ROMANIA’S LONG-TERM GROWTH PERSPECTIVES

Abstract

In a world of sustainable development, affected by a certain short-term liquidity distress, the economic actors are facing uncertainty since inadequate resource management and decision making can have significant effects over a country’s future economic and societal development. Therefore, the stakeholders involved in the decision-making process consider economic forecasts, based on variables that factor in recent developments, to aid them in the aforementioned process. If they did not, they would not have a greater understanding of a country’s performance indicators. Using past data, a set of analyses are performed, and economic models are built, with the scope of probabilistically determining Gross Domestic Product (GDP). The main objectives of this paper are thus, to assess Romania’s development since joining the European Union (EU) and provide a long-term macroeconomic projection, together with a comparison between the forecasted data and the country’s Sustainable Development Goals (SDGs) in order to realistically estimate their achievability. Furthermore, a set of scenarios have been built that aim at creating a clearer perspective of how the recent global pandemic might affect Romania. The data sets have been taken from Eurostat, the International Monetary Fund, World Bank, European Commission, National Bank of Romania and the National Institute of Statistics; and a set of statistical tools have been used to fulfill the previously stated objectives, namely; Auto Regressive Moving Average (ARMA) for the short-term forecasts and Variable Autoregression (VAR) for the long-term models. The main results suggest a negative GDP growth of 4.95% for 2020 followed by a strong rebound in 2021. However, this figure will drastically be affected by how well or poorly the situation is managed. The European Commission anticipates a downturn of 6% in 2020 and a rebound 4.2% in the following year. This paper includes a sensitivity analysis as well, that attempts to eliminate potential forecasting biases.

JEL Classification: E17, C53