Dynamic Factor Models of Consumption, Hours and Income
Bibliographic record
Abstract
This paper addresses two questions. First, what are the key factors that affect a consumer's lifetime budget constraint and how do they evolve over the lifecycle? Second, how do consumers respond to changes in these factors? We examine the permanent income hypothesis and the Keynesian consumption model using a dynamic factor model of consumption, hours, wages, unemployment, and income. We show that a quarterly dynamic factor model with restrictions on the lag structure nay be used with annual panel data to account for the fact that in many micro panel data sets the variables relevant to a study are measured at different time intervals and/or are aggregates for the calendar year. By using several income indicators we are able to extend the panel data studies of Hall and Mishkin and Bernanke to allow for measurement error. We are also able to study the response of income and consumption to some of the factors which determine them. In addition, we study a dynamic factor representation of a joint lifecycle model of consumption and labor supply. We provide estimates of the effect of wages, unemployment, and other income determinants on the marginal utility of income as well as estimates of the substitution effects of wage change on labor supply and consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".