The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Estimating nonlinear time-series models using simulated vector autoregressions
Abstract
This paper develops two new methods for conducting formal statistical inference in nonlinear dynamic economic models. The two methods require very little analytical tractability, relying instead on numerical simulation of the model's dynamic behaviour. Although one of the estimators is asymptotically more efficient than the other, a Monte Carlo study shows that, for a specific application, the less efficient estimator has smaller mean squared error in samples of the size typically encountered in macroeconomics. The estimator with superior small sample performance is used to estimate the parameters of a real business cycle model using observed US time-series data.
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Cites
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Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
Newey, W., & West, K. (1986) · National Bureau of Economic Research · doi:10.3386/t0055 · Cited by 12,278
The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis
Perron, P. (1989) · Econometrica · doi:10.2307/1913712 · Cited by 7,732
Time to Build and Aggregate Fluctuations
Kydland, F. E., & Prescott, E. C. (1982) · Econometrica · doi:10.2307/1913386 · Cited by 5,825
Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
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Investment, Capacity Utilization and the Real Business Cycle
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Indivisible labor and the business cycle
Hansen, G. D. (1985) · Journal of Monetary Economics · doi:10.1016/0304-3932(85)90039-x · Cited by 1,976
Journal of business and economic statistics 5
Fildes, R. (1988) · International Journal of Forecasting · doi:10.1016/0169-2070(88)90121-5 · Cited by 1,973
Cited by
Simulation-based Econometric Methods
Gouriéroux, C., & Monfort, A. (1997) · doi:10.1093/0198774753.001.0001 · Cited by 1,038
Gallant, A. R., & Tauchen, G. (1996) · Econometric Theory · doi:10.1017/s0266466600006976 · Cited by 988
Chapter 15 Volatility and Correlation Forecasting
Andersen, T. G., Bollerslev, T., Christoffersen, P. F., & Diebold, F. X. (2006) · Handbook of Economic Forecasting · doi:10.1016/s1574-0706(05)01015-3 · Cited by 330
Measuring the Sensitivity of Parameter Estimates to Estimation Moments*
Andrews, I., Gentzkow, M., & Shapiro, J. M. (2017) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx023 · Cited by 313
Linking data to models: data regression
Jaqaman, K., & Danuser, G. (2006) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2030 · Cited by 243
Metadata
- Authors
- Smith, A. A.
- Venue
- Journal of Applied Econometrics
- Publisher
- Wiley
- Published
- 1993-12
- Volume
- 8
- Issue
- S1
- Pages
- S63-S84
- ISSN
- 0883-7252 (ISSN-L)
- 1099-1255
- OpenAlex
- W2169829317
- Cited by
- 685
- References
- 21
- FWCI
- 15.62
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
