Climate-resilient Economic Development in Vietnam: Insights from a Dynamic General Equilibrium Analysis (DGE-CRED)
Andrej Drygalla, Katja Heinisch, Christoph Schult
IWH Technical Reports,
No. 1,
2024
Abstract
In a multi-sector and multi-region framework, this paper employs a dynamic general equilibrium model to analyze climate-resilient economic development (DGE-CRED) in Vietnam. We calibrate sector and region-specific damage functions and quantify climate variable impacts on productivity and capital formation for various shared socioeconomic pathways (SSPs 119, 245, and 585). Our results based on simulations and cost-benefit analyses reveal a projected 5 percent reduction in annual GDP by 2050 in the SSP 245 scenario. Adaptation measures for the dyke system are crucial to mitigate the consumption gap, but they alone cannot sufficiently address it. Climate-induced damages to agriculture and labor productivity are the primary drivers of consumption reductions, underscoring the need for focused adaptation measures in the agricultural sector and strategies to reduce labor intensity as vital policy considerations for Vietnam’s response to climate change.
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Optimal Monetary Policy in a Two-sector Environmental DSGE Model
Oliver Holtemöller, Alessandro Sardone
IWH Discussion Papers,
No. 18,
2024
Abstract
In this paper, we discuss how environmental damage and emission reduction policies affect the conduct of monetary policy in a two-sector (clean and dirty) dynamic stochastic general equilibrium model. In particular, we examine the optimal response of the interest rate to changes in sectoral inflation due to standard supply shocks, conditional on a given environmental policy. We then compare the performance of a nonstandard monetary rule with sectoral inflation targets to that of a standard Taylor rule. Our main results are as follows: first, the optimal monetary policy is affected by the existence of environmental policy (carbon taxation), as this introduces a distortion in the relative price level between the clean and dirty sectors. Second, compared with a standard Taylor rule targeting aggregate inflation, a monetary policy rule with asymmetric responses to sector-specific inflation allows for reduced volatility in the inflation gap, output gap, and emissions. Third, a nonstandard monetary policy rule allows for a higher level of welfare, so the two goals of welfare maximization and emission minimization can be aligned.
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
No. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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Is Risk the Fuel of the Business Cycle? Financial Frictions and Oil Market Disturbances
Christoph Schult
IWH Discussion Papers,
No. 4,
2024
Abstract
I estimate a dynamic stochastic general equilibrium (DSGE) model for the United States that incorporates oil market shocks and risk shocks working through credit market frictions. The findings of this analysis indicate that risk shocks play a crucial role during the Great Recession and the Dot-Com bubble but not during other economic downturns. Credit market frictions do not amplify persistent oil market shocks. This result holds as long as entry and exit rates of entrepreneurs are independent of the business cycle.
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Searching where Ideas Are Harder to Find – The Productivity Slowdown as a Result of Firms Hindering Disruptive Innovation
Richard Bräuer
IWH Discussion Papers,
No. 22,
2023
Abstract
This paper proposes to explain the productivity growth slowdown with the poaching of disruptive inventors by firms these inventors threaten with their research. I build an endogenous growth model with incremental and disruptive innovation and an inventor labor market where this defensive poaching takes place. Incremental firms poach more as they grow, which lowers the probability of disruption and makes large incremental firms even more prevalent. I perform an event study around disruptive innovations to confirm the main features of the model: Disruptions increase future research productivity, hurt incumbent inventors and raise the probability of future disruption. Without disruption, technology classes slowly trend even further towards incrementalism. I calibrate the model to the global patent landscape in 1990 and show that the model predicts 52% of the decline of disruptive innovation until 2010.
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People
People Doctoral Students PhD Representatives Alumni Supervisors Lecturers Coordinators Doctoral Students Afroza Alam (Supervisor: Reint Gropp ) Julian Andres Diaz Acosta…
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Media Response
Media Response October 2024 IWH: Autohaus Staffel mit sieben Standorten in der vorläufigen Insolvenz in: Coburger Tageblatt, 31.10.2024 IWH: In der Zinsfalle in: Der Treasurer,…
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Department Profiles
Research Profiles of the IWH Departments All doctoral students are allocated to one of the four research departments (Financial Markets – Laws, Regulations and Factor Markets –…
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Projects
Our Projects 07.2022 ‐ 12.2026 Evaluation of the InvKG and the federal STARK programme On behalf of the Federal Ministry of Economics and Climate Protection, the IWH and the RWI…
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Uncovering Disaggregated Oil Market Dynamics: A Full-Information Approach to Granular Instrumental Variables
Christiane Baumeister, James D. Hamilton
Working Paper,
2023
Abstract
The world price of oil is determined by the interactions of multiple producers and consumers who face different constraints and shocks. We show how this feature of the oil market can be used to estimate local and global elasticities of supply and demand and provide a rich set of testable restrictions. We develop a novel approach to estimation based on full-information maximum likelihood that generalizes the insights from granular instrumental variables. We conclude that the supply responses of Saudi Arabia and adjustments of inventories have historically played a key role in stabilizing the price of oil. We illustrate how our structural model can be used to analyze how individual producers and consumers would dynamically adapt to a geopolitical event such as a major disruption in the supply of oil from Russia.
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