Policy Risk and the Business Cycle

The argument that policy risk, i.e. uncertainty about monetary and fiscal policy, has been holding back the economic recovery in the U.S. during the Great Recession has a large popular appeal. We analyze the role of policy risk in explaining business cycle fluctuations by using an estimated New Keynesian model featuring policy risk as well as uncertainty about technology. We directly measure uncertainty from aggregate time series using Sequential Monte Carlo Methods. While we find considerable evidence of policy risk in the data, we show that the "pure uncertainty"-effect of policy risk is unlikely to play a major role in business cycle fluctuations. With the estimated model, output effects are relatively small due to i) dampening general equilibrium effects that imply a low amplification and ii) counteracting partial effects of uncertainty. Finally, we show that policy risk has effects that are an order of magnitude larger than the ones of uncertainty about aggregate TFP.Policy Risk; Uncertainty; Aggregate Fluctuations; Particle Filter; General Equilibrium.

Central bank communication on financial stability

Central banks regularly communicate about financial stability issues, by publishing Financial Stability Reports (FSRs) and through speeches and interviews. The paper asks how such communications affect financial markets. Building a unique dataset, it provides an empirical assessment of the reactions of stock markets to more than 1000 releases of FSRs and speeches by 37 central banks over the past 14 years. The findings suggest that FSRs have a significant and potentially long-lasting effect on stock market returns, and also tend to reduce market volatility. Speeches and interviews, in contrast, have little effect on market returns and do not generate a volatility reduction during tranquil times, but have had a substantial effect during the 2007-10 financial crisis. The findings suggest that financial stability communication by central banks are perceived by markets to contain relevant information, and they underline the importance of differentiating between communication tools, their content and the environment in which they are employed.central bank, financial stability, communication, event study

Policy Risk and the Business Cycle

The argument that policy risk, i.e., uncertainty about monetary and fiscal policy, has been holding back the economic recovery in the U.S. during the Great Recession has a large popular appeal. We analyze the role of policy risk in explaining business cycle fluctuations by using an estimated New Keynesian model featuring policy risk as well as uncertainty about technology. We directly measure uncertainty from aggregate time series and find considerable evidence of time-varying policy risk in the data. However, the pure uncertainty-effect of policy risk is unlikely to play a major role in business cycle fluctuations. In the estimated model, output effects are relatively small because the aggregate policy risk shocks are i) too small and ii) not sufficiently amplified

Uncertainty-driven business cycles: assessing the markup channel

A growing recent literature relies on a precautionary pricing motive embedded in representative agent DSGE models with sticky prices and wages to generate negative output effects of uncertainty shocks. We assess whether this model channel is consistent with the data. We build a New Keynesian DSGE model with time-varying wage and price markups and document the predicted conditional comovement of output and markups following demand and supply uncertainty shocks. Using the model as a business cycle accounting device, we also construct aggregate markup series from the data. Time-series techniques are used to identify uncertainty shocks in the data and to study whether the conditional comovement between markups and output is consistent with the one implied by the model. The response to uncertainty shocks is found to be consistent with precautionary wage setting, but not price setting, putting the role of sticky wages into the focus

Uncertainty-driven Business Cycles: Assessing the Markup Channel

A growing recent literature relies on a precautionary pricing motive embedded in representative agent DSGE models with sticky prices and wages to generate negative output effects of uncertainty shocks. We assess whether this theoretical model channel is consistent with the data. Building a New Keynesian model, we show that indeed with sufficient nominal rigidities markups increase and output falls after uncertainty shocks. The model is also used as a business cycle accounting device to construct aggregate markups from the data. Time-series techniques are employed to study the conditional comovement between markups and output in the data. Consistent with the model’s precautionary wage setting, we find that wage markups increase after uncertainty shocks. Price markups in contrast fall. This finding - inconsistent with the model - is corroborated by industry-level data. Overall, these results point to a prominent role for sticky wages in the transmission of uncertainty shocks

Testing for Serial Correlation in Fixed-Effects Panel Data Models

In this paper, we propose three new tests for serial correlation in the disturbances of fixed-effects panel data models. First, a modified Bhargava, Franzini and Narendranathan (1982) panel Durbin-Watson statistic that does not need to be tabulated as it follows a standard normal distribution. Second, a modified Baltagi and Li (1991) LM statistic with limit distribution independent of T, and, third, a test using an unbiased estimator for the autocorrelation coefficient to achieve robustness against temporal heteroskedasticity. The first two tests are robust against cross-sectional but not time dependent heteroskedasticity and the third statistic is robust against both forms of heteroskedasticity. Furthermore, all test statistics can be easily adapted to unbalanced data. Monte Carlo simulations suggest that our new tests have good size and power properties compared to the often used Wooldridge (2002)-Drukker (2003) test

Simple Regression Based Tests for Spatial Dependence

We propose two simple diagnostic tests for spatial error autocorrelation and spatial lag dependence. The idea is to reformulate the testing problem such that the test statistics are asymptotically equivalent to the familiar LM test statistics. Speci cally, our version of the test is based on a simple auxiliary regression and an ordinary regression t-statistic can be used to test for spatial autocorrelation and lag dependence. We also propose a variant of the test that is robust to heteroskedasticity. This approach gives practitioners an easy to implement and robust alternative to existing tests. Monte Carlo studies show that our variants of the spatial LM tests possess comparable size and power properties even in small samples

Risk Matters: A Comment

Jesús Fernández-Villaverde, Pablo A. Guerrón-Quintana, Juan F. Rubio-Ramírez and Martín Uribe (2011) find that risk shocks are an important factor in explaining emerging market business cycles. We show that their model needs to be recalibrated because it underpredicts the targeted business cycle moments by a factor of three once a time aggregation error is corrected. Recalibrating the corrected model for the benchmark case of Argentina, the peak response of output after an interest rate risk shock increases by 63 percent and the contribution of interest rate risk shocks to business cycle volatility more than doubles. Hence, risk matters more in the recalibrated model. However, the recalibrated model does worse in capturing the business cycle properties of net exports once an additional error in the computation of net exports is corrected

Government Spending Shocks in Quarterly and Annual U.S. Time-Series

Government spending shocks are frequently identi?ed in quarterly time-series data by ruling out a contemporaneous response of government spending to other macroeconomic aggregates. We provide evidence that this assumption may not be too restrictive for U.S. annual time-series data.Government spending shocks, Annual Data, Identi?cation

Fiscal News and Macroeconomic Volatility

This paper analyzes the contribution of anticipated capital and labor tax shocks to business cycle volatility in an estimated New Keynesian DSGE model. While fiscal policy accounts for 12 to 20 percent of output variance at business cycle frequencies, the anticipated component hardly matters for explaining fluctuations of real variables. Anticipated capital tax shocks do explain a sizable part of inflation and interest rate fluctuations, accounting for between 5 and 15 percent of total variance. In line with earlier studies, news shocks in total account for 20 percent of output variance. Further decomposing this news effect, we find that it is mostly driven by stationary TFP and non-stationary investment-specific technology.Anticipated Tax Shocks; Sources of Aggregate Fluctuations; Bayesian Estimation