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Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence

Journal of econometrics, 2014-01, Vol.178 (2), p.352-367

2014

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Testing for structural stability of factor augmented forecasting models

Journal of econometrics, 2014-09, Vol.182 (1), p.100-118

2014

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Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods

International journal of forecasting, 2018-04, Vol.34 (2), p.339-354

2018

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Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

Journal of econometrics, 2015-08, Vol.187 (2), p.606-621

2015

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New evidence of the marginal predictive content of small and large jumps in the cross-section

Econometrics, 2020-06, Vol.8 (2), p.1-52

2020

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Fixed and long time span jump tests: New Monte Carlo and empirical evidence

Econometrics, 2019-03, Vol.7 (1), p.1-32

2019

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Forecasting volatility using double shrinkage methods

Journal of empirical finance, 2021-06, Vol.62, p.46-61

2021

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Jackknife estimation of a cluster-sample IV regression model with many weak instruments

Journal of econometrics, 2023-08, Vol.235 (2), p.1747-1769

2023

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Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

International journal of forecasting, 2019-04, Vol.35 (2), p.555-572

2019

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Money and output viewed through a rolling window

Journal of monetary economics, 1998-06, Vol.41 (3), p.455-474

Swanson, Norman R.

1998

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Testing for jumps and jump intensity path dependence

Journal of econometrics, 2018-06, Vol.204 (2), p.248-267

2018

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Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008

Empirical economics, 2023-03, Vol.64 (3), p.1421-1469

2023

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A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks

The review of economics and statistics, 1997-11, Vol.79 (4), p.540-550

1997

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Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions

Journal of the American Statistical Association, 1997-03, Vol.92 (437), p.357-367

1997

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Evaluation of dynamic stochastic general equilibrium models based on distributional comparison of simulated and historical data

Journal of econometrics, 2007-02, Vol.136 (2), p.699-723

2007

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Testing overidentifying restrictions with many instruments and heteroskedasticity

Journal of econometrics, 2014-01, Vol.178 (1), p.15-21

2014

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Predictive Inference for Integrated Volatility

Journal of the American Statistical Association, 2011-12, Vol.106 (496), p.1496-1512

2011

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An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series

Journal of econometrics, 2006-03, Vol.131 (1), p.539-578

2006

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Predictive density and conditional confidence interval accuracy tests

Journal of econometrics, 2006-11, Vol.135 (1), p.187-228

2006

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Bootstrap conditional distribution tests in the presence of dynamic misspecification

Journal of econometrics, 2006-08, Vol.133 (2), p.779-806

2006

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Prediction and simulation using simple models characterized by nonstationarity and seasonality

International review of economics & finance, 2015-11, Vol.40, p.312-323

2015

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An introduction to stochastic unit-root processes

Journal of econometrics, 1997-09, Vol.80 (1), p.35-62

1997

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The real-time predictive content of money for output

Journal of monetary economics, 2001-08, Vol.48 (1), p.3-24

2001

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Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models

Econometrics, 2011-04, Vol.161 (2), p.304-324

2011

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An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors

International journal of forecasting, 2024-10, Vol.40 (4), p.1391-1409

2024

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Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence

Testing for structural stability of factor augmented forecasting models

Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods

Empirical evidence on the importance of aggregation, asymmetry, and jumps for volatility prediction

New evidence of the marginal predictive content of small and large jumps in the cross-section

Fixed and long time span jump tests: New Monte Carlo and empirical evidence

Forecasting volatility using double shrinkage methods

Jackknife estimation of a cluster-sample IV regression model with many weak instruments

Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes

Money and output viewed through a rolling window

Testing for jumps and jump intensity path dependence

Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008

A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks

Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions

Evaluation of dynamic stochastic general equilibrium models based on distributional comparison of simulated and historical data

Testing overidentifying restrictions with many instruments and heteroskedasticity

Predictive Inference for Integrated Volatility

An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series

Predictive density and conditional confidence interval accuracy tests

Bootstrap conditional distribution tests in the presence of dynamic misspecification

Prediction and simulation using simple models characterized by nonstationarity and seasonality

An introduction to stochastic unit-root processes

The real-time predictive content of money for output

Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models

An assessment of the marginal predictive content of economic uncertainty indexes and business conditions predictors

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