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We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk.

Recent developments in US house prices mirror those of the 1996–2006 boom, but the recovery in construction activity has been weak. Using data for 254 US metropolitan areas, we show that housing supply elasticities have fallen markedly in recent years. Consistent with this, we find that monetary policy shocks have a stronger effect on house prices during the recent recovery than the previous boom. At the same time, building permits respond less. Finally, we find that housing supply elasticities have declined more in areas where land-use regulation has tightened the most, and in areas that experienced the sharpest housing busts.

Do inflation expectations and the associated pass-through of oil price shocks depend on demand and supply conditions underlying the global market for crude oil? We answer this question with a novel structural vector autoregressive model of the global oil market that jointly identifies transmissions of oil demand and supply shocks through the real price of oil to both expected and realized inflation. Our main insight is that US households form their expectations of inflation differently when faced with long sustained increases in the price of oil, such as the early millennium oil price surge of 2003 to 2008, as compared to short and sharp price fluctuations that characterized much of the twentieth century. We also find that oil demand and supply shocks can explain a large proportion of expected and realized inflation dynamics during multiple periods of economic significance, and resolve disagreements around the role of oil prices in explaining the missing deflation puzzle of the Great Recession.

We evaluate the impact of mortgage regulation on credit volumes, household balance sheets and the reaction to adverse economic shocks. Using a comprehensive dataset of all housing transactions in Norway matched with buyers' balance sheet information from official tax records, we identify causal effects of mortgage loan-to-value (LTV) limits. Our results show that LTV-requirements have substantial effects on credit volumes, especially on the extensive margin. As a result, such requirements contribute to dampening aggregate credit growth. We find that affected households lower their debt uptake and face lower interest expenses, thereby reducing their vulnerability to adverse shocks. However, affected households also deplete liquid assets when purchasing a home, in order to meet the new requirement. This negative effect on liquid savings persists in the years following the house purchase, suggesting that the impact on financial vulnerability at the household level is in fact ambiguous. We illustrate this further by documenting that households affected by the regulation are more likely to sell their home when becoming unemployed compared to non-affected households.

The responsiveness of house prices to monetary policy shocks depends on the nature of the shock—expansionary versus contractionary—and on local housing supply elasticities. These findings are established using a panel of 263 US metropolitan areas. Expansionary monetary policy shocks have a larger impact on house prices in supply-inelastic areas. Contractionary shocks are orthogonal to housing supply elasticities. In supply-elastic areas, contractionary shocks have a greater impact on house prices than expansionary shocks do. The opposite holds true in supply-inelastic areas. We attribute this to asymmetric housing supply adjustments. (JEL E32, E43, E52, R21, R31)

Increasingly, professional forecasters and academic researchers present model-based and subjective or judgment-based forecasts in economics which are accompanied by some measure of uncertainty. In its most complete form this measure is a probability density function for future values of the variables of interest. At the same time combinations of forecast densities are being used in order to integrate information coming from several sources like experts, models and large micro-data sets. Given this increased relevance of forecast density combinations, the genesis and evolution of this approach, both inside and outside economics, is explored. A fundamental density combination equation is specified which shows that various frequentist as well as Bayesian approaches give different specific contents to this density. In its most simplistic case, it is a restricted finite mixture, giving fixed equal weights to the various individual densities. The specification of the fundamental density combination is made more flexible in recent literature. It has evolved from using simple average weights to optimized weights and then to 'richer' procedures that allow for time-variation, learning features and model incompleteness. The recent history and evolution of forecast density combination methods, together with their potential and benefits, are illustrated in a policy making environment of central banks.

We assess the importance of residential investment in predicting economic recessions for an unbalanced panel of 12 OECD countries over the period 1960Q1–2014Q4. Our approach is to estimate various probit models with different leading indicators and evaluate their relative prediction accuracy using the receiver operating characteristic curve. We document that residential investment contains information useful in predicting recessions both in-sample and out-of-sample. This result is robust to adding typical leading indicators, such as the term spread, stock prices, consumer confidence surveys and oil prices. It is shown that residential investment is particularly useful in predicting recessions for countries with high home-ownership rates. Finally, in a separate exercise for the US economy, we show that the predictive ability of residential investment is robust to employing real-time data.

In this paper we use a structural VAR model with time-varying parameters and stochastic volatility to investigate whether the Federal Reserve has responded systematically to asset prices and whether this response has changed over time. To recover the systematic component of monetary policy, we interpret the interest rate equation in the VAR as an extended monetary policy rule responding to inflation, the output gap, house prices and stock prices. We find some time variation in the coefficients for house prices and stock prices but fairly stable coefficients over time for inflation and the output gap. Our results indicate that the systematic component of monetary policy in the US: i) attached a positive weight to real house price growth but lowered it prior to the crisis and eventually raised it again and, ii) only episodically took real stock price growth into account.

The responsiveness of house prices to monetary policy shocks depends on the nature of the shock – expansionary versus contractionary – and on local housing supply elasticities. These findings are established based on a panel of 263 US metropolitan areas. We test and find supporting evidence for the hypothesis that expansionary monetary policy shocks have a larger impact on house prices when supply elasticities are low. Our results also suggest that contractionary shocks are orthogonal to supply elasticities, as implied by downward rigidity of housing supply. A standard theoretical conjecture is that contractionary shocks have a greater impact on house prices than expansionary shocks, as long as supply is not perfectly inelastic. For areas with high housing supply elasticity, our results are in line with this conjecture. However, for areas with an inelastic housing supply, we find that expansionary shocks have a greater impact on house prices than contractionary shocks. We provide evidence that the direction of the asymmetry is related to a momentum effect that is more pronounced when house prices are increasing than when they are falling.

This paper reviews several methods to dene and forecast classical business cycle turning points in Norway, a country which does not have an ocial business cycle indicator. It compares the Bry and Boschan rule (BB), an autoregressive Markov Switching model (MS), and their versions augmented with surveys or nancial indicators, using several vintages of Norwegian Gross Domestic Product as the business cycle indicator. Timing of the business cycles depends on the vintage and the method used. BB provides the most reliable denition of business cycles. A forecasting exercise is also presented: the BB applied to density forecasts augmented with the consumer condence survey, the regional network survey, and the nancial

Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier literature focused on whether there were sizable parameter changes in the early 1980s, in either the conditional mean or variance parameters, and in the subsequent period till the beginning of the new century. In this paper we conduct a similar analysis but focus on the effects of the recent crisis. Using a range of techniques, we provide substantial evidence against parameter stability. The evolution of the unemployment rate seems particularly different relative to its past behavior. We then discuss and evaluate alternative methods to handle parameter instability in a forecasting context. While none of the methods clearly emerges as best, some techniques turn out to be useful to improve the forecasting performance.

In this paper we propose a parametric block wild bootstrap approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. First, Monte Carlo simulations show that predictive densities for the various MIDAS models derived from the block wild bootstrap approach are more accurate, in terms of coverage rates, than predictive densities derived from either a residual-based bootstrap approach or by drawing errors from a normal distribution. This result holds irrespective of the data generating errors being normally independently distributed, serially correlated, heteroskedastic or a mixture of normal distributions. Second, in an empirical exercise we evaluate density forecasts for quarterly U.S. real output growth, exploiting information from typical monthly and weekly series. We show that the block wild bootstrapping approach, applied to the various MIDAS regressions, produces predictive densities for U.S. real output growth that are well-calibrated. Moreover, the relative accuracy, measured in terms of the logarithmic score or the continuous ranked probability score, improves for the various MIDAS specifications as more information becomes available.

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random variables that depend on past nowcasting performance and other learning mechanisms. The combined density scheme is incorporated in a Bayesian Sequential Monte Carlo method which re-balances the set of nowcasted densities in each period using updated information on the time-varying weights. Experiments with simulated data show that CDN works particularly well in a situation of early data releases with relatively large data uncertainty and model incompleteness. Empirical results, based on US real-time data of 120 leading indicators, indicate that CDN gives more accurate density nowc asts of US GDP growth than a model selection strategy and other combination strategies throughout the quarter with relatively large gains for the two first months of the quarter. CDN also provides informative signals on model incompleteness during recent recessions. Focusing on the tails, CDN delivers probabilities of negative growth, that provide good signals for calling recessions and ending economic slumps in real time.

This paper examines the impact of dierent types of oil price shocks on the U.S. economy, using a factor-augmented VAR (FAVAR) approach. The results indicate that when examining the eects of oil price shocks, it is important to account for the interaction between the oil market and the macroeconomy. I nd that oil demand shocks are more important than oil supply shocks in driving several macroeconomic variables, and that the origin of demand shocks matter. Specically, the U.S. economy and monetary policy respond dierently to global demand shocks that have the eect of raising the price of oil and to oil-specic demand shocks.

This paper explores if economic uncertainty alters the macroeconomic influence of monetary policy. We consider several measures of U.S. economic uncertainty, and estimate their interaction eects with monetary policy shocks as identified through structural vector autoregressions. We find that monetary policy shocks aect economic activity considerably weaker when uncertainty is high, consistently with “real-options” eects suggested by models with non-convex adjustment costs. Investment responds two to five times weaker when uncertainty is in its upper instead of its lower decile. High U.S. uncertainty is associated with lower policy influence not only domestically, but in Canada too. JEL-codes: E30, E32, E37

We analyze the importance of demand from emerging and developed economies as drivers of the real price of oil over the last two decades. Using a factor-augmented vector autoregressive (FAVAR) model that allows us to distinguish between different groups of countries, we find that demand from emerging economies (most notably from Asian countries) is more than twice as important as demand from developed countries in accounting for the fluctuations in the real price of oil and in oil production. Furthermore, we find that different geographical regions respond differently to oil supply shocks and oilspecific demand shocks that drive up oil prices, with Europe and North America being more negatively affected than emerging economies in Asia and South America. We demonstrate that this heterogeneity in responses is not only attributable to differences in energy intensity in production across regions but also to degree of openness and the investment share in GDP.

In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a wide selection of individual models within each model class are combined separately. Then, the nowcasts from the three model classes are combined into a single predictive density. We update the density nowcast for every new data release throughout the quarter, and highlight the importance of new information for the evaluation period 1990Q2-2010Q3. Our results show that the logarithmic score of the predictive densities for U.S. GDP increase almost monotonically as new information arrives during the quarter. While the best performing model class is changing during the quarter, the density nowcasts from our combination framework is always performing well both in terms of logarithmic scores and calibration tests. The density combination approach is superior to a simple model selection strategy and also performs better in terms of point forecast evaluation than standard point forecast combinations.

This paper bridges the new open economy factor augmented VAR (FAVAR) studies with the recent findings in the business cycle synchronization literature emphasizing the importance of regional factors. That is, we estimate and identify a three block FAVAR model with separate world, regional and domestic blocks and study the transmission of both global and regional shocks to four small open economies (Canada, New Zealand, Norway and UK). The results show that foreign shocks explain a major share of the variance in all countries, most so shocks that are common to the world. However, regional shocks also play an important role, explaining more than 20 percent of the variance in the variables. Hence in small open economies, the world is not enough. The regional factors impact the four countries differently, though, some through trade and some through consumer sentiment. Our findings of a strong transmission of both global and regional shocks to open economies are in sharp contrast to the evidence from recently developed open economy DSGE models.