Fiscal policy, macroeconomic performance and industry structure in a small open economy

We analyse how fiscal policy affects both the macroeconomy and the industry structure, using a multi-sector macroeconomic model of the Norwegian economy with an inflation targeting monetary policy. Our simulations show that the magnitude of the government spending and labour tax cut multipliers, whether monetary policy is active or passive, is comparable to what is found in the literature. A novel finding from our simulations is that the industry structure is substantially affected by an expansionary fiscal policy, as value added in the non-traded goods sector increases at the expense of value added in the traded goods sector. Moreover, expansionary fiscal policy reduces the mark-ups in the traded goods sector, while the mark-ups are roughly unchanged in the non-traded goods sector. The contraction of activity in the traded goods sector increases when monetary tightening accompanies the fiscal stimulus. Hence, we find that such a policy mix is likely to produce significant de-industrialization in a small open economy with inflation targeting.publishedVersio

COVID-19, tapt verdiskaping og finanspolitikkens rolle. Utredning for Koronakommisjonen

Ifølge våre beregninger førte pandemien til at BNP for Fastlands-Norge i 2020 falt med 145 milliarder kroner, eller 4,7 prosent, sammenlignet med en bane uten korona. Samlet for perioden 2020–2023 anslås det neddiskonterte tapet til rundt 330 milliarder kroner, tilsvarende 11 prosent av årlig BNP for Fastlands-Norge. Anslaget er svært usikkert. Ettersom pandemien fullstendig har dominert verdensøkonomien siden midten av mars, har vi med få unntak lagt til grunn at avvik fra prognosen som ble laget i desember 2019 skyldes korona

Coordination between production and sales planning in an oil company based on Lagrangean Decomposition

In our work, we study the issues between production and sales planning processes in an oil company . The planning problems involve decisions regarding procurement of crude oil, generation of components , blending of products, internal transportation of components and products , operation of depots , and sale s and distri bution of products to markets . We formulate two separated planning problems in a decoupled setting i.e. production model solved by the production department (PD) and sales model solved by the sales department (SD). Sales planning problem is formulated in s everal ways, considering different scenarios for allocation of depot operation decision and calculation of departmental premium. In addition, we consider two different formulations of re venue functions in each of the sales problems. The first way assume s q uadratic programming model with linear demand function s , whereas the second one assume s a piecewise linear approximation of the revenue function and is a mixed integer programming model. The sale s model maximizes the premium received by SD, whereas the pro duction model minimize s the cost s based on the demand from SD. We also present integrated models that assume centralized planning and maximize the company's total profit. Because in many cases integrated planning is not possible in practice, these models s erve only as a theoreti cal benchmark . We assume that coordination between the departments is achieved through internal prices. We propose two mechanisms for setting internal prices. The first mechanism includes two cost based - methods , whereas the second mechanism is based on Lagrangea n Decomposition (LD). Then we present numerical example to illustrate the methodologies. We study the performance of each of the mechanisms and compare the results achieved under different scenarios. We illustrate the potent ial advantages and possible disadvantages of LD over the cost - based methods and discuss the allocation of decision - making and sharing rule , in which the company attain s a better outcome under the decoupled planning

The shale oil boom and the U.S. economy: Spillovers and time-varying effects

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 - effcts that were not present before the shale oil boom.publishedVersio

News-Driven Inflation Expectations and Information Rigidities

We investigate the role played by the media in the expectations formation process of households. Using a novel news-topic-based approach we show that news types the media choose to report on, e.g., fiscal policy, health, and politics, are good predictors of households' stated inflation expectations. In turn, in a noisy information model setting, augmented with a simple media channel, we document that the underlying time series properties of relevant news topics explain the time-varying information rigidity among households. As such, we not only provide a novel estimate showing the degree to which information rigidities among households varies across time, but also provide, using a large news corpus and machine learning algorithms, robust and new evidence highlighting the role of the media for understanding inflation expectations and information rigidities.publishedVersio

The Shale Oil Boom and the U.S. Economy: Spillovers and Time-Varying Effects

We analyze if the transmission of oil price shocks on the U.S. economy has changed as a result of the shale oil boom. To do so we allow for spillovers at the state level, as well as aggregate country level effects. We identify and quantify these spillovers using a factor-augmented vector autoregressive (VAR) model, allowing for time-varying changes. In contrast to previous results, we find considerable changes in the way oil price shocks are transmitted: there are now positive spillovers to non-oil investment, employment and production in many U.S. states from an increase in the oil price - effects that were not present before the shale oil boom

The shale oil boom and the U.S. economy: Spillovers and time-varying effects

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 - effcts that were not present before the shale oil boom

News-driven inflation expectations and information rigidities

Using a large news corpus and machine learning algorithms we investigate the role played by the media in the expectations formation process of households, and conclude that the news topics media report on are good predictors of both inflation and inflation expectations. In turn, in a noisy information model, augmented with a simple media channel, we document that the time series features of relevant topics help explain time-varying information rigidity among households. As such, we provide a novel estimate of state-dependent information rigidities and present new evidence highlighting the role of the media in understanding inflation expectations and information rigidities

News-Driven Inflation Expectations and Information Rigidities

We investigate the role played by the media in the expectations formation process of households. Using a novel news-topic-based approach we show that news types the media choose to report on, e.g., fiscal policy, health, and politics, are good predictors of households' stated inflation expectations. In turn, in a noisy information model setting, augmented with a simple media channel, we document that the underlying time series properties of relevant news topics explain the time-varying information rigidity among households. As such, we not only provide a novel estimate showing the degree to which information rigidities among households varies across time, but also provide, using a large news corpus and machine learning algorithms, robust and new evidence highlighting the role of the media for understanding inflation expectations and information rigidities