To Örebro University

This paper focuses on identifying useful indicators for nowcasting GDP in Sweden. We analyze 35 monthly indicators spanning the period from 1993 to 2023. Additionally, we evaluate the group-wise performance of these indicators. The analysis is conducted using mixed-data sampling (MIDAS) and mixed-frequency VAR models in both individual and pooled setups forn owcasting. While the primary focus is on nowcasting, we also assess the performance of the indicators for backcasting and forecasting. For nowcasting, we identify 16 indicators in the individual setup and 23 indicators in the pooled setup that outperform the benchmark. Group-wise, indicators belonging to the survey, interest & exchange rates, and public finance groups exhibit strong performance in the individual setup. Notably, in the pooled setup, the output, survey, price, interest & exchange rates, and public finance groups demonstrate strong performance.

In this paper, we add new evidence to a long-debated macroeconomic question, namely, whether money growth has predictive power for inflation or put differently, whether money growth Granger causes inflation. We use a historical dataset-consisting of annual Swedish data on money growth and inflation ranging from 1620 to 2021-and employ state-of-the-art Bayesian estimation methods. Specifically, we employ VAR models with drifting parameters and stochastic volatility which are used to conduct analysis both within- and out-of-sample. Our results indicate that the within-sample analysis-based on marginal likelihoods-provides strong evidence in favour of money growth Granger causing inflation. This strong evidence is, however, not reflected in our out-of-sample analysis, as it does not translate into a corresponding improvement in forecast accuracy.

The variety and the number of ways of measuring individual wellbeing has increased over the past two decades. In addition to all self-reported measures, researchers also consider a wide variety of objectively-measured indicators of wellbeing (e.g., blood pressure, pulse rate, and the pattern of activity in different parts of the brain). However, it has not yet been established if the analysis of one only of these measurement concepts suffices, or rather whether more can be learnt from the joint analyses of both subjective and objective adult wellbeing indicators. This chapter briefly reviews this question, focussing on cortisol (as a potential objective measure) and life satisfaction (as a subjective measure), and suggests directions for future research.

We assess the bivariate relation between money growth and inflation in the euro area and the United States using hybrid time-varying parameter Bayesian VAR models. Model selection based on marginal likelihoods suggests that the relation is statistically unstable across time in both regions. The effect of money growth on inflation weakened notably after the 1980s before strengthening after 2020. There is evidence that this time variation is related to the pace of price changes, as we find that the maximum impact of money growth on inflation is increasing in the trend level of inflation. These results caution against asserting a simple, time-invariant relationship when modeling the joint dynamics of monetary aggregates and consumer prices.

It has been claimed that the fall in US inflation during the Great Recession was surprisingly small. One possible explanation for this is that the Phillips curve is unstable and that its slope was lower around the Great Recession. We investigate the importance of time-varying parameters using Bayesian vector autoregressions for inflation and unemployment. We find support for time variation in the inflation equation and an unstable Phillips curve that was somewhat flatter between 2005 and 2013. However, conditional forecasts mostly suggest that inflation was not unexpectedly high around the Great Recession, which puts the claim of a "missing disinflation" into question.

Att de störningar som drabbar makroekonomin är normalfördelade och har konstant varians är två antaganden som allt oftare har övergivits i den inter-nationella forskningslitteraturen under de senaste två decennierna. I denna artikel undersöks om detta är relevant för ett antal nyckelvariabler i svensk mak-roekonomi. Sammantaget tyder våra resultat på att forskare och policyekonomer som modellerar svenska makroekonomiska variabler – t ex i syfte att beskriva riskbilden kring dem – har påtaglig anledning att åtminstone överge antagandet om konstant störningsvarians. Ett konkret problem som annars kan uppstå är att prognososäkerhet överskattas i lugna tider och underskattas i turbulenta tider.

With uncertain changes of the economic environment, macroeconomic downturns during recessions and crises can hardly be explained by a Gaussian structural shock. There is evidence that the distribution of macroeconomic variables is skewed and heavy tailed. In this paper, we contribute to the literature by extending a vector autoregression (VAR) model to account for more realistic assumptions on the multivariate distribution of macroeconomic variables. We propose a general class of generalized hyperbolic skew Student’s  distribution with stochastic volatility for the innovations in the VAR model that allows us to take into account both skewness and heavy tails. Tools for Bayesian inference and model selection using a Gibbs sampler are provided. In an empirical study, we present evidence of skewness and heavy tails for monthly macroeconomic variables. The analysis also gives a clear message that skewness is a value-added feature to VAR models with heavy tails.

We propose a general class of multivariate fat-tailed distributions which includes the normal, t and Laplace distributions as special cases as well as their mixture. Full conditional posterior distributions for the Bayesian VAR-model are derived and used to construct a MCMC-sampler for the joint posterior distribution. The framework allows for selection of a specific special case as the distribution for the error terms in the VAR if the evidence in the data is strong while at the same time allowing for considerable flexibility and more general distributions than offered by any of the special cases. As fat tails can also be a sign of conditional heteroskedasticity we also extend the model to allow for stochastic volatility. The performance is evaluated using simulated data and the utility of the general model specification is demonstrated in applications to macroeconomics and finance.