Construction Business Cycle Analysis Using the Regime Switching Model
Bibliographic record
Abstract
The construction industry is a key industry in many countries, usually making up to 5–10% of the overall gross domestic product (GDP). It is closely related to the financial and labor markets, depending on the characteristics of businesses in a given country. For example, the moratorium in Russia in 1998 and the subprime mortgage crisis in the U.S. in 2007 greatly influenced the financial markets of many countries, which consequently affected the construction market. The effect of such crises on the construction industry differs, however, depending on the size of the business cycle and the foundation of the financial market. Thus, this study analyzed the construction business cycle of three countries: the United States, the United Kingdom, and South Korea. The economies of these three countries have different characteristics. This study, which used the three-state Markov switching model, also used construction industry data for categorizing GDP by economic activity. Although the validation results of the U.S. construction industry were unsatisfactory because of the unprecedented long-term recession, results of the analysis showed that the proposed model could be used to determine the construction business cycle. The forecasting performance test also showed that the proposed model could be used to predict more than one quarter in advance, which was the interval in identifying the business cycle. Accordingly, it is believed that the proposed methodology can be used to determine and cope with each country’s business cycle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".