The role of forward‐ and backward‐looking information for inflation expectations formation

Assuming that private forecasters learn inflation dynamics to form their inflation expectations and that they believe a hybrid New Keynesian Phillips curve (NKPC) to capture the true data‐generating process of inflation, we aim at establishing the role of backward‐ and forward‐looking information in the inflation expectation formation process. We find that longer term expectations are crucial in shaping shorter horizon expectations. While the influence of backward‐looking information seems to diminish over time, we do not find evidence of a structural break in the expectation formation process of professional forecasters. Our results further suggest that the weight put on longer term expectations does not solely reflect a mean‐reverting process to trend inflation. Rather, it might also capture beliefs about the central bank's long‐run inflation target and its credibility to achieve inflation stabilization.     

Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

Linear and threshold forecasts of output and inflation using stock and housing prices

This study examines whether simple measures of Canadian equity and housing price misalignments contain leading information about output growth and inflation. Previous authors have generally found that the information content of asset prices in general, and equity and housing prices in particular, are unreliable in that they do not systematically predict future economic activity or inflation. However, earlier studies relied on simple linear relationships that would fail to pick up the potential nonlinear effects of asset price misalignments. Our results suggest that housing prices are useful for predicting GDP growth, even within a linear context. Meanwhile, both stock and housing prices can improve inflation forecasts, especially when using a threshold specification. These improvements in forecast performance are relative to the information contained in Phillips‐curve type indicators for inflation and IS‐curve type indicators for GDP growth. Copyright © 2008 John Wiley & Sons, Ltd.     

Do monetary policy transparency and central bank communication reduce interest rate disagreement?

Yes. This study produces evidence that monetary policy transparency and communication policy of the Bank of England have information content in reducing disagreement about interest rate forecasts. Different from most extant studies employing the transparency index derived from official documents of the central banks, this study extends the literature by using a recently developed market-based monetary transparency index. Moreover, this study analyzes forecast disagreement in a multivariate perspective based on survey data of short- and long-term rates over short and long horizons. This study characterizes several patterns on forecast disagreement related to maturities of interest rates, forecast horizons, recessions, forward guidance, credibility, transparency, and communication policy. Interestingly, disagreement among the Monetary Policy Committee in policy rate decisions is associated with lower disagreement among professional forecasters on interest rate outlook, whereas neither announcement of changes in policy rates nor publication of inflation reports affects forecast disagreement. These results have important implications for monetary policymakers in managing market expectations of interest rates.     

Medium‐term forecasts of potential GDP and inflation using age structure information

Economic behaviour as well as economic resources of individuals vary with age. Swedish time series show that the age structure contains information correlated to medium‐term trends in growth and inflation. GDP gaps estimated by age structure regressions are closely related to conventional measures. Monetary policy is believed to affect inflation with a lag of 1 or 2 years. Projections of the population's age structure are comparatively reliable several years ahead and provide additional information to improve on 3–5 years‐ahead forecasts of potential GDP and inflation. Thus there is a potential scope for using age structure based forecasts as an aid to monetary policy formation. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting growth and inflation in an enlarged euro area

We compare models for forecasting growth and inflation in the enlarged euro area. Forecasts are built from univariate autoregressive and single‐equation models. The analysis is undertaken for both individual countries and EU aggregate variables. Aggregate forecasts are constructed by both employing aggregate variables and by aggregating country‐specific forecasts. Using financial variables for country‐specific forecasts tends to add little to the predictive ability of a simple AR model. However, they do help to predict EU aggregates. Furthermore, forecasts from pooling individual country models usually outperform those of the aggregate itself, particularly for the EU25 grouping. This is particularly interesting from the perspective of the European Central Bank, who require forecasts of economic activity and inflation to formulate appropriate economic policy across the enlarged group. Copyright © 2008 John Wiley & Sons, Ltd.     

A linear benchmark for forecasting GDP growth and inflation?

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory‐based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can hardly be beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation. Copyright © 2008 John Wiley & Sons, Ltd.     

Intermediate targets and segmental pathfinding

Neurons must often extend axons over fairly long distances, making multiple changes in their trajectory of growth before arriving at their final target. It has become clear that as growth cones navigate these complex projections, they typically extend toward a number of intermediate targets before they contact their final target. Recent work from a variety of systems has identified intermediate targets that seem to play similar roles in vertebrate and invertebrate nervous system development. From these examples it appears that a general model of axon guidance can be proposed whereby neurons are guided to their targets segmentally. Within each segment, an intermediate target appears to be the primary target for growth cone recognition and thus the completion of the journey to the final target is determined by a series of successful segmental pathfinding decisions.     

Do US Macroeconomic Forecasters Exaggerate their Differences?

Application of the Bernhardt et al. (Journal of Financial Economics 2006; 80 (3): 657–675) test of herding to the calendar‐year annual output growth and inflation forecasts suggests forecasters tend to exaggerate their differences, except at the shortest horizon, when they tend to herd. We consider whether these types of behaviour can help to explain the puzzle that professional forecasters sometimes make point predictions and histogram forecasts which are mutually inconsistent. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting euro area inflation using dynamic factor measures of underlying inflation

Standard measures of prices are often contaminated by transitory shocks. This has prompted economists to suggest the use of measures of underlying inflation to formulate monetary policy and assist in forecasting observed inflation. Recent work has concentrated on modelling large data sets using factor models. In this paper we estimate factors from data sets of disaggregated price indices for European countries. We then assess the forecasting ability of these factor estimates against other measures of underlying inflation built from more traditional methods. The power to forecast headline inflation over horizons of 12 to 18 months is adopted as a valid criterion to assess forecasting. Empirical results for the five largest euro area countries, as well as for the euro area itself, are presented. Copyright © 2005 John Wiley & Sons, Ltd.     

How to make value-driven climate science for policy more ethical

In previous works, I examine inferential methods employed in Probabilistic Weather Event Attribution studies (PEAs), and explored various ways they can be used to aid in climate policy decisions and decision-making about climate justice issues. This paper evaluates limitations of PEAs and considers how PEA researchers’ attributions of “liability” to specific countries for specific extreme weather events could be made more ethical. In sum, I show that it is routinely presupposed that PEA methods are not prone to inductive risks and presuppose that PEA researchers thus have no epistemic consequences or responsibilities for their attributions of liability. I argue that although PEAs are nevertheless crucially useful for practical decision-making, the attributions of liability made by PEA researchers are in fact prone to indicative risks and are influenced by non-epistemic values that PEA researchers should make transparent to make such studies more ethical. Finally, I outline possible normative approaches for making sciences, including PEAs, more ethical; and discuss implications of my arguments for the ongoing debate about how PEAs should guide climate policy and relevant legal decisions.     

Oil shocks: How can OECD countries manage them best?

As the 1990-91 Gulf crisis has emphasized, the risk of new oil shocks remains high in the medium to long run. Although energy intensity has decreased in the major countries since the first two oil shocks, the simulation performed with the MIMOSA world model, which is described in this paper, shows that a permanent rise in oil prices still induces direct strong negative consequences in the OECD: a temporary recession, a durable loss in production and employment and a lasting rise in inflation. Moreover, according to the model and due to national dissimilarities, an oil shock in Europe has some asymmetrical effects which are pointed out here. Further simulations test two different economic policy responses in the OECD: a ‘beggar-thy-neighbor’ policy in which each country tries to ‘export’ its unemployment by curbing domestic inflation and wage growth; and a co-ordinated policy supporting both supply and demand. The first leads to a general deflationist overshooting. The second ensures stabilization with a much lower cost in employment.     

A mid-level approach to modeling scientific communities

This paper provides an account of mid-level models which calibrate highly theoretical agent-based models of scientific communities by incorporating empirical information from real-world systems. As a result, these models more closely correspond with real-world communities, and are better suited for informing policy decisions than extant how-possibly models. I provide an exemplar of a mid-level model of science funding allocation that incorporates bibliometric data from scientific publications and data generated from empirical studies of peer review into an epistemic landscape model. The results of my model show that on a dynamic epistemic landscape, allocating funding by modified and pure lottery strategies performs comparably to a perfect selection funding allocation strategy. These results support the idea that introducing randomness into a funding allocation process may be a tractable policy worth exploring further through pilot studies. My exemplar shows that agent-based models need not be restricted to the abstract and the apriori; they can also be informed by empirical data.     

The informational content of unconventional monetary policy on precious metal markets

This paper investigates the informational content of unconventional monetary policies and its effect on commodity markets, adopting a nonlinear approach for modeling volatility. The main question addressed is how the Bank of England, Bank of Japan, and European Central Bank's (ECB's) announcements concerning monetary easing affect two major commodities: gold and silver. Our empirical evidence based on daily and high‐frequency data suggests that relevant information causes ambiguous valuation adjustments as well as stabilization or destabilization effects. Specifically, there is strong evidence that the Japanese Central Bank strengthens the precious metal markets by increasing their returns and by causing stabilization effects, in contrast to the ECB, which has opposite results, mainly due to the heterogeneous expectations of investors within these markets. These asymmetries across central banks' effects on gold and silver risk–return profile imply that the ECB unconventional monetary easing informational content opposes its stated mission, adding uncertainty in precious metals markets.     

A multi-country dynamic factor model with stochastic volatility for euro area business cycle analysis

This paper develops a dynamic factor model that uses euro area country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country-specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by imposing a factor structure on the shocks to the latent states. We moreover introduce flexible stochastic volatility specifications to control for heteroscedasticity in the measurement errors and innovations to the latent states. Carefully specified shrinkage priors allow for pushing the model towards a homoscedastic specification, if supported by the data. Our measure of the output gap closely tracks other commonly adopted measures, with small differences in magnitudes and timing. To assess whether the model-based output gap helps in forecasting inflation, we perform an out-of-sample forecasting exercise. The findings indicate that our approach yields superior inflation forecasts, both in terms of point and density predictions.     

Forecast Combination for Euro Area Inflation: A Cure in Times of Crisis?

The period of extraordinary volatility in euro area headline inflation starting in 2007 raised the question whether forecast combination methods can be used to hedge against bad forecast performance of single models during such periods and provide more robust forecasts. We investigate this issue for forecasts from a range of short‐term forecasting models. Our analysis shows that there is considerable variation of the relative performance of the different models over time. To take that into account we suggest employing performance‐based forecast combination methods—in particular, one with more weight on the recent forecast performance. We compare such an approach with equal forecast combination that has been found to outperform more sophisticated forecast combination methods in the past, and investigate whether it can improve forecast accuracy over the single best model. The time‐varying weights assign weights to the economic interpretations of the forecast stemming from different models. We also include a number of benchmark models in our analysis. The combination methods are evaluated for HICP headline inflation and HICP excluding food and energy. We investigate how forecast accuracy of the combination methods differs between pre‐crisis times, the period after the global financial crisis and the full evaluation period, including the global financial crisis with its extraordinary volatility in inflation. Overall, we find that forecast combination helps hedge against bad forecast performance and that performance‐based weighting outperforms simple averaging. Copyright © 2017 John Wiley & Sons, Ltd.     

BBVA‐ARIES: a forecasting and simulation model for EMU

This paper describes the BBVA‐ARIES, a Bayesian vector autoregression (BVAR) for the European Economic and Monetary Union (EMU). In addition to providing EMU‐wide growth and inflation forecasts, the model provides an assessment of the interactions between key EMU macroeconomic variables and external ones, such as world GDP or commodity prices. A comparison of the forecasts generated by the model and those of private analysts and public institutions reveals a very positive balance in favour of the model. For their part, the simulations allow us to assess the potential macroeconomic effects of macroeconomic developments in the EMU. Copyright © 2003 John Wiley & Sons, Ltd.     

It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures

Through their consumption behavior, households are responsible for 72% of global greenhouse gas emissions. Thus, they are key actors in reaching the 1.5 °C goal under the Paris Agreement. However, the possible contribution and position of households in climate policies is neither well understood, nor do households receive sufficiently high priority in current climate policy strategies. This paper investigates how behavioral change can achieve a substantial reduction in greenhouse gas emissions in European high-income countries. It uses theoretical thinking and some core results from the HOPE research project, which investigated household preferences for reducing emissions in four European cities in France, Germany, Norway and Sweden. The paper makes five major points: First, car and plane mobility, meat and dairy consumption, as well as heating are the most dominant components of household footprints. Second, household living situations (demographics, size of home) greatly influence the household potential to reduce their footprint, even more than country or city location. Third, household decisions can be sequential and temporally dynamic, shifting through different phases such as childhood, adulthood, and illness. Fourth, short term voluntary efforts will not be sufficient by themselves to reach the drastic reductions needed to achieve the 1.5 °C goal; instead, households need a regulatory framework supporting their behavioral changes. Fifth, there is a mismatch between the roles and responsibilities conveyed by current climate policies and household perceptions of responsibility. We then conclude with further recommendations for research and policy.     

It starts at home? Climate policies targeting household consumption and behavioral decisions are key to low-carbon futures

Through their consumption behavior, households are responsible for 72% of global greenhouse gas emissions. Thus, they are key actors in reaching the 1.5 °C goal under the Paris Agreement. However, the possible contribution and position of households in climate policies is neither well understood, nor do households receive sufficiently high priority in current climate policy strategies. This paper investigates how behavioral change can achieve a substantial reduction in greenhouse gas emissions in European high-income countries. It uses theoretical thinking and some core results from the HOPE research project, which investigated household preferences for reducing emissions in four European cities in France, Germany, Norway and Sweden. The paper makes five major points: First, car and plane mobility, meat and dairy consumption, as well as heating are the most dominant components of household footprints. Second, household living situations (demographics, size of home) greatly influence the household potential to reduce their footprint, even more than country or city location. Third, household decisions can be sequential and temporally dynamic, shifting through different phases such as childhood, adulthood, and illness. Fourth, short term voluntary efforts will not be sufficient by themselves to reach the drastic reductions needed to achieve the 1.5 °C goal; instead, households need a regulatory framework supporting their behavioral changes. Fifth, there is a mismatch between the roles and responsibilities conveyed by current climate policies and household perceptions of responsibility. We then conclude with further recommendations for research and policy.     

Can forecasting performance be improved by considering the steady state? An application to Swedish inflation and interest rate

This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework—in which a mean‐adjusted form of the models is employed—by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out‐of‐sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processes and structural breaks, in particular when relying on small samples. Copyright © 2008 John Wiley & Sons, Ltd.