why has u s inflation become harder to forecast we examine whether the u s rate of price inflation has become harder to forecast and to the extent that it has what changes in the inflation process have made it so the main finding is that the univariate inflation process is well described by an unobserved component trend cycle model with stochastic volatility or equivalently an integrated moving average process with time varying parameters this model explains a variety of recent univariate inflation forecasting puzzles and begins to explain some multivariate inflation forecasting puzzles as well

real time density forecasts from bayesian vector autoregressions with stochastic volatility central banks and other forecasters are increasingly interested in various aspects of density forecasts however recent sharp changes in macroeconomic volatility including the great moderation and the more recent sharp rise in volatility associated with increased variation in energy prices and the deep global recession pose significant challenges to density forecasting accordingly this paper examines with real time data density forecasts of u s gdp growth unemployment inflation and the federal funds rate from bayesian vector autoregression bvar models with stochastic volatility the results indicate that adding stochastic volatility to bvars materially improves the real time accuracy of density forecasts this article has supplementary material online

trend fundamentals and exchange rate dynamics we estimate a multivariate unobserved components stochastic volatility model to explain the dynamics of a panel of six exchange rates against the us dollar the empirical model is based on the assumption that both countries monetary policy strategies may be well described by taylor rules with a time varying inflation target a time varying natural rate of unemployment and interest rate smoothing the estimates closely track major movements along with important time series properties of real and nominal exchange rates across all currencies considered the model generally outperforms a benchmark model that does not account for changes in trend inflation and trend unemployment authors abstract

estimation and forecasting in models with multiple breaks this paper develops a new approach to change point modelling that allows the number of change points in the observed sample to be unknown the model we develop assumes that regime durations have a poisson distribution it approximately nests the two most common approaches the time varying parameter tvp model with a change point every period and the change point model with a small number of regimes we focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters that characterize each regime a markov chain monte carlo posterior sampler is constructed to estimate a version of our model which allows for change in conditional means and variances we show how real time forecasting can be done in an efficient manner using sequential importance sampling our techniques are found to work well in an empirical exercise involving u s gdp growth and inflation empirical results suggest that the number of change points is larger than previously estimated in these series and the implied model is similar to a tvp with stochastic volatility model

macroeconomic forecasting and structural change the aim of this paper is to assess whether explicitly modeling structural change increases the accuracy of macroeconomic forecasts we produce real time out of sample forecasts for inflation the unemployment rate and the interest rate using a time varying coefficients var with stochastic volatility tv var for the us the model generates accurate predictions for the three variables in particular for inflation the tv var outperforms in terms of mean square forecast error all the competing models fixed coefficients vars time varying ars and the na ove random walk model these results are also shown to hold over the period commonly referred to as the great moderation

the semiparametric asymmetric stochastic volatility model with time varying parameters the case of us inflation we propose a semiparametric extension of the time varying parameter regression model with asymmetric stochastic volatility for parameter estimation we use bayesian methods we illustrate our methods with an application to us inflation

real time inflation forecasting in a changing world this article revisits the accuracy of inflation forecasting using activity and expectations variables we apply bayesian model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation a host of real activity data term structure data relative price data and surveys in this model average we can entertain different channels of structural instability by either incorporating stochastic breaks in the regression parameters of each individual specification within this average or allowing for breaks in the error variance of the overall model average or both thus our framework simultaneously addresses structural change and model uncertainty that would unavoidably affect any inflation forecast model the different versions of our framework are used to model u s personal consumption expenditures pce deflator and gross domestic product gdp deflator inflation rates for the 1960 2011 period a real time inflation forecast

time series copulas for heteroskedastic data we propose parametric copulas that capture serial dependence in stationary heteroskedastic time series we suggest copulas for first order markov series and then extend them to higher orders and multivariate series we derive the copula of a volatility proxy based on which we propose new measures of volatility dependence including co movement and spillover in multivariate series in general these depend upon the marginal distributions of the series using exchange rate returns we show that the resulting copula models can capture their marginal distributions more accurately than univariate and multivariate generalized autoregressive conditional heteroskedasticity models and produce more accurate value at risk forecasts

macroeconomic factors strike back a bayesian change point model of time varying risk exposures and premia in the u s cross section this article proposes a bayesian estimation framework for a typical multi factor model with time varying risk exposures to macroeconomic risk factors and corresponding premia to price u s publicly traded assets the model assumes that risk exposures and idiosyncratic volatility follow a break point latent process allowing for changes at any point on time but not restricting them to change at all points the empirical application to 40 years of u s data and 23 portfolios shows that the approach yields sensible results compared to previous two step methods based on naive recursive estimation schemes as well as a set of alternative model restrictions a variance decomposition test shows that although most of the predictable variation comes from the market risk premium a number of additional macroeconomic risks including real output and inflation shocks are significantly priced in the cross section a bayes factor analysis massively favors the proposed change point model supplementary materials for th

dynamic revenue estimation this paper considers the question of adopting dynamic revenue estimation methods which would incorporate the predicted macroeconomic effects of policies it describes the current revenue estimation process and how these estimates are used in the formation of policy it then evaluates this process as an exercise in forecasting considering not only its methodology but also its performance this evaluation illustrates some of the difficulties that would accompany attempts to use dynamic methods finally the paper relates the choice among different revenue estimation procedures to the political process and the use to which the revenue estimates are put

real time macroeconomic forecasting with a heteroskedastic inversion copula there is a growing interest in allowing for asymmetry in the density forecasts of macroeconomic variables in multivariate time series this can be achieved with a copula model where both serial a

understanding the us natural gas market a markov switching var approach abstract over the past three decades the us natural gas market has witnessed significant changes utilizing a standard bayesian model comparison method this paper formally determines four regimes existing in the market it then employs a markov switching vector autoregressive model to investigate the regime dependent responses of the market to its fundamental shocks the results reveal that the us natural gas market tends to be much more sensitive to shocks occurring in regimes existing after the decontrol act 1989 than the other regimes the paper also finds that shocks to the natural gas demand and price have negligible effects on natural gas production while the price of natural gas is mainly driven by specific demand shocks augmenting the model by incorporating the price of crude oil the results show that the impacts of oil price shocks on natural gas prices are relatively small and regime dependent

semiparametric bayesian inference for time varying parameter regression models with stochastic volatility we develop a bayesian semiparametric method to estimate a time varying parameter regression model with stochastic volatility where both the error distributions of the observations and parameter driven dynamics are unspecified we illustrate our methodology with an application to inflation

macroeconomic forecasting performance under alternative specifications of time varying volatility summary this paper compares alternative models of time varying volatility on the basis of the accuracy of real time point and density forecasts of key macroeconomic time series for the usa we consider bayesian autoregressive and vector autoregressive models that incorporate some form of time varying volatility precisely random walk stochastic volatility stochastic volatility following a stationary ar process stochastic volatility coupled with fat tails garch and mixture of innovation models the results show that the ar and var specifications with conventional stochastic volatility dominate other volatility specifications in terms of point forecasting to some degree and density forecasting to a greater degree copyright 2014 john wiley sons ltd

combining forecast densities from vars with uncertain instabilities recursive weight forecast combination is often found to an ineffective method of improving point forecast accuracy in the presence of uncertain instabilities we examine the effectiveness of this strategy for forecast densities using many vector autoregressive var and autoregressive ar models of output growth inflation and interest rates our proposed recursive weight density combination strategy based on the recursive logarithmic score of the forecast densities produces well calibrated predictive densities for us real time data by giving substantial weight to models that allow for structural breaks in contrast equal weight combinations produce poorly calibrated forecast densities for great moderation data copyright 2010 john wiley sons ltd

how should tax progressivity respond to rising income inequality we address this question in a heterogeneous agent incomplete markets model featuring exogenous idiosyncratic risk endogenous skill investment and flexible labor supply the tax and transfer schedule is restricted to be log linear in income a good description of the us system rising inequality is modeled as a combination of skill biased technical change and growth in residual wage dispersion when facing shifts in the income distribution like those observed in the us a utilitarian planner chooses higher progressivity in response to larger residual inequality but lower progressivity in response to widening skill price dispersion reflecting technical change overall optimal progressivity is approximately unchanged between 1980 and 2016 we document that the progressivity of the actual us tax and transfer system has similarly changed little since 1980 in line with the model prescription

federal reserve information and the behavior of interest rates this paper tests for the existence of asymmetric information between the federal reserve and the public by examining federal reserve and commercial inflation forecasts it demonstrates that the federal reserve has considerable information about inflation beyond what is known to commercial forecasters it also shows that monetary policy actions provide signals of the federal reserve s information and that commercial forecasters modify their forecasts in response to those signals these findings may explain why long term interest rates typically rise in response to shifts to tighter monetary policy jel e52 e43 d82 asymmetric information between the federal reserve and the public is a phenomenon that is often posited but rarely tested numerous models of central bank behavior for example show that the existence of asymmetric information has important implications for the effectiveness of policy and the consequences of dynamic inconsistency yet there

the time varying volatility of macroeconomic fluctuations we investigate the sources of the important shifts in the volatility of us mac roeconomic variables in the postwar period to this end we propose the esti mation of dsge models allowing for time variation in the volatility of the structural innovations we apply our estimation strategy to a large scale model of the business cycle and find that shocks specific to the equilibrium condition of investment account for most of the sharp decline in volatility of the last two decades jel c51 e32

the great moderation in the united kingdom we use a bayesian time varying parameters structural var with stochastic volatility for gdp deflator inflation real gdp growth a 3 month nominal rate and the rate of growth of m4 to investigate the underlying causes of the great moderation in the united kingdom our evidence points toward a dominant role played by good luck in fostering the more stable macroeconomic environment of the last two decades results from counterfactual simulations in particular show that i bringing the monetary policy committee back in time would only have had a limited impact on the great inflation episode at the cost of lower output growth ii imposing the 1970s monetary rule over the entire sample period would have made almost no difference in terms of inflation and output growth outcomes and iii the great inflation was due to a dominant extent to large demand non policy shocks and to a lesser extent especially in 1973 and 1979 to supply shocks

common drifting volatility in large bayesian vars the general pattern of estimated volatilities of macroeconomic and financial variables is often broadly similar we propose two models in which conditional volatilities feature comovement and study them using u s macroeconomic data the first model specifies the conditional volatilities as driven by a single common unobserved factor plus an idiosyncratic component we label this model bvar with general factor stochastic volatility bvar gfsv and we show that the loss in terms of marginal likelihood from assuming a common factor for volatility is moderate the second model which we label bvar with common stochastic volatility bvar csv is a special case of the bvar gfsv in which the idiosyncratic component is eliminated and the loadings to the factor are set to 1 for all the conditional volatilities such restrictions permit a convenient kronecker structure for the posterior variance of the var coefficients which in turn permits estimating the model even with large datasets while perhaps misspecifi

regime switches agents beliefs and post world war ii u s macroeconomic dynamics the evolution of inflation and output over the last 50 years is examined through the lens of a micro founded model that allows for changes in the behavior of the federal reserve and in the volatility of structural shocks agents are aware of the possibility of regime changes and their beliefs have an impact on the law of motion underlying the macroeconomy the results support the view that there were regime switches in the conduct of monetary policy however the behavior of the federal reserve is identified by repeated fluctuations between a hawk and a dove regime instead of by the traditional pre and post volcker structure counterfactual simulations show that if agents had anticipated the appointment of an extremely conservative chairman inflation would not have reached the peaks of the late 70s and the inflation output trade off would have been less severe these beliefs counterfactuals are new in the literature finally the paper provides a bayesian algorithm to handle the technical difficulties that arise in a rational expectations model with markov switching regimes

european central bank footprints on inflation forecast uncertainty the finding of the paper shows the relative effectiveness of the one size fits all policy of the european central bank the paper provides strong evidence in favor of this by testing whether the monetary policy effects footprints found in inflation uncertainty converge to a common level these footprints are measured as the fraction of the estimated policy induced reduction in this uncertainty the testing was conducted by applying a bootstrap type test in a regression of the rate of growth of these fractions on their initial values computed for 16 euro area countries jel c33 e52 e58

full bayesian inference for garch and egarch models a full bayesian analysis of garch and egarch models is proposed consisting of parameter estimation model selection and volatility prediction the bayesian paradigm is implemented via markov chain monte carlo methodologies we provide implementation details and illustrations using the general index of the athens stock exchange

forecasting the distribution of economic variables in a data rich environment this article investigates the relevance of considering a large number of macroeconomic indicators to forecast the complete distribution of a variable the baseline time series model is a semiparametric specification based on the quantile autoregressive qar model that assumes that the quantiles depend on the lagged values of the variable we then augment the time series model with macroeconomic information from a large dataset by including principal components or a subset of variables selected by lasso we forecast the distribution of the h month growth rate for four economic variables from 1975 to 2011 and evaluate the forecast accuracy relative to a stochastic volatility model using the quantile score the results for the output and employment measures indicate that the multivariate models outperform the time series forecasts in particular at long horizons and in tails of the distribution while for the inflation variables the improved performance occurs mostly at the 6 month horizon we also illustra

a bayesian markov switching correlation model for contagion analysis on exchange rate markets this article develops a new markov switching vector autoregressive var model with stochastic correlation for contagion analysis on financial markets the correlation and the log volatility dynamics are driven by two independent markov chains thus allowing for different effects such as volatility spill overs and correlation shifts with various degrees of intensity we outline a suitable bayesian inference procedure based on markov chain monte carlo algorithms we then apply the model to some major and asian pacific cross rates against the u s dollar and find strong evidence supporting the existence of contagion effects and correlation drops during crises closely in line with the stylized facts outlined in the contagion literature a comparison of this model with its closest competitors such as a time varying parameter var reveals that our model has a better predictive ability supplementary materials for this article are available online

modeling time varying uncertainty of multiple horizon forecast errors we estimate uncertainty measures for point forecasts obtained from survey data pooling information embedded in observed forecast errors for different forecast horizons to track time varying uncer

time variation in macro financial linkages summary we analyze the contribution of credit spread house and stock price shocks to the us economy based on a time varying parameter vector autoregressive model we find that the contribution of financial shocks to gross domestic product growth fluctuates from about 20 in normal times to more than 50 during the great recession the great recession and the subsequent weak recovery can largely be traced back to negative housing shocks housing shocks have become more important for the real economy since the early 2000s and negative housing shocks are more important than positive ones unexpected increases in credit spreads have not been deflationary recently copyright 2016 john wiley sons ltd

were there regime switches in u s monetary policy working paper 2004 14 june 2004 abstract a multivariate model identifying monetary policy and allowing for simultaneity and regime switching in coefficients and variances is confronted with u s data since 1959 the best fit is with a model that allows time variation in structural disturbance variances only among models that also allow for changes in equation coefficients the best fit is for a model that allows coefficients to change only in the monetary policy rule that model allows switching among three main regimes and one rarely and briefly occurring regime the three main regimes correspond roughly to periods when most observers believe that monetary policy actually differed and the differences in policy behavior are substantively interesting though statistically ill determined the estimates imply monetary targeting was central in the early 80s but was also important sporadically in the 70s the changes in regime were essential neither to the rise in inflation in the 70s nor to its decline in the 80s jel classification e52 e47 c53 key words counterfactuals lucas critique policy rule monetary targeting simultaneity volatility model comparison i the debate over monetary policy change in an influential paper clarida gali and gertler 2000 cgg presented evidence that us monetary policy changed between the 1970 s and the 1980 s indeed that in the 70 s it was drastically worse they found that the policy rule apparently followed in the 70 s was one that when embedded in most stochastic general equilibrium models would imply non uniqueness of the equilibrium and hence vulnerability of the economy to sunspot fluctuations of arbitrarily large size their estimated policy rule for the later period on the other hand eliminated this indeterminacy these results are a possible explanation of the volatile and rising inflation of the 70 s and of its subsequent decline the cgg analysis has two important weaknesses one is that it fails to account for stochastic volatility us macroeconomic variables and particularly the federal funds rate have gone through periods of tranquility and of agitation with forecast error variances varying greatly from period to period ignoring such variation does not lead to inconsistent estimates of model parameters when the forecasting equations themselves are constant but it strongly biases toward a finding of changed parameters tests of the stability of the forecasting equations the other weakness is that the cgg analysis rests on powerful and implausible identifying assumptions they require that we accept that the response of the monetary authority to expected future inflation and output does not depend on the recent history of inflation money growth or output it is hard to understand why this should be so especially in the 70 s when monetarism was a prominent theme in policy debates congress was requiring reports from the fed of projected time paths of monetary aggregates and financial markets were reacting sensitively to weekly money supply numbers the requirement for existence and uniqueness of equilibrium in dynamic models is that the monetary policy rule show a more than unit response of interest rates to the sum of the logs of all nominal variables that appear on the right hand side of the reaction function if we force a particular measure of expected future inflation to proxy for all the nominal variables that actually appear independently in the reaction function we are bound to get distorted conclusions on the one hand because expected future inflation will be a noisy measure of the full set of nominal influences on policy we might get downward bias in our estimates from the usual errorsin variables effect on the other hand to the extent that expected future inflation like most expected future values shows less variation than current nominal variables we could find a mistaken scaling up of coefficients

forecasting the real prices of crude oil using forecast combinations over time varying parameter models abstract in this paper we forecast real prices of crude oil using real time forecast combinations over time varying parameter tvp models with single predictor we reveal the significant predictability at all horizons up to 24 months the mean squared predictive error reduction over the benchmark of no change forecast is as high as 17 and the directional accuracy as high as 0 645 a combination with tvp models is found to generate more accurate forecasts than the same combination with constant coefficient models because the forecast errors of individual tvp models are correlated at a lower degree we also evaluate the forecasting performance in the framework of density forecasting our results indicate that the benchmark model can be significantly outperformed by forecast combination at the horizons longer than 3 months

the stochastic volatility in mean model with time varying parameters an application to inflation modeling this article generalizes the popular stochastic volatility in mean model to allow for time varying parameters in the conditional mean the estimation of this extension is nontrival since the volatility appears in both the conditional mean and the conditional variance and its coefficient in the former is time varying we develop an efficient markov chain monte carlo algorithm based on band and sparse matrix algorithms instead of the kalman filter to estimate this more general variant the methodology is illustrated with an application that involves u s u k and germany inflation the estimation results show substantial time variation in the coefficient associated with the volatility highlighting the empirical relevance of the proposed extension moreover in a pseudo out of sample forecasting exercise the proposed variant also forecasts better than various standard benchmarks

have we underestimated the likelihood and severity of zero lower bound events prior to the financial crisis most economists probably did not view the zero lower bound zlb as a major problem for central banks using a range of structural and statistical models we find that previous research understated the zlb threat by ignoring uncertainty about model parameters and latent variables focusing too much on the great moderation experience and relying on structural models whose dynamics cannot generate sustained zlb episodes our analysis also suggests that the federal reserve s asset purchases while materially improving macroeconomic conditions did not prevent the zlb constraint from having first order adverse effects on real activity and inflation