Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit

Khalaf, Lynda; Kichian, Maral; Saunders, Charles J.; Voia, Marcel

doi:10.1016/j.jeconom.2020.04.015

Cited by 18 publications

(22 citation statements)

References 35 publications

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“…In practice, dictionaries lead to the design matrix X structured appropriately; see Babii, Ghysels, and Striaukas (2020b) for more details on how to construct the design matrix. Note that such weights depend linearly on the parameter β k which allows for the efficient estimation of the high-dimensional MIDAS panel regression model, cf., Khalaf, Kichian, Saunders, and Voia (2020) for the low-dimensional non-linear case. A suitable dictionary for our purposes are Legendre polynomials, which are in the class of orthogonal polynomials and have very good approximating properties.…”

Section: Mixed Frequency Data Panelsmentioning

confidence: 99%

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Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

et al. 2020

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Section: Mixed Frequency Data Panelsmentioning

confidence: 99%

“…Our paper is related to the recent work of Fosten and Greenaway-McGrevy (2019) and Khalaf, Kichian, Saunders, and Voia (2020) who focus on nowcasting and/or mixed frequency panel data. In contrast to Khalaf, Kichian, Saunders, and Voia (2020), we explore the time dimension and introduce the LASSO-type regularization.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

et al. 2020

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“…A recent paper by Khalaf et al . (2021) studies the dynamic panel data model with MIDAS. They extend the GMM methods of Anderson and Hsiao (1982) and Arellano and Bond (1991) to dynamic panel models with MIDAS.…”

Section: Introductionmentioning

confidence: 99%

“…Another difference is that our paper attempts to extend the Mundlak and Chamberlain approach to the panel MIDAS setting, which is not considered in Khalaf et al . (2021).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Panel Data Models with Mixed Sampling Frequencies*

Yang

Jia

2023

Oxf Bull Econ Stat

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Standard panel models usually assume that data are available at the same frequency. Occasionally, researchers might work with variables sampled at different frequencies. A common practice is to aggregate all variables to the same frequency by an equal weighting scheme. We show that such a simple aggregation scheme results in biases for common estimators. We propose a data-driven method to determine weights for aggregation. We further demonstrate that, in contrast with single-frequency panel models, the Mundlak device and the Chamberlain's approach lead to different estimators for panels with mixed sampling frequencies. The proposed estimators have satisfying finite sample performances in various simulation designs. As an empirical illustration, we apply the new method to the estimation of the effects of temperature fluctuations on economic growth. The empirical evidence shows that the temperature shocks mainly work through the level effect instead of the growth effect for poor countries.JEL Classification numbers: C23, C51. *The authors thank the editor Debopam Bhattacharya and two anonymous referees for their constructive comments.

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“…First, following Ghysels, Santa-Clara, and Valkanov (2004), Ghysels, Sinko, and Valkanov (2007), and Andreou, Ghysels, and Kourtellos (2010), there is an increasing interest in using datasets sampled at different frequencies in the empirical practice. Most of this literature, with a notable exception for Ghysels and Wright (2010) and Khalaf, Kichian, Saunders, and Voia (2017), is largely focused on the forecasting problem with mixed-frequency data and does not consider the structural econometric modeling with the instrumental variable approach. The mixed-frequency data typically lead to high-dimensional problems and the dimensionality is controlled using tightly parametrized weight functions, see also Foroni, Marcellino, and Schumacher (2015) for the unrestricted mixed-frequency data mod-els.…”

Section: Introductionmentioning

confidence: 99%

High-dimensional mixed-frequency IV regression

Babii¹

2020

Preprint

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This paper introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonovregularized estimator and derive the convergence rate of its mean-integrated squared error for time series data. The estimator has a closed-form expression that is easy to compute and demonstrates excellent performance in our Monte Carlo experiments. We estimate the real-time price elasticity of supply on the Australian electricity spot market. Our estimates suggest that the supply is relatively inelastic and that its elasticity is heterogeneous throughout the day. * First draft: October 2014. This paper is a substantially revisited Chapter 2 of my Ph.D. thesis. I'm deeply indebted to my advisor Jean-Pierre Florens and other members of my Ph.D. committee: Eric Gautier, Ingrid van Keilegom, and Timothy Christensen for helpful discussions and suggestions.

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Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit

Cited by 18 publications

References 35 publications

Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

Machine Learning Panel Data Regressions with an Application to Nowcasting Price Earnings Ratios

Estimation of Panel Data Models with Mixed Sampling Frequencies*

High-dimensional mixed-frequency IV regression

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