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on Econometric Time Series |
| By: | Manu Navjeevan |
| Abstract: | I propose a new identification-robust test for the structural parameter in a heteroskedastic linear instrumental variables model. The proposed test statistic is similar in spirit to a jackknife version of the K-statistic and the resulting test has exact asymptotic size so long as an auxiliary parameter can be consistently estimated. This is possible under approximate sparsity even when the number of instruments is much larger than the sample size. As the number of instruments is allowed, but not required, to be large, the limiting behavior of the test statistic is difficult to examine via existing central limit theorems. Instead, I derive the asymptotic chi-squared distribution of the test statistic using a direct Gaussian approximation technique. To improve power against certain alternatives, I propose a simple combination with the sup-score statistic of Belloni et al. (2012) based on a thresholding rule. I demonstrate favorable size control and power properties in a simulation study and apply the new methods to revisit the effect of social spillovers in movie consumption. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.14892&r=ets |
| By: | Clarke, Damian (University of Chile); Torres, Nicolás Paris (University of Chile); Villena-Roldan, Benjamin (Diego Portales University) |
| Abstract: | We demonstrate that regression models can be estimated by working independently in a row-wise fashion. We document a simple procedure which allows for a wide class of econometric estimators to be implemented cumulatively, where, in the limit, estimators can be produced without ever storing more than a single line of data in a computer's memory. This result is useful in understanding the mechanics of many common regression models. These procedures can be used to speed up the computation of estimates computed via OLS, IV, Ridge regression, LASSO, Elastic Net, and Non-linear models including probit and logit, with all common modes of inference. This has implications for estimation and inference with 'big data', where memory constraints may imply that working with all data at once is particularly costly. We additionally show that even with moderately sized datasets, this method can reduce computation time compared with traditional estimation routines. |
| Keywords: | big data, estimation, regression, matrix inversion |
| JEL: | C55 C61 C87 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16630&r=ets |
| By: | Robert Stok; Paul Bilokon |
| Abstract: | Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been proposed to solve this problem as it favorable statistical properties, but relies on assumptions about underlying market dynamics. Machine learning methods have also been proposed but lack interpretability, and often lag in performance. In this paper we implement the SV-PF-RNN: a hybrid neural network and particle filter architecture. Our SV-PF-RNN is designed specifically with stochastic volatility estimation in mind. We then show that it can improve on the performance of a basic particle filter. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.06256&r=ets |
| By: | David S. Lee; Justin McCrary; Marcelo J. Moreira; Jack Porter; Luther Yap |
| Abstract: | For the over-identified linear instrumental variables model, researchers commonly report the 2SLS estimate along with the robust standard error and seek to conduct inference with these quantities. If errors are homoskedastic, one can control the degree of inferential distortion using the first-stage F critical values from Stock and Yogo (2005), or use the robust-to-weak instruments Conditional Wald critical values of Moreira (2003). If errors are non-homoskedastic, these methods do not apply. We derive the generalization of Conditional Wald critical values that is robust to non-homoskedastic errors (e.g., heteroskedasticity or clustered variance structures), which can also be applied to nonlinear weakly-identified models (e.g. weakly-identified GMM). |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.15952&r=ets |