CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects

Interactive fixed effects are a popular means to model unobserved heterogeneity in panel data. Models with interactive fixed effects are well studied in the low-dimensional case where the number of parameters to be estimated is small. However, they are largely unexplored in the high-dimensional case where the number of parameters is large, potentially much larger than the sample size itself. In this paper, we develop new econometric methods for the estimation of high-dimensional panel data models with interactive fixed effects.

A Nonparametric Panel Model for Climate Data with Seasonal and Spatial Variation

In this paper, we consider a panel data model which allows for heterogeneous time trends at different locations. We propose a new estimation method for the panel data model before we establish an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation presents in both dimensions of the error terms. We examine the finite–sample properties of the proposed model and estimation method through extensive simulated studies.

A Structural Dynamic Factor Model for Daily Global Stock Market Returns

Most stock markets are open for 6-8 hours per trading day. The Asian, European and American stock markets are separated in time by time-zone differences. We propose a statistical dynamic factor model for a large number of daily returns across multiple time zones. Our model has a common global factor as well as continental factors. Under a mild fixed-signs assumption, our model is identified and has a structural interpretation.

Dr Chen Wang

Host:

Prof. Alexey Onatskiy

Visiting from:

27th June - 26th August 2022

empirical
Ang Li

2022

Ang has received a Janeway Institute Scholarship until September 2022.

Research Interests

Empirical Finance and Financial Econometrics

empirical
Linqi Wang (starting July 2022)

Research Interests

Financial Econometrics, Time Series, Forecasting, Empirical Finance

empirical

Nonparametric Estimation of Large Spot Volatility Matrices for High-Frequency Financial Data

In this paper, we consider estimating spot/instantaneous volatility matrices of high-frequency data collected for a large number of assets. We first combine classic nonparametric kernel-based smoothing with a generalised shrinkage technique in the matrix estimation for noise-free data under a uniform sparsity assumption, a natural extension of the approximate sparsity commonly used in the literature. The uniform consistency property is derived for the proposed spot volatility matrix estimator with convergence rates comparable to the optimal minimax one.

Ganesh Karapakula

2021 - 2024

Ganesh has received a Janeway Institute Scholarship until 2024.

Research Interests

Applied Econometrics, Public Finance, Empirical Finance

empirical

Dynamic Autoregressive Liquidity (DArLiQ)

We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator based on conditional moment restrictions and an efficient semiparametric ML estimator based on an iid assumption. We derive large sample properties for both estimators. We further develop a methodology to detect the occurrence of permanent and transitory breaks in the illiquidity process.

Moqin Zhou

Host:

Prof. Oliver Linton

Visiting from:

Feb 2022 - Jan 2023

empirical
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