dc.contributor.author |
Ateeb Akhter Shah Syed |
|
dc.contributor.author |
Hassan Raza |
|
dc.contributor.author |
Mohsin Waheed |
|
dc.date.accessioned |
2024-11-26T06:47:22Z |
|
dc.date.available |
2024-11-26T06:47:22Z |
|
dc.date.issued |
2023 |
|
dc.identifier.uri |
http://hdl.handle.net/123456789/17599 |
|
dc.description |
PP. 28. ill; |
en_US |
dc.description.abstract |
This paper introduces the monthly State Bank of Pakistan’s EasyData, for conducting empirical macroeconomic analysis and forecasting for Pakistan's economy. For this purpose. We perform a forecasting exercise using the conventional econometric models and the most recent machine-learning algorithms. We find that the machinelearning models outperform the benchmark and regression models based on observed factors. Furthermore, the dataset has a higher ability to predict the external variables, a possible outcome of Pakistan's economy and its persistent balance of payment problem. The focus of policy has been to address this issue. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
© Lahore School of Economics Vol.28, Issue 1, 2023 |
en_US |
dc.subject |
Research on Pakistan |
en_US |
dc.title |
Easydata-MD: A Monthly Dataset for Macroeconomic Research on Pakistan |
en_US |
dc.type |
Article |
en_US |