High dimensional stochastic regression with latent factors, endogeneity and nonlinearity

  title={High dimensional stochastic regression with latent factors, endogeneity and nonlinearity},
  author={Jinyuan Chang and Bin Guo and Qiwei Yao},
  journal={Journal of Econometrics},

Figures and Tables from this paper

Modelling non‐stationary multivariate time series of counts via common factors

We develop a new parameter‐driven model for multivariate time series of counts. The time series is not necessarily stationary. We model the mean process as the product of modulating factors and

Factor-Adjusted Regularized Model Selection

This paper considers the case where covariate dependence can be reduced through the factor model, and proposes a consistency strategy named Factor-Adjusted Regularized Model Selection (FarmSelect), which transforms the problem from model selection with highly correlated covariates to that with weakly correlated ones via lifting.

Factor analysis for high‐dimensional time series: Consistent estimation and efficient computation

A new approach based on the eigenvalues of a non‐negative definite matrix for consistently determining the number of factors is proposed, which is computationally efficient with a single step procedure, especially when both weak and strong factors exist in the factor model.

Smoothed Quantile Regression with Factor-Augmented Regularized Variable Selection for High Correlated Data

Simulation study and real data application demonstrate that the factor-augmented regularized variable selection for quantile regression (Farvsqr) method is better than the common regularized M-estimation LASSO.

Constrained Factor Models for High-Dimensional Matrix-Variate Time Series

A general framework for incorporating domain and prior knowledge in the matrix factor model through linear constraints is established and is shown to be useful in achieving parsimonious parameterization, facilitating interpretation of the latent matrix factor, and identifying specific factors of interest.

Modeling Multivariate Spatial-Temporal Data with Latent Low-Dimensional Dynamics

A new approach to utilize the correlations in variable, space and time to achieve dimension reduction and to facilitate spatial/temporal predictions in the high-dimensional settings and innovative estimation and prediction methods based on the latent low-rank structures are proposed.

Varying-Coefficient Panel Data Models with Partially Observed Factor Structure

In this paper, we study a varying-coefficient panel data model with nonstationarity, wherein a factor structure is adopted to capture different effects of time invariant variables over time. The

Homogeneity and Structure Identification in Semiparametric Factor Models

Abstract Factor modeling is an essential tool for exploring intrinsic dependence structures in financial and economic studies through the construction of common latent variables, including the famous



Large Vector Auto Regressions

One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements

Estimation of latent factors for high-dimensional time series

This paper deals with the dimension reduction of high-dimensional time series based on a lower-dimensional factor process. In particular, we allow the dimension of time series N to be as large as, or

Factor modeling for high-dimensional time series: inference for the number of factors

This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense that both the

Sparse Vector Autoregressive Modeling

A two-stage approach for fitting sparse VAR (sVAR) models in which many of the AR coefficients are zero is proposed, based on an estimate of the partial spectral coherence (PSC) together with the use of BIC.

Inferential Theory for Factor Models of Large Dimensions

  • J. Bai
  • Mathematics, Economics
  • 2003
This paper develops an inferential theory for factor models of large dimensions. The principal components estimator is considered because it is easy to compute and is asymptotically equivalent to the

Model Specification in Multivariate Time Series

The concept of scalar component models within the vector ARMA framework is introduced to reveal possibly hidden simplifying structures of the process, to achieve parsimony in parameterization and to identify the exchangeable models.

Determining the Number of Factors in the General Dynamic Factor Model

This article develops an information criterion for determining the number q of common shocks in the general dynamic factor model developed by Forni et al., as opposed to the restricted dynamic model

Segmenting Multiple Time Series by Contemporaneous Linear Transformation: PCA for Time Series

We seek for a contemporaneous linear transformation for a $p$-variate time series such that the transformed series is segmented into several lower-dimensional subseries, and those subseries are

The Generalized Dynamic-Factor Model: Identification and Estimation

This paper proposes a factor model with infinite dynamics and nonorthogonal idiosyncratic components. The model, which we call the generalized dynamic-factor model, is novel to the literature and

Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions

We propose an estimation method for models of conditional moment restrictions, which contain finite dimensional unknown parameters (theta) and infinite dimensional unknown functions (h). Our proposal