is known as the average amount of cargo containers that may move through a port on a regular basis when a ship is loaded into the port or discharged from the ship for clearance. Throughput is commonly expressed in tons of measurements.
Sri Lankan macro-economic factors mostly affecting to the container throughput volumes at Colombo Port. In this research mainly focusing on analysing the most affective macro-economic factor which are identified by literatures (Jung (2011) Hao & Xiaohong (2008) , Inland Navigation Market, (2017)) the to the container throughput volume and the relationship between all the macro economic variables and container throughputs at Colombo Port. After analysing the macroeconomic factors, the ports throughput, it can provide information to both private sector and government officers for planning & managing their future development. The results can be providing as a support for port business investment, decision making and risk control, and also can provide assistance for port enterprises or other researchers.

 Research Problem
There has no particular study on calculating container throughput at Colombo port. Therefore, this study can be refer to the future researches with the relevant information.
 Objectives  Primary -Identifying the factors and measures which impact economically on Colombo Port throughput  Secondary -Determine the relationship between macro-economic variables and Colombo Port throughput levels and identifying which variable has the greatest impact on Container Throughput Level at Colombo Port.

Methodology
Augmanted dickey fuller test and Phillips Perron test will be applied in order to check the stationarity of the variables. Stationarity of the data is important for forecasting. Also checking for stationarity, unit root testing has been carried out prior to modeling. In order to make accurate predictions, unit root testing will provide guide to build the models (Mudunkotuwa, 2015) Yt is level and ∆Yt is first difference time series. T is time in year. α is the intercept constant. β is the coefficient on the time period. γ is the coefficient presenting root. p is the lag order of first difference autoregressive process. α ,β, γ are parameters which are estimated.
The hypothesis of stationary test is as followed; H0: variable has a unit root (non-stationary) H1: variable has no unit root (stationary) Co-integration is a statistical property generally applies for set of stationary series. When the observed series are stationary at the first difference, it is said to be that the series are integrated of order one. That is series are in I(1). Once a unit root has been confirmed for all data series as stationary, it is required to test whether there is any possibility for the existence of a long run equilibrium relationship among a given set of variables. In this aspect it is required to find the lag period Johansen's cointegration test is very sensitive to the choice of optimal lag length. Thereafter, the sequential modified likelihood ratio test statistics is used to select the number of lags required in the cointegration test (Mudunkotuwa & Karunaratne, 2017). Hathurusingha & Mudunkotuwa (2015) have cited that Granger Causality is a technique based on predictions rather than causation and was developed by Prof. Clive Granger (1969) to determining the causal relationship between two variables. This concept is used to determine whether one variable can be used to predict the other and if so, what the direction of that relationship is. Ordinarily regressions mirror "mere" co-relations. According to Poof.Clive Granger's definition (1969), "a variable Y is Causal for another Variable X if knowledge of the past history of Y is useful for predicting the future state of X over above knowledge of the past history of X itself. So if the prediction of X is improved by including Y as a predictor, then Y is said to be Granger causal for X.".
Collecting data using secondary data collection method. Data from sources such as the Department of Census and Statistics, Sri Lanka Central Bank Study Publications, Port Annual Accounts, related articles, newspapers, blogs, conference papers, thesis papers, academic papers and literature books have been collected for this research. I was collected statistical data from Mr.Upul Jayatissa, who is a Managing Director at Sri Lanka Ports Authority. And refer the SLPA annual reports. For the research to be carried out, 20 years of data are gathered in order to establish the relationship between variables. Using a sample size of 20 years. The 10year timeframe for this analysis is the period from 2000 to 2019, the most recent data available.

OLS Model
When considering about the OLS Model Results, there is only number of exports has a significant P-value. According to that number of exports which is greatly affect to the Container Throughput of Colombo Port in short run.

Discussion of Findings
Augmented root analysis of the Dickey Fuller (ADF) unit was used in the report. As more variables at the first discrepancy are stationary, they can be cointegrated to have a long-term relationship. All theI (1) variables have also been added to assess their long-term relationship. As there are several factors, through applying the temporal disaggregation process, researchers interpolated the low frequency of annual data to the high frequency of quarterly data. The Cointegration Equation