The aim of the study is to examine the main factors driving economic growth in the CEE-4 countries since the transition with the main focus on macroeconomic policies and institutions. The building of a market economy in the region required deep macroeconomic reforms and the creation of a wide range of institutions and business practices needed to support those reforms. Since the collapse of communist regimes, the CEE-4 countries have adopted in the early 1990s a set of policy principles focused on fiscal discipline, interest rate liberalisation, trade and financial liberalisation, privatisation, deregulation and openness to direct foreign investment. Macroeconomic stability by itself, however, does not ensure high rates of GDP growth. In most cases, sustained high rates of growth also depend upon key structural measures, such as regulatory reform, civil service reform, improved governance, and banking sector reform. Institutions of central planning in the CEE-4 region were one of the key barriers to growth prior to the transition. As the development of institutions has been necessary to support the well-functioning market economies in the CEE-4 region, the study also examines deep factors of production – institutions – in addition to the demand-side and the supply-side factors affecting output.
This study examines major drivers behind horticultural exports in Kenya for the period 2005-2017. Using co-integration model, the study finds out horticultural exports, interest rate, exchange rate, and inflation rate are co-integrated in long-run. These co-integrated series converge to their long-run equilibrium at a speed of 8.53% on each period at 1% statistically significance level. More specifically, the study explores that the interest rate has negative influence on horticultural exports of Kenya, while inflation and exchange rates have positive impact. Thus, the study recommends that the government in Kenya should reduce interest rates using their monetary policies and stabilize macroeconomic environment in order to increase horticultural exports such as targeted exchange rate through application of foreign reserves adjustments.
This paper examines relationship of unemployment rates with other macroeconomic aggregates in Bangladesh over 1991-2019 using robust econometric analyses. It sheds a light on the fact that GDP growth rate, inflation, and foreign direct investment flows have statistically significant impacts on unemployment rate both in short-run and long-run. More specifically, the paper documents that unemployment rate, GDP growth rate, inflation rate and foreign direct investment flows are co-integrated in long-run at 5% significance level.
Using Vector Error Correction analysis, the paper finds that co-integrated series converge it their long-run equilbruim at a speed of 17.24% per annum at 1% significance level. In case short-run, the study finds that a unit increase in GDP growth rate decreases unemployment by approximately 0.0159 units in short-run at 1% statistically significance level. Likewise, a unit increase in inflation rate will lead approximately 0.004 units drop in unemployment rate at 10% significance level. Plus, it also observes that a unit in Foreign Direct Investment flows causes 0.005 units decrease in unemployment rate in short-run at 5% significance level.
Governments need accurate tax revenue forecast figures for good economic planning but there seems to be no consensus on which method is the most suitable to deliver reliable results leading to differences in the choice of technique from one country to another. This study therefore forecasts Ghana’s Value Added Tax (VAT) Revenue by comparing two methods, ARIMA with Intervention and Holt linear trend methods to establish the one with more precise predictive powers for VAT Revenue. Monthly VAT revenue data from the year 2002 to 2019 is used in the analysis. The findings show that ARIMA with Intervention method outperformed the Holt linear trend model in terms of accuracy and precision. A comparison of predicted results from the ARIMA with intervention model from 2017 to 2019 with Ghana Revenue Authority’s VAT revenue targets based on their in-house forecasting model for the same period reveals that the ARIMA with intervention approach performs better than the in-house forecasting model of the VAT authority. In this case, the study recommends the ARIMA with intervention method to the tax authority for consideration in its forecasting.
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