Boughabi, Houssam (2025): Inflation Persistence and Involuntary Unemployment in Pakistan: A Keynesian Econometric Study.
Preview |
PDF
MPRA_paper_126294.pdf Download (482kB) | Preview |
Abstract
This paper develops a stochastic Keynesian model linking inflation, unemployment, and GDP. Inflation follows a fractional Brownian motion, capturing persistent shocks, while a temporal convolutional network forecasts conditional paths, allowing machine learning to account for nonlinear interactions and long-memory effects. Unemployment responds conditionally to inflation thresholds, permitting involuntary joblessness, while GDP depends on both variables, reflecting aggregate demand and labor market frictions. The model is applied to Pakistan, simulating macroeconomic dynamics under alternative policy scenarios. We demonstrate that sustained growth is possible even under persistent inflation, reinforcing the empirical relevance of Keynesian theory in contemporary macroeconomic analysis and highlighting the value of machine learning for policy evaluation.
| Item Type: | MPRA Paper |
|---|---|
| Original Title: | Inflation Persistence and Involuntary Unemployment in Pakistan: A Keynesian Econometric Study |
| English Title: | Inflation Persistence and Involuntary Unemployment in Pakistan: A Keynesian Econometric Study |
| Language: | English |
| Keywords: | Stagflation, Fractional Brownian Motion, Temporal Convolutional Networks, Keynesian Policy, Pakistan |
| Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E24 - Employment ; Unemployment ; Wages ; Intergenerational Income Distribution ; Aggregate Human Capital ; Aggregate Labor Productivity E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level ; Inflation ; Deflation E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications O - Economic Development, Innovation, Technological Change, and Growth > O5 - Economywide Country Studies > O53 - Asia including Middle East |
| Item ID: | 126294 |
| Depositing User: | P.hD Houssam Boughabi |
| Date Deposited: | 10 Oct 2025 01:34 |
| Last Modified: | 10 Oct 2025 01:34 |
| References: | Aghion, P., Bacchetta, P., & Banerjee, A. (2001). Currency crises and monetary policy in an economy with credit constraints. European Economic Review, 45(7), 1121–1150. Akerlof, G. A., Dickens, W. T., & Perry, G. L. (1996). The macroeconomics of low inflation. Brookings Papers on Economic Activity, 1996(1), 1–76. Ball, L. (1994). What determines the sacrifice ratio? In Mankiw, N. G. (Ed.), Monetary Policy (pp. 155–182). University of Chicago Press, Chicago. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Proceedings of NeurIPS 2018, 1–11. Beran, J. (1994). Statistics for Long-Memory Processes. Chapman & Hall, New York. Blanchard, O. (1990). Suggestions for a new set of fiscal indicators. OECD Economics Department Working Papers, No. 79. Blinder, A. S. (1987). Keynes, Lucas, and scientific progress. American Economic Review, 77(2), 130–136. Friedman, M. (1968). The role of monetary policy. American Economic Review, 58(1), 1–17. Galí, J. (2015). Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework. Princeton University Press, Princeton. Heeren, S. (2021). Forecasting inflation using machine learning for an emerging economy. Erasmus University Rotterdam, Bachelor Thesis. Kalecki, M. (1943). Political aspects of full employment. Political Quarterly, 14(4), 322–331. Lucas, R. E. (1972). Expectations and the neutrality of money. Journal of Economic Theory, 4(2), 103–124. Mandelbrot, B. B., & Wallis, J. R. (1968). Computer experiments with fractional Gaussian noises. Water Resources Research, 4(5), 909–918. Tobin, J. (1972). Inflation and unemployment. American Economic Review, 62(1/2), 1–18. Phillips, A. W. (1958). The relationship between unemployment and the rate of change of money wages in the United Kingdom, 1861–1957. Economica, 25(100), 283–299. Woodford, M. (2003). Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press. Zhou, H., Yang, Y., & Zhang, X. (2020). Stock price prediction using temporal convolutional networks. Proceedings of the 2020 International Conference on Artificial Intelligence and Big Data, 1–6. Liu, Z., Zhang, Y., & Wang, H. (2021). A new approach to seasonal energy consumption forecasting using temporal convolutional networks. Proceedings of the 2021 International Conference on Artificial Intelligence and Energy Engineering, 1–6. Araujo, G. S., & Gaglianone, W. P. (2020). Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models. Central Bank of Brazil Working Paper. He, Q., & Zhang, L. (2019). Forecasting macroeconomic time series using machine learning: Evidence from emerging markets. Journal of Forecasting, 38(5), 423–442. |
| URI: | https://mpra.ub.uni-muenchen.de/id/eprint/126294 |

