Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Real-time Prediction of the Great Recession and the Covid-19 Recession (2310.08536v5)

Published 12 Oct 2023 in econ.EM

Abstract: This paper uses standard and penalized logistic regression models to predict the Great Recession and the Covid-19 recession in the US in real time. It examines the predictability of various macroeconomic and financial indicators with respect to the NBER recession indicator. The findings strongly support the use of penalized logistic regression models in recession forecasting. These models, particularly the ridge logistic regression model, outperform the standard logistic regression model in predicting the Great Recession in the US across different forecast horizons. The study also confirms the traditional significance of the term spread as an important recession indicator. However, it acknowledges that the Covid-19 recession remains unpredictable due to the unprecedented nature of the pandemic. The results are validated by creating a recession indicator through principal component analysis (PCA) on selected variables, which strongly correlates with the NBER recession indicator and is less affected by publication lags.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. On the existence of maximum likelihood estimates in logistic regression models. Biometrika 71(1), 1–10.
  2. The cost of business cycles under endogenous growth. American Economic Review 94(4), 964–990.
  3. On the welfare cost of consumption volatility. NBER Working Paper No. 12763.
  4. Predicting recessions with leading indicators: Model averaging and selection over the business cycle. Journal of Forecasting 34, 455–471.
  5. Evaluating the classification of economic activity into recessions and expansions. American Economic Journal: Macroeconomics 3(2), 246–277.
  6. On the use of cross-validation for time series predictor evaluation. Information Sciences 191, 192–213.
  7. Cyclical analysis of time series: selected procedures and computer programs. NBER Technical Paper 20.
  8. Measuring business cycles. National Bureau of Economic Research New York.
  9. Were financial crises predictable? Journal of Money, Credit and Banking 26(1), 102–124.
  10. Evaluating time series forecasting models: an empirical study on performance estimation methods. Machine Learning 109.11, 1997–2028.
  11. Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications 112, 353–371.
  12. Dating business cycle turning points. Contributions to Economic Analysis 276, 1–54.
  13. A comparison of the real-time performance of business cycle dating methods. Journal of Business and Economic Statistics 26(1), 42–49.
  14. The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. Bio Genomics 21(6), 1724–1734.
  15. The costs of recession. The Economic Journal 104(422), 20–36.
  16. Recession forecasting using bayesian classification. International Journal of Forecasting 35(3), 848–867.
  17. Strengthening the case for the yield curve as a predictor of u.s. recessions. Federal Reserve Bank of St. Louis Review 79, 41–51.
  18. Predicting recessions with boosted regression trees. International Journal of Forecasting 33(4), 745–759.
  19. Predicting u.s. recessions: Financial variables as leading indicators. The Review of Economics and Statistics 80(1), 45–61.
  20. Ridge regression: Applications to nonorthogonal problems. Technometircs 12(1), 69–82.
  21. Forecasting recessions with time-varying models. Journal of Macroeconomics 62, 103153.
  22. Logistic regression in rare events data. Political Analysis 9(2).
  23. Job displacement risk and the cost of business cycles. American Economic Review 97(3), 664–686.
  24. Monitoring recessions: A bayesian sequential quickest detection method. International Journal of Forecasting 37, 500–510.
  25. What predicts us recessions? International Journal of Forecasting 32, 1138–1150.
  26. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 30.
  27. The estimation of choice probabilities from choice based samples. Econometrica 45(8), 1977–1988.
  28. Verifying the solution from a nonlinear solver: A case study. American Economic Review 93(3), 873 – 892.
  29. Recession forecasting with high-dimensional data. Journal of Forecasting 41(4), 752–764.
  30. Viewpoint: boosting recessions. Canadian Journal of Economics 47, 1–34.
  31. Nonstationary binary choice. Econometrica 68(5), 1249–1280.
  32. A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology 49(12), 1373–1379.
  33. Forecasting with a real-time data set for macroeconomists. Journal of Macroeconomics 24(4), 507–531.
  34. Missforest: Non-parametric missing value imputation for mixed-type data. Bioinformatics 28(1), 112–118.
  35. New indexes of coincident and leading economic indicators. NBER Macroeconomics Annual 4, 351–394.
  36. A procedure for predicting recessions with leading indicators: Econometric issues and recent experience. Business Cycles, Indicators and Forecasting University of Chicago Press, 95–156.
  37. Classification assessment methods. Applied Computing and Informatics 17(1), 168–192.
  38. Predictive power of markovian models: Evidence from us recession forecasting. Journal of Forecasting 38(6), 525–551.
  39. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B, 267–288.
  40. Modeling and predicting u.s. recessions using machine learning techniques. International Journal of Forecasting 37, 647–671.
  41. Regularization and variable selection via the elastic net. Journal of Royal Statistical Society Series B 67(2), 301–320.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now