Time Series as a Methodological Instrument for Measuring Economic Processes in Puerto Rico
DOI:
https://doi.org/10.70577/asce.v5i3.1076Keywords:
: econometrics; economic analysis; economic models; economic research; fiscal policy; Puerto Rico; artificial intelligence.Abstract
Time series constitute a statistical and econometric tool of high relevance for measuring, analyzing, and projecting economic processes in territories with particular structural characteristics, such as Puerto Rico. However, the academic literature applying these models specifically to the Puerto Rican case remains scarce and scattered, and seldom connects findings from official sources and quasi-experimental research with the formal apparatus of classical time series models. In response to that gap, this article critically examines the state of the art of ARIMA, VAR, cointegration, and ARCH/GARCH models applied to Puerto Rican macroeconomic indicators, as well as their complementarity with contemporary machine learning approaches. A narrative review of the specialized literature and official technical reports published between 1970 and 2026 was conducted, organized around three specific objectives: systematizing the theoretical foundations of the models, identifying the state of the art applied to the Puerto Rican context, and formulating a future research agenda. The results show that available studies on economic activity, public debt, and hurricane impacts in Puerto Rico document severe structural breaks and persistent economic effects, while recent methodological evidence suggests that hybrid models combining classical econometrics and machine learning improve predictive capacity in the face of exogenous shocks. The article concludes that strengthening the Puerto Rican empirical base in this field requires greater availability of high-frequency data, systematic incorporation of intervention variables, and a research agenda that combines non-conventional administrative data sources with Bayesian and deep learning models.
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References
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705 DOI: https://doi.org/10.1109/TAC.1974.1100705
Albalate, D., & Padró-Rosario, G. (2018). The economic cost of a hurricane: A case study of Puerto Rico and Hurricane Georges 1998 using synthetic control method (IREA Working Papers 2018/27). Universitat de Barcelona.
Babii, A., Ghysels, E., & Striaukas, J. (2024). Econometrics of machine learning methods in economic forecasting. En M. P. Clements & A. B. Galvão (Eds.), Handbook of research methods and applications in macroeconomic forecasting (pp. 246–273). Edward Elgar Publishing. DOI: https://doi.org/10.4337/9781035310050.00014
Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78. https://doi.org/10.2307/2998540 DOI: https://doi.org/10.2307/2998540
Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1–22. https://doi.org/10.1002/jae.659 DOI: https://doi.org/10.1002/jae.659
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 DOI: https://doi.org/10.1016/0304-4076(86)90063-1
Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day.
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3.ª ed.). Springer. https://doi.org/10.1007/978-3-319-29854-2 DOI: https://doi.org/10.1007/978-3-319-29854-2
Chow, G. C. (1960). Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28(3), 591–605. https://doi.org/10.2307/1910133 DOI: https://doi.org/10.2307/1910133
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348 DOI: https://doi.org/10.1080/01621459.1979.10482531
Enders, W. (2015). Applied econometric time series (4.ª ed.). Wiley.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773 DOI: https://doi.org/10.2307/1912773
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251–276. https://doi.org/10.2307/1913236 DOI: https://doi.org/10.2307/1913236
Federal Reserve Bank of New York. (2014). An update on the competitiveness of Puerto Rico's economy. https://www.newyorkfed.org/outreach-and-education/puerto-rico/2014/report-main.html
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384. https://doi.org/10.2307/1912559 DOI: https://doi.org/10.2307/1912559
Hamilton, J. D. (1994). Time series analysis. Princeton University Press. DOI: https://doi.org/10.1515/9780691218632
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5 DOI: https://doi.org/10.1016/0165-1765(80)90024-5
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254. https://doi.org/10.1016/0165-1889(88)90041-3 DOI: https://doi.org/10.1016/0165-1889(88)90041-3
Junta de Planificación de Puerto Rico. (2022). Informe económico al gobernador 2021. Gobierno de Puerto Rico.
Koopman, S. J., Harvey, A. C., Doornik, J. A., & Shephard, N. (2012). STAMP 8.3: Structural time series analyser, modeller and predictor. Timberlake Consultants Press.
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y DOI: https://doi.org/10.1016/0304-4076(92)90104-Y
Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297 DOI: https://doi.org/10.1093/biomet/65.2.297
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer. https://doi.org/10.1007/978-3-540-27752-1 DOI: https://doi.org/10.1007/978-3-540-27752-1
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), Artículo e0194889. https://doi.org/10.1371/journal.pone.0194889 DOI: https://doi.org/10.1371/journal.pone.0194889
Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2023). Machine learning advances for time series forecasting. Journal of Economic Surveys, 37(1), 76–111. https://doi.org/10.1111/joes.12429 DOI: https://doi.org/10.1111/joes.12429
Medeiros, M. C., Vasconcelos, G. F. R., Veiga, A., & Zilberman, E. (2021). Forecasting inflation in a data-rich environment: The benefits of machine learning methods. Journal of Business & Economic Statistics, 39(1), 98–119. https://doi.org/10.1080/07350015.2019.1637745 DOI: https://doi.org/10.1080/07350015.2019.1637745
Peri, G., Rury, D., & Wiltshire, J. C. (2020). The economic impact of migrants from Hurricane Maria (NBER Working Paper No. 27718). National Bureau of Economic Research. https://doi.org/10.3386/w27718 DOI: https://doi.org/10.3386/w27718
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335 DOI: https://doi.org/10.1093/biomet/75.2.335
Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., & dos Santos Coelho, L. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals, 135, Artículo 109853. https://doi.org/10.1016/j.chaos.2020.109853 DOI: https://doi.org/10.1016/j.chaos.2020.109853
Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599–607. https://doi.org/10.1093/biomet/71.3.599 DOI: https://doi.org/10.1093/biomet/71.3.599
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136 DOI: https://doi.org/10.1214/aos/1176344136
Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48. https://doi.org/10.2307/1912017 DOI: https://doi.org/10.2307/1912017
Stock, J. H., & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167–1179. https://doi.org/10.1198/016214502388618960 DOI: https://doi.org/10.1198/016214502388618960
U.S. Government Accountability Office. (2025). Puerto Rico: Fiscal conditions have improved but risks remain (GAO-25-108629). https://www.gao.gov/products/gao-25-108629
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0 DOI: https://doi.org/10.1016/S0925-2312(01)00702-0
Zivot, E., & Andrews, D. W. K. (1992). Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 10(3), 251–270. https://doi.org/10.2307/1391541 DOI: https://doi.org/10.1080/07350015.1992.10509904
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