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  • 1.
    Andersson, Michael K.
    et al.
    Sveriges Riksbank.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro universitet.
    Bayesian forecast combination for VAR models2008Inngår i: Bayesian Econometrics / [ed] Siddhartha Chib, William Griffiths, Gary Koop, Dek Terrell, Bingley: Emerald , 2008, s. 501-524Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 2.
    Andersson, Michael K.
    et al.
    National Institute of Economic Research, Stockholm, Sweden.
    Karlsson, Sune
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Bootstrapping Error Component Models2001Inngår i: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 16, nr 2, s. 221-231Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper proposes several resampling algorithms suitable for error component models and evaluates them in the context of bootstrap testing. In short, all the algorithms work well and lead to tests with correct or close to correct size. There is thus little or no reason not to use the bootstrap with error component models.

  • 3.
    Andrén, Daniela
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Clark, Andrew
    D'Ambrassio, Conchita
    University of Luxembourg, Esch-sur-Alzette, Luxemburg.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    From birth to adulthood: What are the consequences for a woman in her forties?2016Konferansepaper (Fagfellevurdert)
  • 4.
    Andrén, Daniela
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Clark, Andrew
    Paris School of Economics, Paris, France.
    D’Ambrosio, Conchita
    Université du Luxembourg, Luxembourg.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Pettersson, Nicklas
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Nya sätt att mäta välbefinnande? En analys av subjektiva och objektiva mått2019Inngår i: Ekonomisk Debatt, ISSN 0345-2646, Vol. 1, s. 44-51Artikkel i tidsskrift (Fagfellevurdert)
  • 5.
    Andrén, Daniela
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Clark, Andrew
    Paris School of Economics, Paris, France.
    D'Ambrosio, Conchita
    Université du Luxembourg, Esch-sur-Alzette, Luxembourg.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Pettersson, Nicklas
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Subjective and physiological measures of well-being: an exploratory analysis using birth-cohort data2017Rapport (Annet vitenskapelig)
    Abstract [en]

    We use a rich longitudinal data set following a cohort of Swedish women from age 10 to 49 to analyse the effects of birth and early-life conditions on adulthood outcomes. These latter include both well-being and the stress hormone cortisol. Employment and marital status are important adult determinants of well-being. Log family income and absence from school also predict adult well-being, although their importance falls when controlling for adult and birth characteristics. Among the birth characteristics, we find that high birth weight (>4.3kg) affects adult well-being. We predict the level of adult cortisol only poorly, and suggest that the relationship between life satisfaction and cortisol is non-monotonic: both high and low cortisol are negatively correlated with life satisfaction. The results from an OLS life satisfaction regression and a multinomial logit of high or low cortisol (as compared to medium) are more similar to each other.

  • 6.
    Andrén, Daniela
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Clark, Andrew E.
    Paris School of Economics - CNRS, Paris, France.
    D’Ambrosio, Conchita
    Université du Luxembourg, Esch-sur-Alzette, Luxembourg.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Pettersson, Nicklas
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    New ways to measure well-being?: A first joint analysis of subjective and objective measures2018Rapport (Annet vitenskapelig)
    Abstract [en]

    Our study is, to our knowledge, the first joint analysis of subjective and objective measures of well-being. Using a rich longitudinal data from the mothers pregnancy until adulthood for a birth cohort of children who attended school in Örebro during the 1960s, we analyse in a first step how subjective (self-assessed) and objective (cortisol-based) measures of well-being are related to each other. In a second step, life-course models for these two measures are estimated and compared with each other. Despite the fact that our analysis is largely exploratory, our results suggest interesting possibilities to use objective measures to measure well-being, even though this may imply a greater degree of complexity.

  • 7.
    Ding, Shutong
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Bayesian forecasting combination in VAR models with many predictorsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    This paper is motivated by the findings of our previous work, that is forecasting VAR models in the cases of small and medium-sized datasets, both marginalized marginal likelihood and predictive likelihood based averaging approaches tend to produce superior forecasts than the Bayesian VAR methods using shrinkage priors. With an efficient reversible-jump MCMC algorithm, We extend the forecast combination and model averaging of VAR models to the context of large datasets (more than hundred predictors), and consider a range of competitive alternative methods to compare and examine their forecast performance. Our empirical results show that the Bayesian model averaging approach outperforms the various alternatives.

  • 8.
    Ding, Shutong
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Bayesian forecasting using reduced rank VARsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Reduced rank regression has a long tradition as a technique to achieve a parsimonious parameterization in multivariate regression models. Recently this has been applied in the Bayesian VAR framework where the rich parameterization is a common concern in applied work. We advocate a parameterization of the reduced rank VAR which leads to a natural interpretation in terms of a dynamic factor model. Without additional restrictions on the parameters the reduced rank model is unidentified and we consider two identification schemes. The traditional ad-hoc identification with the first rows of one of the reduced rank parameter matrices being the identity matrix and a semi-orthogonal identification originally proposed in the context of cointegrated VAR models with the advantage that it does not depend on the ordering of the variables. Borrowing from the cointegration literature, we propose efficient MCMC algorithms for the evaluation of the posterior distribution given the two identification schemes. The determination of the rank of the reduced rank VAR is an important practical issue and we study the performance of different criteria for determining the rank. Finally, the forecasting performance of the reduced rank VAR model is evaluated in comparison with other popular forecasting models for large data sets.

  • 9.
    Ding, Shutong
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Model averaging and variable selection in VAR modelsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Bayesian model averaging and model selection is based on the marginal likelihoods of the competing models. This can, however, not be used directly in VAR models when one of the issues is which - and how many - variables to include in the model since the likelihoods will be for different groups of variables and not directly comparable. One possible solution is to consider the marginal likelihood for a core subset of variables that are always included in the model. This is similar in spirit to a recent proposal for forecast combination based on the predictive likelihood. The two approaches are contrasted and their performance is evaluated in a simulation study and a forecasting exercise. 

  • 10.
    Edlund, Per-Olov
    et al.
    Stockholm School of Economics, Stockholm, Sweden.
    Karlsson, Sune
    Stockholm School of Economics, Stockholm, Sweden.
    Forecasting the Swedish unemployment rate. VAR vs. transfer function modelling1993Inngår i: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 9, nr 1, s. 61-76Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Swedish unemployment rate is forecast using three time series methods: the ARIMA, transfer function and Vector Autoregressive (VAR) models. Within this context, the choice of modelling strategy is discussed. It is found that the forecasting performance of VAR models is improved by explicitly taking account of cointegration between the variables in the model, despite the fact that unemployment is not cointegrated. However, the more parsimonious ARIMA and transfer function models have lower RMSE for all forecasting horizons. It is also found that the additional variables in the VAR models are important for predicting the turning points in the unemployment rate.

  • 11.
    Eklund, Jana
    et al.
    Bank of England.
    Karlsson, Sune
    Örebro universitet, Institutionen för ekonomi, statistik och informatik.
    Forecast combination and model averaging using predictive measures2007Inngår i: Econometric Reviews, ISSN 0747-4938, E-ISSN 1532-4168, Vol. 26, nr 2-4, s. 329-363Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting when uninformative priors on the model parameters are used and improves forecast performance. For the predictive likelihood we argue that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and in an application to forecasts of the Swedish inflation rate, where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.

  • 12.
    Gredenhoff, Mikael
    et al.
    Dept. of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Karlsson, Sune
    Dept. of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Lag-length selection in VAR-models using equal and unequal lag-length procedures1999Inngår i: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 14, nr 2, s. 171-187Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    It is well known that inference in vector autoregressive models depends crucially on the choice of lag-length. Various lag-length selection procedures have been suggested and evaluated in the literature. In these evaluations the possibility that the true model may have unequal lag-length has, however, received little attention. In this paper we investigate how sensitive lag-length estimation procedures, based on assumptions of equal or unequal lag-lengths, are to the true model structure. The procedures used in the paper are based on information criteria and we give results for AIC, HQ and BIG. In the Monte Carlo study we generate data from a variety of VAR models with properties similar to macro-economic time-series. We find that the commonly used procedure based on equal lag-length together with AIC and HQ performs well in most cases. The procedure (due to Hsiao) allowing for unequal lag-lengths produce reasonable results when the true model has unequal lag-length. The Hsiao procedure tend to do better than equal lag-length procedures in models with a more complicated lag structure.

  • 13.
    Hahn-Strömberg, Victoria
    et al.
    Örebro universitet, Institutionen för hälsovetenskap och medicin. Department of Laboratory Medicine, Section for Pathology, Örebro University Hospital, Örebro, Sweden.
    Askari, Shlear
    Department of Laboratory Medicine, Section for Pathology, Örebro University Hospital, Örebro, Sweden.
    Befekadu, Rahel
    Örebro universitet, Institutionen för hälsovetenskap och medicin. Department of Laboratory Medicine, Section for Pathology, Örebro University Hospital, Örebro, Sweden.
    Matthiessen, Peter
    Region Örebro län. Department of Clinical Surgery, Örebro University Hospital, Örebro, Sweden.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Nilsson, Torbjorn K.
    Department of Medical Biosciences/Clinical Chemistry, Umeå University, Umeå, Sweden.
    Polymorphisms in the CLDN1 and CLDN7 genes are related to differentiation and tumor stage in colon carcinoma2014Inngår i: Acta Pathologica, Microbiologica et Immunologica Scandinavica (APMIS), ISSN 0903-4641, E-ISSN 1600-0463, Vol. 122, nr 7, s. 636-642Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Tight junction is composed of transmembrane proteins important for maintaining cell polarity and regulating ion flow. Among these proteins are the tissue-specific claudins, proteins that have recently been suggested as tumor markers for several different types of cancer. An altered claudin expression has been observed in colon, prostatic, ovarian, and breast carcinoma. The aim of this study was to analyze the allele frequencies of three common single nucleotide polymorphisms (SNPs) in the genes for claudin 1 and claudin 7 in colon cancer (CC) patients and in a control population of healthy blood donors. Pyrosequencing was used to genotype the CLDN1 SNP rs9869263 (c.369C>T), and the CLDN7 SNPs rs4562 (c.590C>T) and rs374400 (c.606T>G) in DNA from 102 formalin fixed paraffin embedded (FFPE) colon cancer tissue, and 111 blood leukocyte DNA from blood/plasma donors. These results were correlated with clinical parameters such as TNM stage, tumor localization, tumor differentiation, complexity index, sex, and age. We found that there was a significant association between the CLDN1 genotype CC in tumor samples and a higher risk of colon cancer development (OR 3.0, p < 0.001). We also found that the CLDN7 rs4562 (c.590C>T) genotype CT had a higher risk of lymph node involvement (p = 0.031) and a lower degree of tumor differentiation (p = 0.028). In the control population, the allele frequencies were very similar to those in the HapMap cohort for CLDN7. The CLDN1 rs9869263 genotype (c.369C>T) was related to increased risk of colon cancer, and the CLDN7 rs4562 genotype (c.590C>T) was related to tumor differentiation and lymph node involvement in colon carcinoma. Further studies are warranted to ascertain their potential uses as biomarkers predicting tumor development, proliferation, and outcome in this disease.

  • 14.
    Hultblad, Brigitta
    et al.
    Statistics Sweden.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro universitet.
    Bayesian simultaneous determination of structural breaks and lag lengths2008Inngår i: Studies in Nonlinear Dynamics and Econometrics, ISSN 1081-1826, E-ISSN 1558-3708, Vol. 12, nr 3, s. Article 4-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The detection of structural change and determination of lag lengths are long-standing issues in time series analysis. This paper demonstrates how these can be successfully married in a Bayesian analysis. By taking account of the inherent uncertainty about the lag length when deciding on the number of structural breaks and vice versa we avoid some common pitfalls and are able to draw more robust conclusions. The approach is illustrated using both real data and a simulation study.

  • 15.
    Jacobson, Tor
    et al.
    Sveriges Riksbank, Stockholm, Sweden.
    Karlsson, Sune
    Stockholm School of Economics, Stockholm, Sweden.
    Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach2004Inngår i: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 23, nr 7, s. 479-496Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We consider a Bayesian model averaging approach for the purpose of forecasting Swedish consumer price index inflation using a large set of potential indicators, comprising some 80 quarterly time series covering a wide spectrum of Swedish economic activity. The paper demonstrates how to efficiently and systematically evaluate (almost) all possible models that these indicators in combination can give rise to. The results, in terms of out-of-sample performance, suggest that Bayesian model averaging is a useful alternative to other forecasting procedures, in particular recognizing the flexibility by which new information can be incorporated.

  • 16.
    Kadiyala, K. Rao
    et al.
    Krannert Graduate School of Management, Purdue University, West Lafayette IN, USA.
    Karlsson, Sune
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Forecasting with Generalized Bayesian Vector Autoregressions1993Inngår i: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 12, nr 3-4, s. 365-378Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The effects of using different distributions to parameterize the prior beliefs in a Bayesian analysis of vector autoregressions are studied. The well-known Minnesota prior of Litterman as well as four less restrictive distributions are considered. Two of these prior distributions are new to vector autoregressive models. When the forecasting performance of the different parameterizations of the prior beliefs are compared it is found that the prior distributions that allow for dependencies between the equations of the VAR give rise to better forecasts.

  • 17.
    Kadiyala, K Rao
    et al.
    Krannert Graduate School of Management, Purdue University, W. Lafayette IN, USA.
    Karlsson, Sune
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Numerical Methods for Estimation and Inference in Bayesian VAR-models1997Inngår i: Journal of applied econometrics (Chichester, England), ISSN 0883-7252, E-ISSN 1099-1255, Vol. 12, nr 2, s. 99-132Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In Bayesian analysis of vector autoregressive models, and especially in forecasting applications, the Minnesota prior of Litterman is frequently used. In many cases other prior distributions provide better forecasts and are preferable from a theoretical standpoint. Several of these priors require numerical methods in order to evaluate the posterior distribution. Different ways of implementing Monte Carlo integration are considered. It is found that Gibbs sampling performs as well as, or better, then importance sampling and that the Gibbs sampling algorithms are less adversely affected by model size. We also report on the forecasting performance of the different prior distributions.

  • 18.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Conditional posteriors for the reduced rank regression modelManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    The multivariate reduced rank regression model plays an important role in econo- metrics. Examples include co-integration analysis and models with a factor struc- ture. Geweke (1996) provided the foundations for a Bayesian analysis of this model. Unfortunately several of the full conditional posterior distributions, which forms the basis for constructing a Gibbs sampler for the poster distribution, given by Geweke contains errors. This paper provides correct full conditional posteriors for the re- duced rank regression model under the prior distributions considered by Geweke.

  • 19.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Corrigendum to “Bayesian reduced rank regression in econometrics” [J. Econometrics 75 (1996) 121–146]2017Inngår i: Journal of Econometrics, ISSN 0304-4076, E-ISSN 1872-6895, Vol. 201, nr 1, s. 170-171Artikkel i tidsskrift (Fagfellevurdert)
  • 20.
    Karlsson, Sune
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Forecasting with Bayesian Vector Autoregression2013Inngår i: Handbook of Economic Forecasting, Volume 2 / [ed] Graham Elliott, Allan Timmermann, Elsevier, 2013, s. 791-897Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    This chapter reviews Bayesian methods for inference and forecasting with VAR models. Bayesian inference and, by extension, forecasting depends on numerical methods for simulating from the posterior distribution of the parameters and special attention is given to the implementation of the simulation algorithm.

  • 21.
    Karlsson, Sune
    et al.
    Örebro universitet, Handelshögskolan vid Örebro universitet.
    Lundin, Nannan
    Sjöholm, Fredrik
    Örebro universitet, Handelshögskolan vid Örebro universitet.
    Ping, He
    Foreign Firms and Chinese Employment2009Inngår i: The World Economy, ISSN 0378-5920, E-ISSN 1467-9701, Vol. 32, nr 1, s. 178-201Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper examines the effect of foreign direct investment (FDI) on employment in the Chinese manufacturing sector. As one of the world's largest recipients of FDI, China has arguably benefited from foreign multinational enterprises in various respects. However, one of the main challenges for China, and other developing countries, is job creation, and the effect of FDI on employment is uncertain. The effect depends on the amount of jobs created within foreign firms as well as the effect of FDI on employment in domestic firms. We analyse FDI and employment in China using a large sample of manufacturing firms for the period 1998–2004. Our results show that FDI has positive effects on employment growth. The relatively high employment growth in foreign firms is associated with their firm characteristics and their high survival rate. Employment growth is also relatively high in private domestic Chinese firms. There also seems to be a positive indirect effect of FDI on employment in private domestically-owned firms, presumably caused by spillovers.

  • 22.
    Karlsson, Sune
    et al.
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Löthgren, Mickael
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Computationally efficient double bootstrap variance estimation2000Inngår i: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 33, nr 3, s. 237-247Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The double bootstrap provides a useful tool for bootstrapping approximately pivotal quantities by using an "inner" bootstrap loop to estimate the variance. When the estimators are computationally intensive, the double bootstrap may become infeasible. We propose the use of a new variance estimator for the nonparametric bootstrap which effectively removes the requirement to perform the inner loop of the double bootstrap. Simulation results indicate that the proposed estimator produce bootstrap-t confidence intervals with coverage accuracy which replicates the coverage accuracy for the standard double bootstrap.

  • 23.
    Karlsson, Sune
    et al.
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    Löthgren, Mickael
    Department of Economic Statistics, Stockholm School of Economics, Stockholm, Sweden.
    On the power and interpretation of panel unit root tests2000Inngår i: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 66, nr 3, s. 249-255Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We demonstrate that panel unit root tests can have high power when a small fraction of the series is stationary and may lack power when a large fraction is stationary. The acceptance or rejection of the null is thus not sufficient evidence to conclude that all series have a unit root or that all are stationary.

  • 24.
    Karlsson, Sune
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Mazur, Stepan
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Flexible Fat-tailed BVARs2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a general class of fat-tailed distributions which includes the t,Cauchy, Laplace and slash distributions as well as the normal distribution as spe-cial cases. Full conditional posterior distributions for the Bayesian VAR-model arederived and used to construct a MCMC-sampler for the joint posterior distribution.The framework allows for selection of a specic special case as the distribution forthe error terms in the VAR if the evidence in the data is strong while at the sametime allowing for considerable exibility and more general distributions than oeredby any of the special cases.

  • 25.
    Karlsson, Sune
    et al.
    Stockholm School of Economics, Department of Economic Statistic, Stockholm.
    Skoglund, Jimmy
    Swedbank Group Financial Risk Control, Stockholm, Sweden.
    Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects2004Inngår i: Empirical Economics, ISSN 0377-7332, E-ISSN 1435-8921, Vol. 29, nr 1, s. 79-88Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The general case where the time specific effect in a two way model follows an arbitrary ARMA process has not been considered previously. We offer a straightforward maximum likelihood estimator for this case. Allowing for general ARMA processes raises the issue of model specification and we propose tests of the null hypothesis of no serial correlation as well as tests for discriminating between different specifications. A Monte-Carlo experiment evaluates the finite-sample properties of the estimators and test-statistics.

  • 26.
    Karlsson, Sune
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Österholm, Pär
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    A Note on the Stability of the Swedish Phillips Curve2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We use Bayesian techniques to estimate bivariate VAR models for Swedish unemployment rate and inflation. Employing quarterly data from 1995Q1 to 2018Q3 and new tools for model selection, we compare models with time-varying parameters and/or stochastic volatility to specifications with constant parameters and/or covariance matrix. The evidence in favour of a stable dynamic relationship between the unemployment rate and inflation is mixed. Model selection based on marginal likelihood calculations indicates that the relation is time varying, whereas the use of the deviance information criterion suggests that it is constant over time; we do, however, note consistent evidence in favour of stochastic volatility. An out-of-sample forecast exercise is also conducted, but similarly provides mixed evidence regarding which model to favour. Importantly though, even if time-varying parameters are allowed for, our results do not suggest that the Phillips curve has been flatter in more recent years. This finding thereby questions the explanation that a flatter Phillips curve is the cause of the low inflation that Sweden has experienced in recent year.

  • 27.
    Karlsson, Sune
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Österholm, Pär
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Is the US Phillips Curve Stable? Evidence from Bayesian VARs2018Rapport (Annet vitenskapelig)
    Abstract [en]

    Inflation did not fall as much as many economists expected as the Great Recession hit the US economy. One explanation suggested for this phenomenon is that the Phillips curve has become flatter. In this paper we investigate the stability of the US Phillips curve, employing Bayesian VARs to quarterly data from 1990Q1 to 2017Q3. We estimate bivariate models for PCE inflation and the unemployment rate under a number of different assumptions concerning the dynamics and covariance matrix. Specifically, we assess the importance of time-varying parameters and stochastic volatility. Using new tools for model selection, we find support for both time-varying parameters and stochastic volatility. Interpreting the Phillips curve as the inflation equation of our Bayesian VAR, we conclude that the US Phillips curve has been unstable. Our results also indicate that the Phillips curve may have been somewhat flatter between 2005 and 2013 than in the decade preceding that period. However, while the dynamic relations of the model appear to be subject to time variation, we note that the effect of a shock to the unemployment rate on inflation is not fundamentally different over time. Finally, a conditional forecasting exercise suggests that as far as the models are concerned, inflation may not have been unexpectedly high around the Great Recession.

  • 28.
    Karlsson, Sune
    et al.
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Österholm, Pär
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Volatilities, drifts and the relation between treasury yields and the corporate bond yield spread in Australia2019Inngår i: Finance Research Letters, ISSN 1544-6123, E-ISSN 1544-6131, Vol. 30, s. 378-384Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We estimate Bayesian VAR models in order to investigate the relation between Treasury yields and the corporate bond yield spread in Australia. Recent developments in Bayesian model selection allow us to formally assess the relevance of stochastic volatility and drifting parameters. A model comparison indicates that a model with stochastic volatility and constant parameters is preferred. Our results imply that while previous studies may have relied on empirically flawed models, their main conclusion – namely that an increase in the risk free rate decreases the corporate bond yield spread – appears to be an empirically robust finding.

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