Bibliographic record
Abstract
Abstract This paper estimates an investment equation for private investment using Bayesian estimation techniques. In the paper we derive the optimal capital accumulation behavior in the model economy from the households’ optimization problem of utility. The equation is derived as in Smets and Wouters (2003). The model contains costly adjustment of investment and random shocks to adjustment cost function. The driving variable of investment is Tobin Q variable. The empirical proxy for Tobin Q in this paper is the ratio of OMX Helsinki Cap Index to the price index of the physical capital. The investment series is the seasonally adjusted private investment in quarterly national accounts. The AR(1) modelled investment shocks are found to be less persistent in Finland than in the euro area. The estimated median of persistence parameter for Finland is 0.485. Also the shocks to investment adjustment cost function are found to vary less in Finland as in the euro area. The estimated standard deviation of the shocks is 0.065. The adjustment cost parameter is roughly the same for both data sets. The results are robust to loosening the strict prior of discount factor, beta=0.99. The paper also provides discussion about adjustment cost parameter and we investigate the behaviour of the posterior chain of B with different prior distributions for the parameter. Tiivistelmä Tässä pro gradussa estimoidaan yhtälö yksityisille investoinneille bayesilaisella menetelmällä. Tässä työssä optimaalinen pääoman akkumulointi mallikansantaloudessa johdetaan kotitalouksien hyödyn optimointi-ongelmasta. Investointiyhtälö johdetaan kuten Smets’n ja Wouterin (2003) artikkelissa. Malli sisältää investointien sopeutuskustannukset ja satunnaisia shokkeja sopeutuskustannus-funktioon. Investointien selittävä muuttuja on Tobin Q -muuttuja. Empiirinen vastine teoreettiselle Tobin Q muuttujalle on OMX Helsinki Cap indexin arvo suhteutettuna fyysisen pääoman hintaindeksillä. Työssä käytetty investointisarja on kausitasoitettu yksityisten investointien sarja kansantalouden neljännestilinpidossa. Investointishokit ovat AR(1)-prosessi. Shokit osoittautuvat vähemmän pysyviksi Suomessa kuin euroalueella. Estimoitu AR(1)-kerroin investointishokeille on 0.485. Investointishokit myös vaihtelevat vähemmän Suomessa kuin euroalueella, sillä estimoitu shokkien keskihajonta on 0.065. Investointien sopeutuskustannus on likipitäen samankokoinen Suomessa ja euroalueella. Tulokset ovat robusteja kiinnitetyn diskonttausparametrin beta=0.99 löysäämiselle antamalla betalle eri priorijakaumia. Tässä työssä myös keskustellaan sopeutuskustannusparametrista ja tutkitaan sen posterioiriketjujen käyttäytymistä kun sille annetaan eri priorijakaumia.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".