| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w22441 |
| 来源ID | Working Paper 22441 |
| Identification of Dynamic Latent Factor Models: The Implications of Re-Normalization in a Model of Child Development | |
| Francesco Agostinelli; Matthew Wiswall | |
| 发表日期 | 2016-07-25 |
| 出版年 | 2016 |
| 语种 | 英语 |
| 摘要 | A recent and growing area of research applies latent factor models to study the development of children's skills. Some normalization is required in these models because the latent variables have no natural units and no known location or scale. We show that the standard practice of “re-normalizing” the latent variables each period is over-identifying and restrictive when used simultaneously with common skill production technologies that already have a known location and scale (KLS). The KLS class of functions include the Constant Elasticity of Substitution (CES) production technologies several papers use in their estimation. We show that these KLS production functions are already restricted in the sense that their location and scale is known (does not need to be identified and estimated) and therefore further restrictions on location and scale by re-normalizing the model each period is unnecessary and over-identifying. The most common type of re-normalization restriction imposes that latent skills are mean log-stationary, which restricts the class of CES technologies to be of the log-linear (Cobb-Douglas) sub-class, and does not allow for more general forms of complementarities. Even when a mean log-stationary model is correctly assumed, re-normalization can further bias the estimates of the skill production function. We support our analytic results through a series of Monte Carlo exercises. We show that in typical cases, estimators based on “re-normalizations” are biased, and simple alternative estimators, which do not impose these restrictions, can recover the underlying primitive parameters of the production technology. |
| 主题 | Econometrics ; Estimation Methods ; Labor Economics ; Demography and Aging |
| URL | https://www.nber.org/papers/w22441 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/580115 |
| 推荐引用方式 GB/T 7714 | Francesco Agostinelli,Matthew Wiswall. Identification of Dynamic Latent Factor Models: The Implications of Re-Normalization in a Model of Child Development. 2016. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w22441.pdf（402KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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