nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒04‒24
seven papers chosen by
Jaqueson K. Galimberti
Asian Development Bank

  1. Cointegration without Unit Roots By Duffy, J.; Simons, J.
  2. The Vector Error Correction Index Model: Representation, Estimation and Identification By Gianluca Cubadda; Marco Mazzali
  3. Factor Augmented Vector-Autoregression with narrative identification. An application to monetary policy in the US By Giorgia De Nora
  4. Impulse response estimation via fexible local projections By Haroon Mumtaz; Michele Piffer
  5. sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings By Luke Mosley; Tak-Shing Chan; Alex Gibberd
  6. Uncertain Prior Economic Knowledge and Statistically Identified Structural Vector Autoregressions By Sascha A. Keweloh
  7. The Initial and Dynamic Effects of the COVID-19 Pandemic on Crime in New Zealand By Lydia Cheung; Philip Gunby

  1. By: Duffy, J.; Simons, J.
    Abstract: It has been known since Elliott (1998) that standard methods of inference on cointegrating relationships break down entirely when autoregressive roots are near but not exactly equal to unity. We consider this problem within the framework of a structural VAR, arguing this it is as much a problem of identification failure as it is of inference. We develop a characterisation of cointegration based on the impulse response function, which allows long-run equilibrium relationships to remain identified even in the absence of exact unit roots. Our approach also provides a framework in which the structural shocks driving the common persistent components continue to be identified via long-run restrictions, just as in an SVAR with exact unit roots. We show that inference on the cointegrating relationships is affected by nuisance parameters, in a manner familiar from predictive regression; indeed the two problems are asymptotically equivalent. By adapting the approach of Elliott, Müller and Watson (2015) to our setting, we develop tests that robustly control size while sacrificing little power (relative to tests that are efficient in the presence of exact unit roots).
    Keywords: Cointegration, inference, near-integrated processes, least-favourable distributions, nuisance parameters, power
    JEL: C01 C32 C40 C80
    Date: 2023–04–12
  2. By: Gianluca Cubadda (CEIS, Università di Roma ‘Tor Vergata’); Marco Mazzali (Università di Roma ‘Tor Vergata’)
    Abstract: This paper extends the multivariate index autoregressive model by Reinsel (1983) to the case of cointegrated time series of order (1, 1). In this new modelling, namely the Vector Error-Correction Index Model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves a substantial dimension reduction w.r.t. the Vector Error Correction Model. We show that the VECIM allows one to decompose the reduced form errors into sets of common and uncommon shocks, and that the former can be further decomposed into permanent and transitory shocks. Moreover, we offer a switching algorithm for optimal estimation of the VECIM. Finally, we document the practical value of the proposed approach by both simulations and an empirical application, where we search for the shocks that drive the aggregate fluctuations at different frequency bands in the US.
    Keywords: Vector autoregressive models, multivariate autoregressive index model, cointegration, reduced-rank regression, dimension reduction, main business cycle shock.
    Date: 2023–04–04
  3. By: Giorgia De Nora (Queen Mary University of London)
    Abstract: I extend the Bayesian Factor-Augmented Vector Autoregressive model (FAVAR) to incorporate an identification scheme based on an exogenous variable approach. A Gibbs sampling algorithm is provided to estimate the posterior distributions of the models parameters. I estimate the effects of a monetary policy shock in the United States using the proposed algorithm, and find that an increase in the Federal Fund Rate has contractionary effects on both the real and financial sides of the economy. Furthermore, the paper suggests that data-rich models play an important role in mitigating price and real economic puzzles in the estimated impulse responses as well as the discrepancies among the impulse responses obtained with different monetary policy instruments.
    Keywords: information sufficiency, factor-augmented VARs, instrumental variables, monetary policy, structural VARs
    JEL: C32 C38 E52
    Date: 2021–12–15
  4. By: Haroon Mumtaz (Queen Mary University London); Michele Piffer (King's College London)
    Abstract: This paper introduces a exible local projection that generalises the model by Jordá (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first application shows that the fiscal multiplier is stronger in recession than expansion only in response to contractionary fiscal shocks, but not in response to expansionary fiscal shocks. We then show that financial shocks generate effects on the economy that increase more than proportionately in the size of the shock when the shock is negative, but not when the shock is positive.
    Keywords: Non-linear models, non-parametric techniques, identification
    JEL: C14 C11 C32 E52
    Date: 2022–04–21
  5. By: Luke Mosley; Tak-Shing Chan; Alex Gibberd
    Abstract: sparseDFM is an R package for the implementation of popular estimation methods for dynamic factor models (DFMs) including the novel Sparse DFM approach of Mosley et al. (2023). The Sparse DFM ameliorates interpretability issues of factor structure in classic DFMs by constraining the loading matrices to have few non-zero entries (i.e. are sparse). Mosley et al. (2023) construct an efficient expectation maximisation (EM) algorithm to enable estimation of model parameters using a regularised quasi-maximum likelihood. We provide detail on the estimation strategy in this paper and show how we implement this in a computationally efficient way. We then provide two real-data case studies to act as tutorials on how one may use the sparseDFM package. The first case study focuses on summarising the structure of a small subset of quarterly CPI (consumer price inflation) index data for the UK, while the second applies the package onto a large-scale set of monthly time series for the purpose of nowcasting nine of the main trade commodities the UK exports worldwide.
    Date: 2023–03
  6. By: Sascha A. Keweloh
    Abstract: This study proposes an estimator that combines statistical identification with economically motivated restrictions on the interactions. The estimator is identified by (mean) independent non-Gaussian shocks and allows for incorporation of uncertain prior economic knowledge through an adaptive ridge penalty. The estimator shrinks towards economically motivated restrictions when the data is consistent with them and stops shrinkage when the data provides evidence against the restriction. The estimator is applied to analyze the interaction between the stock and oil market. The results suggest that what is usually identified as oil-specific demand shocks can actually be attributed to information shocks extracted from the stock market, which explain about 30-40% of the oil price variation.
    Date: 2023–03
  7. By: Lydia Cheung (Auckland University of Technology); Philip Gunby (University of Canterbury)
    Abstract: We use seasonal ARIMA methods to study the imposition and removal of national uniform social distancing restrictions in response to Covid-19 in New Zealand for six crime types in six cities. We then use the estimated models to forecast counterfactual crime trajectories. Novel elements include cleanly defined lockdown periods, two dis- tinct lockdowns with meaningful gaps between them, and sizeable periods after each one to allow for dynamics. We find that social restrictions initially lower offending, subsequent lockdowns have smaller impacts on offending, “bounce back” occurs in criminal offending after their removal, and bounce back is faster from subsequent lockdowns.
    JEL: C22 H75 K14 K42
    Date: 2023–03

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