'Small Data': Efficient Inference with Occasionally Observed States

Lanz, Andreas; Müller, Philipp; Reich, Gregor; Wilms, Ole

doi:10.2139/ssrn.3638618

Cited by 1 publication

(2 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our context in which the consumer's satisfaction with the consumed content is unobserved, the likelihood is not straightforward to compute, because it forms an integral over the unobserved states. To cope with this condition, we apply recursive likelihood integration (RLI; Reich 2018;Lanz et al, 2021). The model and the estimation method are implemented in MATLAB using CasADi (Andersson et al, 2019); the MPEC problem is solved using the KNITRO constrained optimization solver.…”

Section: Estimation With Unobserved Statesmentioning

confidence: 99%

“…its flexible counterpart results in roughly 1.5 times longer computation times. The case of the model with a serially correlated, unobserved utility component requires us (i) to approximate the expected value as a function of a two-dimensional state variable, and (ii) to integrate out the random shock when computing the likelihood function; we do the latter by applying the recursive likelihood integration method (RLI; Reich, 2018;Lanz, 2021). Our findings regarding the efficiency of the balanced error approach compared to fixed and uniform grids in the two-dimensional case are comparable, but given the longer absolute computing times, the absolute gains are even more significant.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive grids for the estimation of dynamic models

Lanz

Reich²,

Wilms

2022

Quant Mark Econ

Self Cite

View full text Add to dashboard Cite

This paper develops a method to flexibly adapt interpolation grids of value function approximations in the estimation of dynamic models using either NFXP (Rust, Econometrica: Journal of the Econometric Society, 55, 999–1033, 1987) or MPEC (Su & Judd, Econometrica: Journal of the Econometric Society, 80, 2213–2230, 2012). Since MPEC requires the grid structure for the value function approximation to be hard-coded into the constraints, one cannot apply iterative node insertion for grid refinement; for NFXP, grid adaption by (iteratively) inserting new grid nodes will generally lead to discontinuous likelihood functions. Therefore, we show how to continuously adapt the grid by moving the nodes, a technique referred to as r-adaption. We demonstrate how to obtain optimal grids based on the balanced error principle, and implement this approach by including additional constraints to the likelihood maximization problem. The method is applied to two models: (i) the bus engine replacement model (Rust, 1987), modified to feature a continuous mileage state, and (ii) to a dynamic model of content consumption using original data from one of the world’s leading user-generated content networks in the domain of music.

show abstract

Section: Estimation With Unobserved Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%