Two models of dynamic input demand : estimates with Canadian manufacturing data
Bibliographic record
Abstract
Over the past decade there has been a number of innovations in the estimation of input demand equations. In particular, ways of incorporating the hypothesis of rational expectations into empirical models of the firm have been developed and improved upon. This research agenda was perhaps inspired by the Lucas critique of econometric policy evaluation, which suggested that econometric models which did not explicitly take account of how expectations of the future affect current behaviour would give misleading results regarding the possible effects of various government policies. Lucas specifically directed part of his critique at empirical models of business investment, which had been used previously in the assessment of tax policies designed to affect investment. This thesis has a dual purpose. First, two distinct models of input demand are estimated with Canadian manufacturing data. Each of the models incorporates to some degree the hypothesis of rational expectations, but the specifications of technology differ. Neither of these models, to our knowledge, has been estimated with Canadian data. We are interested in whether either model explains well the behaviour of the Canadian manufacturing sector, and in how the results compare with the (few) U.S. applications of this type of model. The second purpose is to use the results of these models in simulations to assess the effect of changes to the after-tax rental rate of capital on investment and employment in manufacturing. While there have been studies in Canada (and elsewhere) that attempt to calculate the effects of various tax policies on investment, most studies were done prior to the innovation of techniques in estimating models with rational expectations. This thesis is able to examine the effects of a particular change while remaining immune to the Lucas critique. If the modelling of expectations is correct, this could not only improve the reliability of the estimates, but also give some indication of the empirical importance of the Lucas critique. The results can be summarized as follows. The two models give very different estimates of price elasticities of demand for capital and labour, even though they are similar in many respects and are estimated with a common data set. It is also the case that their estimates of the effects of temporary and permanent changes to the rental rate are different. Adjusting the reduced form parameters of the input demand equations to account for changes in tax policy regimes alters the results to a significant degree, suggesting that the explicit modelling of expectations matters in an empirically relevant sense. However, these effects are in opposite directions for the two models considered here. All this suggests that more research is required into the relationship between expectations of future policy and investment behaviour.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".