Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

This article extends the current literature which questions the stability of the monetary transmission mechanism, by proposing a factor‐augmented vector autoregressive (VAR) model with time‐varying coefficients and stochastic volatility. The VAR coefficients and error covariances may change gradually in every period or be subject to abrupt breaks. The model is applied to 143 post‐World War II quarterly variables fully describing the US economy. I show that both endogenous and exogenous shocks to the US economy resulted in the high inflation volatility during the 1970s and early 1980s. The time‐varying factor augmented VAR produces impulse responses of inflation which significantly reduce the price puzzle. Impulse responses of other indicators of the economy show that the most notable changes in the transmission of unanticipated monetary policy shocks occurred for gross domestic product, investment, exchange rates and money.     

Macroeconomic forecasting and structural change

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting with High‐Dimensional Panel VARs

This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time‐varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients and the error covariance matrix, and propose a Bayesian dynamic learning procedure that controls for various sources of model uncertainty. We tackle computational concerns by means of a simulation‐free algorithm that relies on analytical approximations to the posterior. We use our methods to forecast inflation rates in the eurozone and show that these forecasts are superior to alternative methods for large vector autoregressions.     

Macroeconomic Forecasting Performance under Alternative Specifications of Time‐Varying Volatility

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.     

Composite likelihood methods for large Bayesian VARs with stochastic volatility

Adding multivariate stochastic volatility of a flexible form to large vector autoregressions (VARs) involving over 100 variables has proved challenging owing to computational considerations and overparametrization concerns. The existing literature works with either homoskedastic models or smaller models with restrictive forms for the stochastic volatility. In this paper, we develop composite likelihood methods for large VARs with multivariate stochastic volatility. These involve estimating large numbers of parsimonious models and then taking a weighted average across these models. We discuss various schemes for choosing the weights. In our empirical work involving VARs of up to 196 variables, we show that composite likelihood methods forecast much better than the most popular large VAR approach, which is computationally practical in very high dimensions: the homoskedastic VAR with Minnesota prior. We also compare our methods to various popular approaches that allow for stochastic volatility using medium and small VARs involving up to 20 variables. We find our methods to forecast appreciably better than these as well.     

Optimal Portfolio Choice Under Decision‐Based Model Combinations

We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility‐based objective function. We apply this model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods, both within the class of linear and time‐varying coefficients, stochastic volatility models. Overall, we find that our combination scheme produces markedly more accurate predictions than the existing alternatives, both in terms of statistical and economic measures of out‐of‐sample predictability. Copyright © 2016 John Wiley & Sons, Ltd.     

Labor Market Dynamics: A Time‐Varying Analysis

This paper studies how key labour market stylized facts and the responses of labour market variables to technology shocks vary over the US postwar period. It uses a benchmark dynamic, stochastic, general equilibrium model enriched with labour market frictions and investment‐specific technological progress that enables a novel identification scheme based on sign restrictions on a SVAR with time‐varying coefficients and stochastic volatility. Key findings are: (i) the volatility in job finding and separation rates has declined over time, while their correlation varies across time; (ii) the job finding rate plays an important role for unemployment, and the two series are strongly negatively correlated over the sample period; (iii) the magnitude of the response of labour market variables to technology shocks varies across the sample period.     

Volatility of Price Indices for Heterogeneous Goods with Applications to the Fine Art Market

Price indices for heterogeneous goods such as real estate or fine art constitute crucial information for institutional or private investors considering alternative investment decisions in times of financial markets turmoil. Classical mean‐variance analysis of alternative investments has been hampered by the lack of a systematic treatment of volatility in these markets. In this paper we propose a hedonic regression framework which explicitly defines an underlying stochastic process for the price index, allowing to treat the volatility parameter as the object of interest. The model can be estimated using maximum likelihood in combination with the Kalman filter. We derive theoretical properties of the volatility estimator and show that it outperforms the standard estimator. We show that extensions to allow for time‐varying volatility are straightforward using a local‐likelihood approach. In an application to a large data set of international blue chip artists, we show that volatility of the art market, although generally lower than that of financial markets, has risen after the financial crisis of 2008–09, but sharply decreased during the recent debt crisis. Copyright © 2013 John Wiley & Sons, Ltd.     

我国财政支出政策冲击效应的动态变化——基于包含随机波动的时变参数模型的考察

本文采用能够同时捕捉区制转换性变化和累积性变化的包含随机波动的时变参数结构向量自回归模型实证考察了1995~2009年间我国政府支出冲击效应的动态变化。结果表明虽然财政支出冲击的传导机制出现了局部的趋势性变化,但是对冲击效应的影响并不显著;冲击效应的大小主要取决于冲击本身的波动性。冲击的波动越大,冲击效应水平越高。这也使得冲击效应的动态变化在样本期间上表现出明显的区制转换性特征。     

Commodity prices and fiscal policy design: Procyclical despite a rule

Recent studies offer evidence of reduced fiscal procyclicality to commodity price changes in resource‐rich countries—a feature commonly attributed to the adoption of fiscal policy rules. We revisit this issue and find that, by controlling for global activity shocks while allowing for time‐varying changes in both fiscal policy and the volatility of shocks, this finding does not hold. To show this we develop a time‐varying dynamic factor model, allowing for a multiple of shocks, stochastic volatility and time‐varying parameters, and estimate it on data for Norway, whose handling of resource wealth is often cited as exemplary.     

Structural break threshold VARs for predicting US recessions using the spread

This paper proposes a model to predict recessions that accounts for non‐linearity and a structural break when the spread between long‐ and short‐term interest rates is the leading indicator. Estimation and model selection procedures allow us to estimate and identify time‐varying non‐linearity in a VAR. The structural break threshold VAR (SBTVAR) predicts better the timing of recessions than models with constant threshold or with only a break. Using real‐time data, the SBTVAR with spread as leading indicator is able to anticipate correctly the timing of the 2001 recession. Copyright © 2006 John Wiley & Sons, Ltd.     

Choosing Between Different Time‐Varying Volatility Models for Structural Vector Autoregressive Analysis

The performance of information criteria and tests for residual heteroscedasticity for choosing between different models for time‐varying volatility in the context of structural vector autoregressive analysis is investigated. Although it can be difficult to find the true volatility model with the selection criteria, using them is recommended because they can reduce the mean squared error of impulse response estimates substantially relative to a model that is chosen arbitrarily based on the personal preferences of a researcher. Heteroscedasticity tests are found to be useful tools for deciding whether time‐varying volatility is present but do not discriminate well between different types of volatility changes. The selection methods are illustrated by specifying a model for the global market for crude oil.     

An identification‐robust test for time‐varying parameters in the dynamics of energy prices

We test for the presence of time‐varying parameters (TVP) in the long‐run dynamics of energy prices for oil, natural gas and coal, within a standard class of mean‐reverting models. We also propose residual‐based diagnostic tests and examine out‐of‐sample forecasts. In‐sample LR tests support the TVP model for coal and gas but not for oil, though companion diagnostics suggest that the model is too restrictive to conclusively fit the data. Out‐of‐sample analysis suggests a random‐walk specification for oil price, and TVP models for both real‐time forecasting in the case of gas and long‐run forecasting in the case of coal. Copyright © 2010 John Wiley & Sons, Ltd.     

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general vector autoregressive moving averages (VARMAs). A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with time‐varying vector moving average (VMA) coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.     

An empirical application of stochastic volatility models

This paper studies the empirical performance of stochastic volatility models for twenty years of weekly exchange rate data for four major currencies. We concentrate on the effects of the distribution of the exchange rate innovations for both parameter estimates and for estimates of the latent volatility series. The density of the log of squared exchange rate innovations is modelled as a flexible mixture of normals. We use three different estimation techniques: quasi-maximum likelihood, simulated EM, and a Bayesian procedure. The estimated models are applied for pricing currency options. The major findings of the paper are that: (1) explicitly incorporating fat-tailed innovations increases the estimates of the persistence of volatility dynamics; (2) the estimation error of the volatility time series is very large; (3) this in turn causes standard errors on calculated option prices to be so large that these prices are rarely significantly different from a model with constant volatility. © 1998 John Wiley & Sons, Ltd.     

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

This paper undertakes both a narrow and wide replication of the constant coefficients vector autoregression (VAR) identified with sign restrictions considered by Peersman (Journal of Applied Econometrics 2005; 20 (2): 185–207. His results for the US are robust to an increase in the sample period from 2002:Q2 to 2014:Q2, but the extension to time‐varying parameters highlights the importance of testing the robustness of results against time variation. In particular, there are differences across models regarding the role of individual shocks during the 2001 US slowdown. Copyright © 2015 John Wiley & Sons, Ltd.     

POSTERIOR‐PREDICTIVE EVIDENCE ON US INFLATION USING EXTENDED NEW KEYNESIAN PHILLIPS CURVE MODELS WITH NON‐FILTERED DATA

Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended New Keynesian Phillips curve (NKPC) models. It is shown that mechanical removal or modeling of simple low‐frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic NKPC models are extended to include structural time series models that describe typical time‐varying patterns in levels and volatilities. Forward‐ and backward‐looking expectation components for inflation are incorporated and their relative importance is evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation‐based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long‐run stability between inflation and marginal costs. Backward‐looking inflation appears stronger than forward‐looking inflation. Levels and volatilities of inflation are estimated more precisely using rich NKPC models. The extended NKPC structures compare favorably with existing basic Bayesian vector autoregressive and stochastic volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of deflation in recent years. Copyright © 2014 John Wiley & Sons, Ltd.     

Combining Time Variation and Mixed Frequencies: an Analysis of Government Spending Multipliers in Italy

In this paper, we propose a time‐varying parameter vector autoregression (VAR) model with stochastic volatility which allows for estimation on data sampled at different frequencies. Our contribution is twofold. First, we extend the methodology developed by Cogley and Sargent (Drifts and volatilities: monetary policies and outcomes in the post WWII U.S. Review of Economic Studies 2005; 8 : 262–302) and Primiceri (Time varying structural vector autoregressions and monetary policy. Review of Economic Studies 2005; 72 : 821–852) to a mixed‐frequency setting. In particular, our approach allows for the inclusion of two different categories of variables (high‐frequency and low‐frequency) into the same time‐varying model. Second, we use this model to study the macroeconomic effects of government spending shocks in Italy over the 1988:Q4–2013:Q3 period. Italy—as well as most other euro area economies—is characterized by short quarterly time series for fiscal variables, whereas annual data are generally available for a longer sample before 1999. Our results show that the proposed time‐varying mixed‐frequency model improves on the performance of a simple linear interpolation model in generating the true path of the missing observations. Second, our empirical analysis suggests that government spending shocks tend to have positive effects on output in Italy. The fiscal multiplier, which is maximized at the 1‐year horizon, follows a U‐shape over the sample considered: it peaks at around 1.5 at the beginning of the sample; it then stabilizes between 0.8 and 0.9 from the mid 1990s to the late 2000s, before rising again to above unity during the recent crisis. Copyright © 2015 John Wiley & Sons, Ltd.     

Inflation Volatility with Regime Switching

This paper presents a new approach to model U.S. inflation dynamics by allowing regime switching in an unobserved components stochastic volatility framework. We use a modified particle filter to construct likelihood and estimate the model using MLE. The number of regimes is determined based on a bootstrap. We find that a model with three regimes and regime‐dependent constant volatilities has superior performance. In addition, we show that since 2000:II, U.S. inflation has entered a regime with moderate volatility where most of the volatility comes from transitory shocks.     

The time varying effect of oil price shocks on euro-area exports

In this paper we provide novel evidence on changes in the relationship between the real price of oil and real exports in the euro area. By combining robust predictions on the sign of the impulse responses obtained from a theoretical model with restrictions on the slope of the oil demand and oil supply curves, we identify oil supply and foreign productivity shocks in a time varying VAR with stochastic volatility. We find that from the 1980s onwards the relationship between oil prices and euro area exports has become less negative conditional on oil supply shortfalls and more positive conditional on foreign productivity shocks. Using the theoretical model we show that our empirical findings can be accounted for by (i) stronger trade relationship between the euro area and emerging economies (ii) a decrease in the share of oil in production and (iii) increased competitive pressures in the product market.