Data on the the macroeconomic effects of COVID-19 in Montenegro, using a Bayesian VAR approach

This fileset contains two data files:These are: <b>22927448_new macro-model.wf1</b> in .wf1 file format, and the openly accessible versioin of the dataset, <b>Time series and scenarios.xlsx </b>in .xlsx file format.<br>The dataset Time series and scenarios.xlsx contains observations on what happens to GDP, capital stock, human capital, and employment, in three economic scenarios – shocks – of how the pandemic disease might impact the economy of Montenegro in the dawn of entering the European Union. The authors forecast a sustainable GDP growth model from January 2006 until December 2017. Deterministic-static simulation solution model – the baseline – was employed, adding sensitivity scenarios – shocks – from January 2018 until Jun 2018, respectively, from -10% until -60%.<br> The following time series data are included: GDP_GAP (The GDP GAP is defined as the difference between potential GDP and real GDP), EMP_SA (denotes seasonally adjusted employment time series), CAPITAL_STOCK_SA (denotes seasonally adjusted capital stock time series), LOGHUMCAP_SA (denotes the natural logarithm of human capital seasonally adjusted time series).<br>In each spreadsheet, S1, S2 and S3 represent the three different scenarios.Scenario 1 represents the employment decrease, scenario 2 represents the capital decrease, and scenario 3 represents the human capital decrease.<br><b>Study aims and methodology</b>: A limited number of studies have examined the potential consequences of a pandemic such as COVID-19, on the economy of Montenegro. The objective of this paper is to fill this space by examining the macroeconomic effects of COVID-19, employing monthly data from January 2006 until 2017, and out-of-sample data from January 2018 until December 2018, predicting the movement of macro-model variables.In this study, the authors measured the COVID-19 impact for the first time in the Montenegrin economy using a Bayesian vector autoregressive (VAR) and forecasting sensitivity deterministic-static scenario model. They applied alternative forecasting scenarios to all the macroeconomic variables. For more details on the methodology, please read the related article.<br><b>Software needed to access data</b>: The WF1 file type is primarily associated with EViews Workfile. (Econometric software). A suitable software like EViews Workfile, is needed to open a WF1 file.<br>本数据集包含两份数据文件：分别为格式为.wf1的<b>22927448_new macro-model.wf1</b>，以及可公开获取的数据集版本<b>Time series and scenarios.xlsx</b>（格式为.xlsx）。<br>数据集<b>Time series and scenarios.xlsx</b>收录了三类经济冲击情景下的观测数据，用以模拟新冠肺炎疫情在黑山即将加入欧盟的关键时期对其经济造成的影响，涉及国内生产总值（GDP）、资本存量、人力资本与就业四项指标。研究团队构建了2006年1月至2017年12月的可持续GDP增长预测模型，采用确定性静态模拟求解模型作为基准情景，并于2018年1月至6月期间增设了降幅从-10%至-60%不等的敏感性冲击情景。<br>本次数据集包含如下时间序列指标：GDP_GAP（即潜在国内生产总值与实际国内生产总值的差值，简称GDP缺口）、EMP_SA（经季节调整的就业时间序列）、CAPITAL_STOCK_SA（经季节调整的资本存量时间序列）以及LOGHUMCAP_SA（经季节调整的人力资本自然对数时间序列）。<br>在每份电子表格中，S1、S2与S3分别对应三类不同情景：情景1为就业下滑情景，情景2为资本存量下滑情景，情景3为人力资本下滑情景。<br><b>研究目标与方法论</b>：目前针对新冠肺炎疫情这类突发公共卫生事件对黑山经济造成的潜在影响，相关研究尚不多见。本研究旨在填补这一研究空白，通过采用2006年1月至2017年的月度数据，以及2018年1月至12月的外样本数据，预测宏观模型变量的变动趋势，以分析新冠肺炎疫情对黑山经济产生的宏观影响。本研究首次采用贝叶斯向量自回归（VAR）模型与敏感性确定性静态情景预测模型，量化评估了新冠肺炎疫情对黑山经济的影响。研究团队对所有宏观经济变量设置了多组备选预测情景，如需了解方法论的更多细节，请参阅相关研究论文。<br><b>数据访问所需软件</b>：.wf1格式文件主要关联EViews工作簿（EViews Workfile，一款计量经济学软件），需使用EViews工作簿等适配软件方可打开.wf1格式文件。