Zobrazeno 1 - 10
of 3 919
pro vyhledávání: '"Ng, Serena"'
Autor:
Goncalves, Silvia, Ng, Serena
A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captu
Externí odkaz:
http://arxiv.org/abs/2403.08130
Autor:
Ng, Serena, Scanlan, Susannah
Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a lo
Externí odkaz:
http://arxiv.org/abs/2303.01863
Autor:
Bai, Jushan, Ng, Serena
Pervasive cross-section dependence is increasingly recognized as a characteristic of economic data and the approximate factor model provides a useful framework for analysis. Assuming a strong factor structure where $\Lop\Lo/N^\alpha$ is positive defi
Externí odkaz:
http://arxiv.org/abs/2109.03773
Autor:
Davis, Richard, Ng, Serena
This paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to accommodate a disaster-type variable with infinite variance into a SVAR. We show that the least squ
Externí odkaz:
http://arxiv.org/abs/2107.06663
Economists are blessed with a wealth of data for analysis, but more often than not, values in some entries of the data matrix are missing. Various methods have been proposed to handle missing observations in a few variables. We exploit the factor str
Externí odkaz:
http://arxiv.org/abs/2103.03045
Autor:
Ng, Serena
The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-covid forecasting models inadequate. It also creates a new problem for estimation of economic
Externí odkaz:
http://arxiv.org/abs/2103.02732
Autor:
Forneron, Jean-Jacques, Ng, Serena
This paper illustrates two algorithms designed in Forneron & Ng (2020): the resampled Newton-Raphson (rNR) and resampled quasi-Newton (rqN) algorithms which speed-up estimation and bootstrap inference for structural models. An empirical application t
Externí odkaz:
http://arxiv.org/abs/2102.10443
Autor:
Bai, Jushan, Ng, Serena
Estimates of the approximate factor model are increasingly used in empirical work. Their theoretical properties, studied some twenty years ago, also laid the ground work for analysis on large dimensional panel data models with cross-section dependenc
Externí odkaz:
http://arxiv.org/abs/2008.00254
Autor:
Lee, Sokbae, Ng, Serena
Researchers may perform regressions using a sketch of data of size $m$ instead of the full sample of size $n$ for a variety of reasons. This paper considers the case when the regression errors do not have constant variance and heteroskedasticity robu
Externí odkaz:
http://arxiv.org/abs/2007.07781
Autor:
Forneron, Jean-Jacques, Ng, Serena
Assessing sampling uncertainty in extremum estimation can be challenging when the asymptotic variance is not analytically tractable. Bootstrap inference offers a feasible solution but can be computationally costly especially when the model is complex
Externí odkaz:
http://arxiv.org/abs/2004.09627