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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Regulating Zombie Mortgages
Jonathan Lee, Duc Duy Nguyen, Huyen Nguyen
IWH Discussion Papers,
No. 16,
2024
Abstract
Using the adoption of Zombie Property Law (ZL) across several US states, we show that increased lender accountability in the foreclosure process affects mortgage lending decisions and standards. Difference-in-differences estimations using a state border design show that ZL incentivizes lenders to screen mortgage applications more carefully: they deny more applications and impose higher interest rates on originated loans, especially risky loans. In turn, these loans exhibit higher ex-post performance. ZL also affects lender behavior after borrowers become distressed, causing them to strategically keep delinquent mortgages alive. Our findings inform the debate on policy responses to foreclosure crises.
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Do Politicians Affect Firm Outcomes? Evidence from Connections to the German Federal Parliament
André Diegmann, Laura Pohlan, Andrea Weber
IWH Discussion Papers,
No. 15,
2024
Abstract
We study how connections to German federal parliamentarians affect firm dynamics by constructing a novel dataset to measure connections between politicians and the universe of firms. To identify the causal effect of access to political power, we exploit (i) new appointments to the company leadership team and (ii) discontinuities around the marginal seat of party election lists. Our results reveal that connections lead to reductions in firm exits, gradual increases in employment growth without improvements in productivity. The economic effects are mediated by better credit ratings while access to subsidies or procurement contracts are documented to be of lower importance.
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Climate Stress Tests, Bank Lending, and the Transition to the Carbon-neutral Economy
Larissa Fuchs, Huyen Nguyen, Trang Nguyen, Klaus Schaeck
IWH Discussion Papers,
No. 9,
2024
Abstract
We ask if bank supervisors’ efforts to combat climate change affect banks’ lending and their borrowers’ transition to the carbon-neutral economy. Combining information from the French supervisory agency’s climate pilot exercise with borrowers’ emission data, we first show that banks that participate in the exercise increase lending to high-carbon emitters but simultaneously charge higher interest rates. Second, participating banks collect new information about climate risks, and boost lending for green purposes. Third, receiving credit from a participating bank facilitates borrowers’ efforts to improve environmental performance. Our findings establish a hitherto undocumented link between banking supervision and the transition to net-zero.
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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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Does IFRS Information on Tax Loss Carryforwards and Negative Performance Improve Predictions of Earnings and Cash Flows?
Sandra Dreher, Sebastian Eichfelder, Felix Noth
Journal of Business Economics,
January
2024
Abstract
We analyze the usefulness of accounting information on tax loss carryforwards and negative performance to predict earnings and cash flows. We use hand-collected information on tax loss carryforwards and corresponding deferred taxes from the International Financial Reporting Standards tax footnotes for listed firms from Germany. Our out-of-sample tests show that considering accounting information on tax loss carryforwards does not enhance performance forecasts and typically even worsens predictions. The most likely explanation is model overfitting. Besides, common forecasting approaches that deal with negative performance are prone to prediction errors. We provide a simple empirical specification to account for that problem.
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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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Conditional Macroeconomic Survey Forecasts: Revisions and Errors
Alexander Glas, Katja Heinisch
Journal of International Money and Finance,
November
2023
Abstract
Using data from the European Central Bank's Survey of Professional Forecasters and ECB/Eurosystem staff projections, we analyze the role of ex-ante conditioning variables for macroeconomic forecasts. In particular, we test to which extent the updating and ex-post performance of predictions for inflation, real GDP growth and unemployment are related to beliefs about future oil prices, exchange rates, interest rates and wage growth. While oil price and exchange rate predictions are updated more frequently than macroeconomic forecasts, the opposite is true for interest rate and wage growth expectations. Beliefs about future inflation are closely associated with oil price expectations, whereas expected interest rates are related to predictions of output growth and unemployment. Exchange rate predictions also matter for macroeconomic forecasts, albeit less so than the other variables. With regard to forecast errors, wage growth and GDP growth closely comove, but only during the period when interest rates are at the effective zero lower bound.
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Climate Stress Tests, Bank Lending, and the Transition to the Carbon-Neutral Economy
Larissa Fuchs, Trang Nguyen, Klaus Schaeck
SSRN Working Papers,
No. 4427729,
2023
Abstract
Does banking supervision affect borrowers’ transition to the carbon-neutral economy? We use a unique identification strategy that combines the French bank climate pilot exercise with borrowers’ carbon emissions to present two novel findings. First, climate stress tests actively facilitate borrowers’ transition to a low-carbon economy through a lending channel. Stress-tested banks increase loan volumes but simultaneously charge higher interest rates for brown borrowers. Second, additional lending is associated with some improvements in environmental performance. While borrowers commit more to reduce carbon emissions and are more likely to evaluate environmental effects of their projects, they neither reduce direct carbon emissions, nor terminate relationships with environmentally unfriendly suppliers. Our findings establish a causal link between bank climate stress tests and borrowers’ reductions in transition risk.
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East Germany
The Nasty Gap 30 years after unification: Why East Germany is still 20% poorer than the West Dossier In a nutshell The East German economic convergence process is hardly…
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