Macroeconomic forecasting for Pakistan in a data-rich environment

References

• Ahmad, S., and A. Haider. 2019. “An Evaluation of the Forecast Performance of DSGE and VAR Models : The Case of a Developing Country.” Business Review 14 (1): 28–52.
   Google Scholar
• Bai, J., and S. Ng. 2008. “Forecasting Economic Time Series Using Targeted Predictors.” Journal of Econometrics 146 (2): 304–317. doi:10.1016/j.jeconom.2008.08.010.
   Web of Science ®Google Scholar
• Bair, E., T. Hastie, D. Paul, and R. Tibshirani. 2006. “Prediction by Supervised Principal Components.” Journal of the American Statistical Association 101 (473): 119–137. doi:10.1198/016214505000000628.
   Web of Science ®Google Scholar
• Bernanke, B. S., J. Boivin, and P. Eliasz. 2005. “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach.” The Quarterly Journal of Economics 120 (1): 387–422. doi:10.1162/0033553053327452.
   Web of Science ®Google Scholar
• Boivin, J., and S. Ng. 2006. “Are More Data Always Better for Factor Analysis?” Journal of Econometrics 132 (1): 169–194. doi:10.1016/j.jeconom.2005.01.027.
   Web of Science ®Google Scholar
• Breiman, L. 1996. “Bagging Predictors.” Machine Learning 24 (2): 123–140. doi:10.1007/BF00058655.
   Web of Science ®Google Scholar
• Eickmeier, S., and T. Ng. 2011. “Forecasting National Activity Using Lots of International Predictors: An Application to New Zealand.” International Journal of Forecasting 27 (2): 496–511. doi:10.1016/j.ijforecast.2009.10.011.
   Web of Science ®Google Scholar
• Ejaz, M., and J. Iqbal. 2019. Estimation and Forecasting of Industrial Production Index. State Bank of Pakistan, Research Department.
   Google Scholar
• Forni, M., M. Hallin, M. Lippi, and L. Reichlin. 2003. “Do Financial Variables Help Forecasting Inflation and Real Activity in the Euro Area?” Journal of Monetary Economics 50 (6): 1243–1255. doi:10.1016/S0304-3932(03)00079-5.
   Web of Science ®Google Scholar
• Geweke, J. 1977. “The Dynamic Factor Analysis of Economic Time Series, edited by D.J. Aigner and A.S. Goldberger.” In Latent Variables in Socio-Economic Models, North-Holland, Amsterdam.
   Google Scholar
• Hoerl, A. E., and R. W. Kennard. 1970. “Ridge Regression: Applications to Nonorthogonal Problems.” Technometrics 12 (1): 69–82. doi:10.1080/00401706.1970.10488635.
   Web of Science ®Google Scholar
• Hussain, F., H. Kalim, and M. Rehman. 2018. “Nowcasting LSM Growth in Pakistan.” SBP Research Bulletin 14 (1): 1–16.
   Google Scholar
• Hyndman, R. J., and A. B. Koehler. 2006. “Another Look at Measures of Forecast Accuracy.” International Journal of Forecasting 22 (4): 679–688.
   Web of Science ®Google Scholar
• Li, J., and W. Chen. 2014. “Forecasting Macroeconomic Time Series: LASSO-based Approaches and Their Forecast Combinations with Dynamic Factor Models.” International Journal of Forecasting 30 (4): 996–1015. doi:10.1016/j.ijforecast.2014.03.016.
   Web of Science ®Google Scholar
• Mahmood, A. 2016. “Transmission of Volatility of Money Market Overnight Repo Rate along the Yield Curve.” SBP Research Bulletin 12 (1): 1–18.
   Google Scholar
• Malik, M., and M. N. Jahanzeb Hanif. 2018. “Learning from Errors while Forecasting Inflation: A Case for Intercept Correction.” International Econometric Review 11 (1): 12–38.
   Google Scholar
• Panagiotelis, A., G. Athanasopoulos, R. J. Hyndman, B. Jiang, and F. Vahid. 2019. “Macroeconomic Forecasting for Australia Using a Large Number of Predictors.” International Journal of Forecasting 35 (2): 616–633. doi:10.1016/j.ijforecast.2018.12.002.
   Web of Science ®Google Scholar
• Sargent, T. J., and C. A. Sims. 1977. “Business Cycle Modeling without Pretending to Have Too Much a Priori Economic Theory.” In Federal Reserve Bank of Minneapolis, Working Papers: 55, 1977. Federal Reserve Bank of Minneapolis, Working Papers: 55. http://libproxy.library.wmich.edu/login?url=https://search.proquest.com/docview/56507542?accountid=15099
   Google Scholar
• State Bank of Pakistan. 2020. SBP Vision 2020. http://www.sbp.org.pk/spd/Strategic.pdf
   Google Scholar
• Stock, J. H., and M. W. Watson. 2002. “Forecasting Using Principal Components from a Large Number of Predictors.” Journal of the American Statistical Association 97 (460): 1167–1179. doi:10.1198/016214502388618960.
   Web of Science ®Google Scholar
• Stock, J. H., and M. W. Watson. 2012. “Generalized Shrinkage Methods for Forecasting Using Many Predictors.” Journal of Business and Economic Statistics 30 (4): 481–493. doi:10.1080/07350015.2012.715956.
   Web of Science ®Google Scholar
• Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society: Series B (Methodological) 58 (1): 267–288.
   Web of Science ®Google Scholar
• Tibshirani, R., I. Johnstone, I. Johnstone, and R. Tibshirani. 2004. “Least Angle Regression.” The Annals of Statistics 32 (2): 407–451. doi:10.1214/009053604000000067.
   Web of Science ®Google Scholar
• Zou, H., and T. Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society. Series B, Statistical Methodology 67 (2): 301–320. doi:10.1111/j.1467-9868.2005.00503.x.
   Web of Science ®Google Scholar