Behavioural Pattern of FDI Inflows: Autoregressive Study
Bibliographic record
Abstract
The study investigates on the behavioral pattern of sectoral foreign direct investment (FDI) inflows in Indian economy for the period 2002-2010. Thirteen sectors of the economy as categorized by the Government of India are used for the study. Econometrics tools of analysis are applied. In particular the study uses Autoregression of Order One denoted as AR (1). The study seeks to explain behavioral pattern of FDI inflows. The study uncovered that Telecommunications and Construction sectors exhibited explosive behavior. Housing and Real Estate and Automobile sectors exhibited non-stationary behavioral pattern during the period. However, some sectors viz electrical equipment including Computer Software and Electronics; Transportation Industry; Chemicals (other than fertilizers); Drugs and Pharmaceuticals; Food processing industries; Cement and Gypsum products and Metallurgical industries have exhibited stationarity. Also the study exposes foreign investors’ biases in choosing some sectors in relation to other sectors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".