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Found 15 papers, 0 papers with code
Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects
In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge.
Decomposition of Bilateral Trade Flows Using a Three-Dimensional Panel Data Model
This study decomposes the bilateral trade flows using a three-dimensional panel data model.
On Time-Varying VAR Models: Estimation, Testing and Impulse Response Analysis
Vector autoregressive (VAR) models are widely used in practical studies, e. g., forecasting, modelling policy transmission mechanism, and measuring connection of economic agents.
Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach
Despite its paramount importance in the empirical growth literature, productivity convergence analysis has three problems that have yet to be resolved: (1) little attempt has been made to explore the hierarchical structure of industry-level datasets; (2) industry-level technology heterogeneity has largely been ignored; and (3) cross-sectional dependence has rarely been allowed for.
Multiple-index Nonstationary Time Series Models: Robust Estimation Theory and Practice
This paper proposes a class of parametric multiple-index time series models that involve linear combinations of time trends, stationary variables and unit root processes as regressors.
Interactive Effects Panel Data Models with General Factors and Regressors
This paper considers a model with general regressors and unobservable factors.
Higher-order Expansions and Inference for Panel Data Models
In this paper, we propose a simple inferential method for a wide class of panel data models with a focus on such cases that have both serial correlation and cross-sectional dependence.
Time-Varying Multivariate Causal Processes
In this paper, we consider a wide class of time-varying multivariate causal processes which nests many classic and new examples as special cases.
Retrieval Based Time Series Forecasting
In this paper, we show both theoretically and empirically that the uncertainty could be effectively reduced by retrieving relevant time series as references.
Robust M-Estimation for Additive Single-Index Cointegrating Time Series Models
Generalized functions incorporate local integrable functions, the so-called regular generalized functions, while the so-called singular generalized functions (e. g. Dirac delta function) can be obtained as the limits of a sequence of sufficient smooth functions, so-called regular sequence in generalized function context.
Time-Varying Vector Error-Correction Models: Estimation and Inference
This paper considers a time-varying vector error-correction model that allows for different time series behaviours (e. g., unit-root and locally stationary processes) to interact with each other to co-exist.
A Localized Neural Network with Dependent Data: Estimation and Inference
In this paper, we propose a localized neural network (LNN) model and then develop the LNN based estimation and inferential procedures for dependent data in both cases with quantitative/qualitative outcomes.
Smoothing the Nonsmoothness
To tackle difficulties for theoretical studies in situations involving nonsmooth functions, we propose a sequence of infinitely differentiable functions to approximate the nonsmooth function under consideration.
Estimation and Inference for a Class of Generalized Hierarchical Models
In this paper, we consider estimation and inference for the unknown parameters and function involved in a class of generalized hierarchical models.
Estimation and Inference for Three-Dimensional Panel Data Models
Hierarchical panel data models have recently garnered significant attention.
