Dynamic unobserved effects model: Difference between revisions

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==Censored dependent variable==

In a panel data [[tobit model]],<ref>{{cite book |last=Greene |first=W. H. |year=2003 |title=Econometric Analysis |publisher=Prentice Hall |___location=Upper Saddle River, NJ }}</ref><ref>The model framework comes from {{cite book |last=Wooldridge |first=J. |year=2002 |title=Econometric Analysis of Cross Section and Panel Data |url=https://archive.org/details/econometricanaly0000wool |url-access=registration |publisher=MIT Press |___location=Cambridge, Mass |page=[https://archive.org/details/econometricanaly0000wool/page/542 542] |isbn=9780262232197 }} But the author revises the model more general here.</ref> if the outcome <math>Y_{i,t}</math> partially depends on the previous outcome history <math>Y_{i,0},\ldots,Y_{t-1}</math> this tobit model is called "dynamic". For instance, taking a person who finds a job with a high salary this year, it will be easier for her to find a job with a high salary next year because the fact that she has a high-wage job this year will be a very positive signal for the potential employers. The essence of this type of dynamic effect is the state dependence of the outcome. The "unobservable effects" here refers to the factor which partially determines the outcome of individual but cannot be observed in the data. For instance, the ability of a person is very important in job-hunting, but it is not observable for researchers. A typical dynamic unobserved effects tobit model can be represented as

: <math>Y_{i,t}=Y_{i,t}^1[Y_{i,t}>0]; </math>

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: <math>\prod_{i=1}^N \int f_\theta(c_i \mid y_{i,0},x_i) \left[ \prod_{t=1}^T\Bigl(1[y_{i,t}=0][1-\Phi(z_{i,t}\delta+\rho y_{i,t-1}>0] \frac{\varphi(z_{i,t}\delta+\rho y_{i,t-1}+c_i)}{\Phi(z_{i,t} \delta+\rho y_{i,t-1}+c_i)}\biggr) \right] \, dc_i </math>

For the initial values <math>\{y_{i,0}\}^N_{i-1}</math> , there are two different ways to treat them in the construction of the likelihood function: treating them as constant, or imposing a distribution on them and calculate out the unconditional likelihood function. But whichever way is chosen to treat the initial values in the likelihood function, we cannot get rid of the integration inside the likelihood function when estimating the model by maximum likelihood estimation (MLE). Expectation Maximum (EM) algorithm is usually a good solution for this computation issue.<ref>For more details, refer to: {{cite book |lastlast1=Cappé |firstfirst1=O. |last2=Moulines |first2=E. |last3=Ryden |first3=T. |year=2005 |title=Inference in Hidden Markov Models |publisher=Springer-Verlag |___location=New York |chapter=Part II: Parameter Inference |isbn=9780387289823 |chapter-url=https://books.google.com/books?id=4d_oEYn8Fl0C&pg=PA347 }}</ref> Based on the consistent point estimates from MLE, Average Partial Effect (APE)<ref>{{cite book |last=Wooldridge |first=J. |year=2002 |title=Econometric Analysis of Cross Section and Panel Data |url=https://archive.org/details/econometricanaly0000wool |url-access=registration |publisher=MIT Press |___location=Cambridge, Mass |page=[https://archive.org/details/econometricanaly0000wool/page/22 22] |isbn=9780262232197 }}</ref> can be calculated correspondingly.<ref> For more details, refer to: {{cite journal |first=Takeshi |last=Amemiya |year=1984 |title=Tobit models: A survey |journal=Journal of Econometrics |volume=24 |issue=1–2 |pages=3–61 |doi=10.1016/0304-4076(84)90074-5 }}</ref>

==Binary dependent variable==