Nonparametric Additive Instrumental Variable Estimator
A Group Shrinkage Estimation Perspective
Bibliographic Data
| ID | 19418244 |
|---|---|
| Authors | Qingliang Fan (0000-0001-9560-3311, Wang Yanan Institute for Studies in Economics (WISE), Department of Statistics, School of Economics and Fujian Key Laboratory of Statistical ScienceXiamen University, Fujian, China()), Wei Zhong (0000-0002-4157-3097, Wang Yanan Institute for Studies in Economics (WISE), Department of Statistics, School of Economics and Fujian Key Laboratory of Statistical ScienceXiamen University, Fujian, China()) |
| Year | 2018 |
| Volume | 36 |
| Issue | 3 |
| Pages | 388-399 |
| Publication date | 2018-07-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2016.1180991 |
| OpenAlex | W2343810144 |
| Language | EN |
| Citations received | 3 |
| References cited | 47 |
In this article, we study a nonparametric approach regarding a general nonlinear reduced form equation to achieve a better approximation of the optimal instrument. Accordingly, we propose the nonparametric additive instrumental variable estimator (NAIVE) with the adaptive group Lasso. We theoretically demonstrate that the proposed estimator is root-n consistent and asymptotically normal. The adaptive group Lasso helps us select the valid instruments while the dimensionality of potential instrumental variables is allowed to be greater than the sample size. In practice, the degree and knots of B-spline series are selected by minimizing the BIC or EBIC criteria for each nonparametric additive component in the reduced form equation. In Monte Carlo simulations, we show that the NAIVE has the same performance as the linear instrumental variable (IV) estimator for the truly linear reduced form equation. On the other hand, the NAIVE performs much better in terms of bias and mean squared errors compared to other alternative estimators under the high-dimensional nonlinear reduced form equation. We further illustrate our method in an empirical study of international trade and growth. Our findings provide a stronger evidence that international trade has a significant positive effect on economic growth
Additive model · Curse of dimensionality · Econometrics · Estimator · Instrumental variable · Linear model · Nonparametric regression · Nonparametric statistics · Statistics · Computer Science · Economic Policies and Impacts · Global trade and economics · Mathematics · Monetary Policy and Economic Impact · Applied Mathematics
Shaping the world economy
Innovation and growth in the global economy
Model Selection and Estimation in Regression with Grouped Variables
Does Trade Cause Growth?
Agricultural productivity, comparative advantage, and economic growth
Regression Shrinkage and Selection via The Lasso
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
The Adaptive Lasso and Its Oracle Properties
National Money as a Barrier to International Trade
Extended Bayesian information criteria for model selection with large model spaces
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
The Impact of Trade on Intra-Industry Reallocations and Aggregate Industry Productivity
Estimating the Dimension of a Model
Regression Shrinkage and Selection Via the Lasso
The curse of natural resources
Instrumental Variables Regression with Weak Instruments
Estimation With Many Instrumental Variables
Additive Nonparametric Regression in the Presence of Endogenous Regressors
The Puzzling Persistence of the Distance Effect on Bilateral Trade
Trade, Growth, and Poverty
Does Compulsory School Attendance Affect Schooling and Earnings
How Far Will International Economic Integration Go
| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |