Estimating DSGE models using seasonally adjusted and unadjusted data

Saijo, Hikaru

doi:10.1016/j.jeconom.2012.10.004

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…In this study, an ARDL model is employed to examine the specifications outlined in Equation ( 4). The analysis uses a dynamic simulation algorithm developed by Jordan and Philips (2018) and was carefully implemented by Abbasi et al ( 2021), Iorember et al (2022), Okere, Muoneke, et al (2023).…”

Section: Dynamic Ardl Simulationsmentioning

confidence: 99%

Balancing the scales: Does public debt and energy poverty mitigate or exacerbate ecological distortions in Nigeria?

Ezenekwe,

Okere,

Dimnwobi

et al. 2023

Int Social Sci J

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Amid Nigeria's economic growth and energy challenges, the escalating public debt (PB) levels and persistent energy poverty (EP) raise critical questions about their potential impacts on the environment. Given the potential conflict among economic development, EP alleviation and ecological conservation, it becomes pertinent to understand whether increased PB and efforts to address EP inadvertently contribute to or alleviate ecological imbalances within the country. Hence, this research investigates the effect of PB and EP on the load capacity factor (LCF) in Nigeria. Using the STIRPAT model and annual data from 1990 to 2021, the study explores the relationships among total PB, EP, gross domestic product per capita, urbanization and LCF. Descriptive analysis, correlation assessments and unit‐root tests precede the data analysis conducted with the autoregressive distributed lag (ARDL) model and dynamic ARDL (DARDL) technique. Key findings reveal significant negative effects of urbanization and EP on LCF. Additionally, the ARDL and DARDL procedure highlights a positive long‐term relationship between PB and LCF. Both ARDL and DARDL analyses show a negative short‐term relationship between GDP growth per capita and LCF, signalling the need for sustainable economic practices. The study concludes with policy recommendations that aim to promote sustainable development and address ecological imbalances by tackling EP and PB challenges in Nigeria.

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Section: Dynamic Ardl Simulationsmentioning

confidence: 99%

Balancing the scales: Does public debt and energy poverty mitigate or exacerbate ecological distortions in Nigeria?

Ezenekwe,

Okere,

Dimnwobi

et al. 2023

Int Social Sci J

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“…An important role of seasonal ‡uctuations in the total variation in aggregate economic variables is well documented in the literature; see, e.g., Barsky and Miron (1989). Ignoring seasonality when estimating dynamic stochastic general equilibrium models may lead to substantial errors in the estimated parameters; see, e.g., Saijo (2013).…”

Section: A Model With Seasonal Changesmentioning

confidence: 99%

“…Seasonal adjustments are a special case of anticipated regime switches; see Barsky and Miron (1989) for well documented evidence on the importance of seasonal changes for the business cycle. Saijo (2013) argues that inadequate treatment of seasonal changes may lead to a signi…cant bias in the parameter estimates. Two approaches are available in the literature to study models with seasonal changes: …rst, Sargent (1993, 2013) use spectral density of variables to construct periodic optimal decision rules; and second, Christiano and Todd (2002) linearize the model around a seasonally-varying steady state growth path and solve for four distinct decision rules.…”

Section: Introductionmentioning

confidence: 99%

A Tractable Framework for Analyzing a Class of Nonstationary Markov Models

Maliar

Taylor

et al. 2015

SSRN Journal

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and graduate students of Econ-288 Computational Economics course at Stanford University. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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“…Third, the identification of their 'deep' parameters remains problematic; see (Canova 2009;Consolo et al 2009). Fourth, the appropriateness of the Hodrick-Prescott (H-P) filter has been seriously challenged; see (Chang et al 2007;Harvey and Jaeger 1993;Saijo 2013). Fifth, the forecasting capacity of DSGE models is rather weak; see (Edge and Gurkaynak 2010).…”

Section: Introductionmentioning

confidence: 99%

Model Validation and DSGE Modeling

Poudyal

Spanos

2022

Econometrics

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The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is argued that most of these weaknesses stem from failing to distinguish between statistical and substantive adequacy and secure the former before assessing the latter. The paper untangles the statistical from the substantive premises of inference to delineate the above-mentioned issues and propose solutions. The discussion revolves around a typical DSGE model using US quarterly data. It is shown that this model is statistically misspecified, and when respecified to arrive at a statistically adequate model gives rise to the Student’s t VAR model. This statistical model is shown to (i) provide a sound basis for testing the DSGE overidentifying restrictions as well as probing the identifiability of the deep parameters, (ii) suggest ways to meliorate its substantive inadequacy, and (iii) give rise to reliable forecasts and policy simulations.

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Estimating DSGE models using seasonally adjusted and unadjusted data

Cited by 5 publications

References 25 publications

Balancing the scales: Does public debt and energy poverty mitigate or exacerbate ecological distortions in Nigeria?

Balancing the scales: Does public debt and energy poverty mitigate or exacerbate ecological distortions in Nigeria?

A Tractable Framework for Analyzing a Class of Nonstationary Markov Models

Model Validation and DSGE Modeling

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