We show that U.S. shale oil producers exhibit a high degree of short-run price responsiveness, primarily through the timing of well completions and refracturing. Using a novel monthly well-level panel covering over 120,000 shale wells across ten states from 2005 to 2019, we document significant supply adjustments to price signals. The response varies significantly across states and firm types, highlighting the importance of accounting for micro-level heterogeneity in production behavior. Mechanisms include accelerating completion of drilled but uncompleted wells and refracturing older wells when forward-looking price signals are favorable. These findings challenge the common assumption of short-run supply inelasticity and call for oil market models that incorporate operational flexibility and forward-looking behavior of shale oil producers.

This paper proposes a new mixed vector autoregression (MVAR) model to examine the relationship between aggregate time series and functional variables in a multivariate setting. The model facilitates a reexamination of the oil-stock price nexus by estimating the effects of demand and supply shocks from the global market for crude oil on the entire distribution of U.S. stock returns since the late 1980s. We show that the MVAR effectively extracts information from the returns distribution that is more relevant for understanding the oil-stock price nexus beyond simply looking at the first few moments. Using novel functional impulse response functions (FIRFs), we find that oil market demand shocks tend to increase returns, while both demand and supply shocks reduce volatility, and have an asymmetric effect on the returns distribution as a whole. In a value-at-risk (VaR) analysis, we also find that the oil market contains important information that reduces expected loss, and that the response of VaR to the oil market demand and supply shocks has changed over time.

We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions.

We compare oil and natural gas price forecasting in a unified pseudo-real-time framework. While oil price forecasting is well established, natural gas has received far less attention: its markets remain regionalized, with prices set at separate hubs rather than in a globally integrated market. Adapting oil models to gas, we assess how market structure shapes model transferability and accuracy. Forecast combinations consistently outperform individual models for both commodities, but gains are far larger for natural gas, where localized pricing leaves more room for improvement. Optimal weighting also differs: equal weights dominate for oil, whereas performance-based weights are superior for gas.

After decades of low and stable inflation, advanced economies experienced a sharp and persistent surge in inflation following the COVID-19 pandemic. While many studies have examined the sources of this inflation, less attention has been paid to how domestic inflation expectations amplify global shocks. This paper makes a novel contribution by quantifying that amplification mechanism across six advanced, inflation-targeting economies: the United States, Canada, New Zealand, the Euro Area, the United Kingdom, and Norway. Using a structural Bayesian vector autoregression model, we jointly identify global demand and supply shocks, including various oil market shocks and global supply chain disruptions, as well as domestic shocks to inflation and inflation expectations. We show that these global shocks were key drivers of the post-pandemic inflation surge in all countries studied. Importantly, our counterfactual analysis reveals that inflation expectations have significantly amplified the transmission of global shocks, particularly in Canada, New Zealand, and the US. These findings demonstrate that the interaction between global forces and country-specific expectations is central to understanding inflation dynamics, and underscore the importance of managing inflation expectations as a tool to mitigate persistent inflation.

We analyse fiscal policy in resource-rich economies using a novel Bayesian regime-switching panel model. The identified regimes capture pro- or countercyclical fiscal behaviour by allowing regime-specific shifts in the average fiscal stance, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to a panel of sixteen oil-producing economies, we show that fiscal policy has alternated between a procyclical and countercyclical regime multiple times over the sample. Furthermore, we find that fiscal policy is more volatile in the procyclical regime and that the probability of being in the procyclical regime is higher for OPEC countries than for non-OPEC countries. We also show that following either an increase or decrease in oil revenues, the growth in government expenditures is mostly increasing, suggesting an upward bias in expenditures in oil-producing countries. These are new findings in the literature.

Summary We provide new evidence that the transmission of oil price shocks to the US economy has changed with the shale oil boom. To show this, we develop a time‐varying parameter factor‐augmented vector autoregressive (FAVAR) model with a large data environment of state‐level, industry, and aggregate US data. The model effectively captures potential spillovers between oil and non‐oil industries, as well as variation over time. Specified in this way, we find that investment, income, industrial production, and (non‐oil) employment in most oil‐producing and some manufacturing‐intensive US states increase following an oil‐specific shock—effects that were not present before the shale oil boom.

Inflation expectations and the associated pass-through of oil price shocks depend on demand and supply conditions underlying the global oil market. We establish this result using a structural VAR model of the global oil market that jointly identifies transmissions of oil demand and supply shocks through real oil prices to both expected and actual inflation. We demonstrate that economic activity shocks have a significantly longer-lasting effect on inflation expectations and actual inflation than other types of real oil price shocks, and resolve disagreements around the role of oil prices in explaining the missing deflation puzzle of the Great Recession.

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We analyse fiscal policy responses in oil rich countries by developing a Bayesian regimes-witching panel country analysis. We use parameter restrictions to identify procyclical and countercyclical fiscal policy regimes over the sample in 23 OECD and non-OECD oil producing countries. We find that fiscal policy is switching between pro- and countercyclial regimes multiple times. Furthermore, for all countries, fiscal policy is more volatile in the countercyclical regime than in the procyclical regime. In the procyclical regime, however, fiscal policy is systematically more volatile and excessive in the non-OECD (including OPEC) countries than in the OECD countries. This suggests OECD countries are able to smooth spending and save more than the non-OECD countries. Our results emphasize that it is both possible and important to separate a procyclical regime from a countercyclical regime when analysing fiscal policy. Doing so, we have encountered new facts about fiscal policy in oil rich countries.

We analyse whether central banks in small open commodity exporting and importing countries respond to exchange rate movements, taking into consideration that there may be structural changes in parameters and volatility. Using a Markov Switching Rational Expectations framework, we estimate the model for Australia, Canada, New Zealand, Norway, Sweden and the UK. We find that the size of policy responses, and the volatility of structural shocks, have not stayed constant over the estimation sample. Furthermore, monetary policy has responded strongly to the exchange rate for many commodity exporters, most notablyNorway. This has had a stabilizing effect on the exchange rate. In particular, although the terms of trade are highly volatile among commodity exporters, the exchange rate has about the same volatility across all importers and exporters in the recent period.

Our analysis suggests; they do not! To arrive at this conclusion we construct a real-time data set of interest rate projections from central banks in three small open economies; New Zealand, Norway, and Sweden, and analyze if revisions to these projections (i.e., forward guidance) can be predicted by timely information. Doing so, we find a systematic role for forward looking international indicators in predicting the revisions to the interest rate projections in all countries. In contrast, using similar indexes for the domestic economy yields largely insignificant results. Furthermore, we find that revisions to forward guidance matter. Using a VAR identified with external instruments based on forecast errors from the predictive regressions, we show that the responses to output, inflation, the exchange rate and asset returns resemble those one typically associates with a conventional monetary policy shock.

In this paper we develop the first model to incorporate the dynamic productivity consequences of both the spending effect and the resource movement effect of oil abundance. We show that doing so dramatically alters the conclusions drawn from earlier models of learning by doing (LBD) and the Dutch disease. In particular, the resource movement effect suggests that the growth effects of natural resources are likely to be positive, turning previous growth results in the literature relying on the spending effect on their head. We motivate the relevance of our approach by the example of a major oil producer, Norway. Empirically we find that the effects of an increase in the price of oil may resemble results found in the earlier Dutch disease literature, while the effects of increased oil activity increases productivity in most industries. Therefore, models that only focus on windfall gains due to increased spending potential from higher oil prices, would conclude – incorrectly based on our analysis – that the resource sector cannot be an engine of growth.

We analyze if the transmission of oil price shocks on the U.S. economy has changed with the shale oil boom. To do so, we put forward a framework that allows for spillovers between industries and learning by doing (LBD) over time. We identify these spillovers using a time-varying parameter factor-augmented vector autoregressive (VAR) model with both state level and country level data. In contrast to previous results, we find considerable changes in the way oil price shocks are transmitted to the U.S economy: there are now positive spillovers to non-oil investment, employment and production from an increase in the oil price effects that were not present before the shale oil boom.

We estimate a regime-switching DSGE model with a banking sector to explain incomplete and asymmetric interest rate pass-through, especially in the presence of a binding zero lower bound (ZLB) constraint. The model is estimated using Bayesian techniques on US data between 1985 and 2016. The framework allows us to explain the time-varying interest rate spreads and pass-through observed in the data. We ?nd that pass-through tends to be delayed in the short run, and incomplete in the long run. All this impacts the dynamics of the other macroeconomic variables in the model. In particular, we ?nd monetary policy to be less e?ective under incomplete pass-through. Furthermore, the behavior of pass-through in the loan rate is di?erent from that of the deposit rate shocks. This creates asymmetric dynamics at the zero lower bound, and incomplete pass-through exacerbates that asymmetry.

Summary This paper provides new results to the literature, showing that output flexibility in oil production depends on the extraction technology. In particular, constructing a novel well‐level monthly production dataset covering more than 16,000 crude oil wells in North Dakota, we find supply elasticity of shale wells to be positive and in the range of 0.3–0.9, depending on wells and firms characteristics. We find no such responses for conventional wells. We interpret the supply pattern of shale oil wells to be consistent with the Hotelling theory of optimal extraction. Reserves are an inventory, and the decision to produce is an intertemporal choice of when to draw down below‐ground inventory.

In this paper we develop the first model to incorporate the dynamic productivity consequences of both the spending effect and the resource movement effect of oil abundance. We show that doing so dramatically alters the conclusions drawn from earlier models of learning by doing (LBD) and the Dutch disease. In particular, the resource movement effect suggests that the growth effects of natural resources are likely to be positive, turning previous growth results in the literature relying on the spending effect on their head. We motivate the relevance of our approach by the example of a major oil producer, Norway. Empirically we find that the effects of an increase in the price of oil may resemble results found in the earlier Dutch disease literature, while the effects of increased oil activity increases productivity in most industries. Therefore, models that only focus on windfall gains due to increased spending potential from higher oil prices, would conclude incorrectly based on our analysis that the resource sector cannot be an engine of growth.

In this paper we develop the first model to incorporate the dynamic productivity consequences of both the spending effect and the resource movement effect of oil abundance. We show that doing so dramatically alters the conclusions drawn from earlier models of learning by doing (LBD) and the Dutch disease. In particular, the resource movement effect suggests that the growth effects of natural resources are likely to be positive, turning previous growth results in the literature relying on the spending effect on their head. We motivate the relevance of our approach by the example of a major oil producer, Norway, where it seems clear that the predictions based on existing theory do not apply. Although the effects of an increase in the price of oil may resemble results found in the earlier Dutch disease literature, the effects of increased oil activity do not. Therefore, models that only focus on windfall gains due to increased spending potential from higher oil prices, would conclude incorrectly based on our analysis that the resource sector cannot be an engine of