ANALYZING THE DYNAMICS OF GROSS DOMESTIC PRODUCT GROWTH. A MIXED FREQUENCY MODEL APPROACH
Bibliographic record
Abstract
Frequency mismatch has been a problem in time series econometrics. Many monthly economic and financial indicators are normally aggregated to match quarterly macroeconomic series such as Gross Domestic Product when performing econometric analysis. However, temporal aggregation, although widely accepted, is prone to information loss. To address this issue, mixed frequency modelling is employed by using state space models with time-varying parameters. Quarter-on-quarter growth rate of GDP estimates are treated as monthly series with missing observation. Using Kalman filter algorithm, state space models are estimated with eleven monthly economic indicators as explanatory variables. A one-step-ahead forecast for GDP growth rates is generated and as more indicators are included in the model, the predicted values became closer to the actual data. Further evaluation revealed that among the group competing models, using Consumer Price Index (CPI), growth rates of Philippine Stock Exchange Index (PSEi), Exchange Rate, Real Money Supply, Wholesale Price Index (WPI) and Merchandise Exports are the more important determinants of GDP growth and generated the most desirable forecasts (lower forecast errors).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".