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Uniform inference with time series

Project description

Advancing econometric inference

Economic and financial data often involve time series with strong memory and nonstationary dynamics, making hypothesis testing and confidence interval construction challenging. Traditional econometric methods provide invalid inference in processes such as explosive trends, long memory or time-varying parameters. The ERC-funded Persistence project addresses this issue by developing a unified econometric framework for a broad class of time series processes. By creating a new explanatory variable that conforms to standard central limit theory, the approach enables valid inference regardless of the regressor’s stochastic properties. The method simplifies implementation with closed-form estimators and tests, making it practical for real-world applications. This framework promises to advance econometrics, offering robust tools for macroeconomic and financial data analysis which remains valid regardless of the regressor’s dynamics.

Objective

This project proposes a novel econometric approach suited for hypothesis testing and confidence interval construction in the presence of generic time series regressors with arbitrary persistence degree. The project will develop inference for a large class of regressor processes commonly encountered in macroeconomic and financial data, ranging from stationary, local-to-unit-root, explosive, long memory, time-varying parameter and other nonstationary processes as well as multivariate systems containing mixed components. The key idea behind the approach is to build a new explanatory variable from the data which conforms to a standard central limit theory even when the original regressor does not. The resulting instrumental variable estimators based on this endogenously constructed instrument are shown to be asymptotically mixed-Gaussian regardless of the true stochastic nature of the regressor, implying standard inference for any IV-based self-normalised test. The main contribution of the project is to place a large class of nonstandard processes with a wide range of dynamics and memory properties under a common econometric framework which delivers standard inference regardless of the regressor's stochastic properties. The asymptotic development of the procedure requires fundamental theoretical contributions such as a novel Granger-Johansen type representation theory for multivariate time series with mixed stochastic components and the asymptotic analysis of time series with different persistence types. The novel procedure is shown to be valid uniformly across persistence regimes and automatically delivers asymptotically correct inference without a priori knowledge of the regressor's true stochastic nature. In addition to its generality and theoretical coherence, the approach has the added advantage of ease of implementation (with closed-form estimators and tests that employ standard critical values), thus making it suitable for general practical application.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2023-STG

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Host institution

UNIVERSITA CA' FOSCARI VENEZIA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 664 850,00
Address
DORSODURO 3246
30123 VENEZIA
Italy

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Region
Nord-Est Veneto Venezia
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 664 850,00

Beneficiaries (2)

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