Vector autoregressions (VARs) are ∞exible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A potential solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious na˜‡ve benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach is theoretically grounded, easy to implement, and greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well both in terms of out-of-sample forecasting, and accuracy in the estimation of impulse response functions.

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Prior selection for vector autoregressions

There is a widespread belief that changes in expectations may be an important independent driver of economic fluctuations. The news view of business cycles offers a formalization of this perspective. In this paper we discuss mechanisms by which changes in agents’ information, due to the arrival of news, can cause business cycle fluctuations driven by expectational change, and we review the empirical evidence aimed at evaluating their relevance. In particular, we highlight how the literature on news and business cycles offers a coherent way of thinking about aggregate fluctuations, while at the same time we emphasize the many challenges that must be addressed before a proper assessment of the role of news in business cycles can be established. ( JEL D83, D84, E13, E32, O33)

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News Driven Business Cycles: Insights and Challenges

We study how different forms of communication influence the inflation expectations of individuals in a randomized controlled trial. We first solicit individuals’ inflation expectations in the Nielsen Homescan panel and then provide eight different forms of information regarding inflation. Reading the actual Federal Open Market Committee (FOMC) statement has about the same average effect on expectations as simply being told about the Federal Reserve’s inflation target. Reading a news article about the most recent FOMC meetings results in a forecast revision which is smaller by half. Our results have implications for how central banks should communicate to the broader public.

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Monetary Policy Communications and their Effects on Household Inflation Expectations

Standard business cycle models have difficulties generating large, state-dependent fiscal multipliers. Employing a model of costly financial intermediation based on Curdia-Woodford, we show that fiscal multipliers can be strongly state dependent: fiscal expansions during recessions may lead to multiplier values exceeding two, while similar expansions during economic booms would produce values falling short of unity. This pattern obtains if the spread (the financial friction) is more sensitive to fiscal policy during recessions than during expansions, a feature that is present in the data. Our results are consistent with recent empirical work documenting the state contingency of multipliers

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Fiscal Multipliers in Recessions

A model for U.S. macroeconomic time series that has been used for forecasting for several years is described in some detail. The model is a multivariate Bayesian autoregression, with allowance for conditional heteroskedasticity, stochastic time-variation in parameters, and non-normality of disturbances. It specifies the prior distribution in ways that improve on previous Bayesian vector autoregression specifications in realism and forecasting performance. The model's record of forecasting in recent years is displayed and discussed.

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A Nine-Variable Probabilistic Macroeconomic Forecasting Model

There have been suggestions for monetary policy to engineer higher inflation expectations to stimulate spending. We examine the relationship between expected inflation and spending attitudes using the microdata from the Michigan Survey of Consumers. The impact of higher inflation expectations on the reported readiness to spend on durables is generally small, outside the zero lower bound, often statistically insignificant, and inside of it typically significantly negative. In our baseline specification, a one percentage point increase in expected inflation during the recent zero lower bound period reduces households’ probability of having a positive attitude towards spending by about 0.5 percentage points. (JEL D12, D84, E21, E31, E52)

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Inflation Expectations and Readiness to Spend: Cross-Sectional Evidence

This paper provides novel evidence on the time varying impact of government spending shocks on output in Germany over the years 1970 to 2013. In a first step, I use an expectations-augmented vector autoregressive model with time varying parameters (TVP-VAR) to show that fiscal multipliers are not stable over time but exhibit a u-shaped pattern. While multipliers fluctuate around 2 at the beginning and end of the sample, they are much smaller in between. In a second step, I discuss which factors determine the magnitude of German multipliers and hence explain the observed variation. It turns out that fiscal policy is more effective when business uncertainty is high but less in periods of financial market stress, while the state of the business cycle is minor important. Moreover, I find that fiscal sustainability is a crucial determinant of the multipliers and that these are about 1 euro higher since the loss of monetary policy autonomy due to the adoption of the euro. And finally, I conclude that policy recommendations based on average multipliers are misleading.

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Time Varying Fiscal Multipliers in Germany

Recent articles suggest that a Bayesian vector autoregression (BVAR) with shrinkage is a good forecast device even when the number of variables is large. In this paper we evaluate different variants of the BVAR with respect to their forecast accuracy for euro area real GDP growth and HICP inflation. We consider BVAR averaging, Bayesian factor augmented VARs (BFAVARs), and large BVARs, which differ in the way information is condensed and shrinkage is implemented. We find that: (a) large BVARs produce accurate point forecasts but show a poor performance when the entire density is considered; (b) BVAR averaging shows the opposite pattern; (c) BFAVARs perform well under both evaluation criteria; (d) choosing the degree of shrinkage optimally does not improve forecast accuracy; (e) all variants except the large BVAR tend to be well calibrated for inflation but poorly calibrated for real GDP growth; (f) these findings are robust to several features of the forecast experiment.

Point and Density Forecasts for the Euro Area Using Bayesian VARs

Point and density forecasts for the euro area using Bayesian VARs

This paper discusses how the forecast accuracy of a Bayesian vector autoregression (BVAR) is affected by introducing the zero lower bound on the federal funds rate. As a benchmark I adopt a common BVAR specification, including 18 variables, estimated shrinkage, and no nonlinearity. Then I entertain alternative specifications of the zero lower bound. I account for the possibility that the effect of monetary policy on the economy is different in this regime, replace the federal funds rate by its shadow rate, consider a logarithmic transformation, feed in monetary policy shocks, or utilize conditional forecasts allowing for all shocks implemented through a rejection sampler. The latter two are also coupled with interest rate expectations from future contracts. It is shown that the predictive densities of all these specifications are greatly different, suggesting that this modeling choice is not innocuous. The comparison is based on the accuracy of point and density forecasts of major US macroeconomic series during the period 2009:1 to 2014:4. The introduction of the zero lower bound is not beneficial per se, but it depends on how it is done and which series is forecasted. With caution, I recommend the shadow rate specification and the rejection sampler combined with interest rate expectations to deal with the nonlinearity in the policy rate. Since the policy rate will remain low for some time, these findings could prove useful for practical forecasters.

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Tim Oliver Berg

Papers

Inflation Expectations and Readiness to Spend: Cross-Sectional Evidence

Time Varying Fiscal Multipliers in Germany

Point and Density Forecasts for the Euro Area Using Bayesian VARs

Point and density forecasts for the euro area using Bayesian VARs

Forecast accuracy of a BVAR under alternative specifications of the zero lower bound