arXiv 13 Nov 2017 · Econometrics · 4 citations (OpenAlex)
arXiv:1711.04392 · PDF · DOI · OpenAlex · Extracted main text
We consider continuous-time models with a large panel of moment conditions, where the structural parameter depends on a set of characteristics, whose effects are of interest. The leading example is the linear factor model in financial economics where factor betas depend on observed characteristics such as firm specific instruments and macroeconomic variables, and their effects pick up long-run time-varying beta fluctuations. We specify the factor betas as the sum of characteristic effects and an orthogonal idiosyncratic parameter that captures high-frequency movements. It is often the case that researchers do not know whether or not the latter exists, or its strengths, and thus the inference about the characteristic effects should be valid uniformly over a broad class of data generating processes for idiosyncratic parameters. We construct our estimation and inference in a two-step continuous-time GMM framework. It is found that the limiting distribution of the estimated characteristic effects has a discontinuity when the variance of the idiosyncratic parameter is near the boundary (zero), which makes the usual "plug-in" method using the estimated asymptotic variance only valid pointwise and may produce either over- or under- coveraging probabilities. We show that the uniformity can be achieved by cross-sectional bootstrap. Our procedure allows both known and estimated factors, and also features a bias correction for the effect of estimating unknown factors.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Connor, G., Matthias, H. and Linton, O (2012) Efficient semiparametric estimation of the fama-french model and extensions | 0.843 | 3 | 3 | 100% |
| 2 | Fan, J., Liao, Y. and Wang, W (2016) Projected principal component analysis in factor models self | 0.843 | 3 | 3 | 100% |
| 3 | Ketz, P (2017) Testing overidentifying restrictions when the true parameter vector is near or at the boundary of the parameter space | 0.811 | 4 | 2 | 100% |
| 4 | Aẗ-Sahalia, Y. and Xiu, D (2017) Using principal component analysis to estimate a high dimensional factor model with high-frequency data | 0.737 | 3 | 2 | 100% |
| 5 | Barndorff-Nielsen, O. E. and Shephard, N (2004) Econometric analysis of realized covariation: High frequency based covariance, regression, and correlation in financial economics | 0.737 | 3 | 2 | 100% |
| 6 | Ferson, W. E. and Harvey, C. R (1999) Conditioning variables and the cross section of stock returns | 0.737 | 3 | 2 | 100% |
| 7 | Gagliardini, P., Ossola, E. and Scaillet, O (2016) Time-varying risk premium in large cross-sectional equity data sets | 0.737 | 3 | 2 | 100% |
| 8 | Herskovic, B., Kelly, B., Lustig, H. and Van Nieuwerburgh, S (2016) The common factor in idiosyncratic volatility: Quantitative asset pricing implications | 0.737 | 3 | 2 | 100% |
| 9 | Li, J., Todorov, V. and Tauchen, G (2016) Inference theory for volatility functional dependencies | 0.737 | 3 | 2 | 100% |
| 10 | Mykland, P. A., Zhang, L. et al (2006) Anova for diffusions and ito processes | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 67 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.