| 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 |