We investigate empirically how fiscal shocks—unanticipated and exogenous changes of government consumption growth—impact the sovereign default premium. For this purpose we assemble a new data set for 38 emerging and developed economies. It contains approximately 3,000 observations for the sovereign default premium and three alternative measures of fiscal shocks. We condition our estimates on a) whether shocks are positive or negative and b) initial conditions in terms of fiscal stress. An increase of government consumption hardly affects the default premium. A reduction raises the premium if fiscal stress is severe, but decreases it if initial conditions are benign.
We systematically evaluate how to translate a Calvo wage duration into an implied Rotemberg wage adjustment cost parameter in medium-scale New Keynesian DSGE models by making use of the well-known equivalence of the two setups at first order. We consider a wide range of felicity functions and show that the assumed household insurance scheme and the presence of labor taxation greatly matter for this mapping, giving rise to differences of up to one order of magnitude. Our results account for the inclusion of wage indexing, habit formation in consumption, and the presence of fixed costs in production. We also investigate the conditional and unconditional welfare implications of the wage setting schemes under efficient and distorted steady states.
In how far does inequality matter for the business cycle and vice versa? Using a Bayesian likelihood approach, we estimate a heterogeneous-agent New-Keynesian (HANK) model with incomplete markets and portfolio choice between liquid and illiquid assets. The model enlarges the set of shocks and frictions in Smets and Wouters (2007) by allowing for shocks to income risk and portfolio liquidity. We find income risk to be an important driver of output and consumption. This makes US recessions more de- mand driven relative to the otherwise identical complete markets benchmark (RANK). The HANK model further implies that business cycle shocks and policy responses have significantly contributed to the evolution of US wealth and income inequality.
Under fixed exchange rates, fiscal policy is an effective tool. According to classical views because it impacts the real exchange rate, according to Keynesian views because it impacts output. Both views have merit because the effects of government spending are asymmetric. A spending cut lowers output but does not alter the real exchange rate. A spending increase appreciates the exchange rate but does not alter output unless there is economic slack. We establish these results in a small open economy model with downward nominal wage rigidity and provide empirical evidence on the basis of quarterly time-series data for 38 countries.
Economic nationalism is on the rise, but at what cost? We study this question using the unexpected outcome of the Brexit vote as a natural macroeconomic experiment. Employing synthetic control methods, we first show that the Brexit vote has caused a UK output loss of 1.7-2.5 percent by year-end 2018. An expectations-augmented VAR suggests that these costs are to a large extent driven by a downward revision of growth expectations in response to the vote. Linking quasi-experimental identification to structural time-series estimation allows us to not only quantify the aggregate costs but also to understand the channels through which expected economic disintegration impacts the macroeconomy.
How much credit does Donald Trump deserve for the macroeconomic performance of the US economy? Growth and job creation have been robust during the first 2.5 years since he took office, but this does not prove that Trump made a difference. In this note we develop a counterfactual scenario for how the US economy would have evolved without Trump—-we let a matching algorithm determine which combination of other economies best resembles the pre-election path of the US economy. We then compare the post-election performance of the US economy to this synthetic “doppelganger”. For now there is little evidence for a Trump effect.
Precautionary pricing in representative agent DSGE models with nominal rigidities is commonly used to generate negative output effects of uncertainty shocks. We assess whether this theoretical model channel is consistent with the data. We use a New Keynesian model 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. Consistent with precautionary wage setting, we find that wage markups increase after uncertainty shocks. The evidence for price markups, on the other hand, is mixed, both at the aggregate as well as at the industry level, regardless of whether it is measured along the labor or the intermediate input margin.
Does time-varying business uncertainty/volatility affect the price setting of firms and in what way? We estimate from the micro data of the German ifo Business Climate Survey the impact of idiosyncratic volatility on the extensive margin of firm-level price setting behavior. Heightened uncertainty increases the probability of a price change, suggesting that, for price setting, the volatility effect dominates the ‘wait-and-see’ effect. In a second step, we use structural vector autoregressions to estimate the effects of uncertainty on the intensive pricing margin. Higher uncertainty leads to an increase in price dispersion and to larger price adjustments.
Has heightened uncertainty been a major contributor to the Great Recession and the slow recovery in the U.S.? To answer this question, we identify exogenous changes in six uncertainty proxies and quantify their contributions to GDP growth and the unemployment rate. The answer is no. In total we find that increased macroeconomic and financial uncertainty can explain up to 10 percent of the drop in GDP at the height of the recession and up to 0.7 percentage points of the increased unemployment rates in 2009 through 2011. Our calculations further suggest that only a minor part of the rise in popular uncertainty measures during the Great Recession was driven by exogenous uncertainty shocks.
In this article, we propose various tests for serial correlation in fixed-effects panel data regression models with a small number of time periods. First, a simplified version of the test suggested by Wooldridge (2002) and Drukker (2003) is considered. The second test is based on the Lagrange Multiplier (LM) statistic suggested by Baltagi and Li (1995), and the third test is a modification of the classical Durbin–Watson statistic. Under the null hypothesis of no serial correlation, all tests possess a standard normal limiting distribution as N tends to infinity and T is fixed. Analyzing the local power of the tests, we find that the LM statistic has superior power properties. Furthermore, a generalization to test for autocorrelation up to some given lag order and a test statistic that is robust against time dependent heteroskedasticity are proposed.
This paper discusses tests for the cointegration rank of integrated vector autoregressions when the series are recursively adjusted for deterministic components. To this end, the asymptotic properties of recursive, or adaptive, procedures for the removal of general additive deterministic components are analyzed in two different, complementary, situations. When the stochastic component of the examined time series is weakly stationary (as would be the equilibrium errors), the effect of recursive adjustment vanishes with increasing sample size. When the suitably normalized stochastic component converges weakly to some limiting continuous-time process with integrable paths (as would be the case with the common stochastic trends), recursive adjustment has a permanent effect even asymptotically: the normalized recursively adjusted process converges weakly to a recursively adjusted version of the limiting process. The null limiting distributions of the cointegration rank tests can be expressed in terms of recursively adjusted Brownian motions. Moreover, the finite-sample properties of the cointegration rank tests with recursive adjustment are examined in cases of empirical relevance: the considered deterministic components are a constant, and a constant and a linear trend, respectively. Compared to the likelihood ratio tests or the tests with generalized least squares adjustment, improvements in terms of empirical rejection frequencies under the null are found in finite samples; improvements are found under the alternative as well, with the likelihood ratio test performing increasingly better as the magnitude of the initial condition increases. Regarding rank selection, a very simple combination of the three testing procedures with different adjustments performs best.
We show that the risk-shock business cycle model of Fernández-Villaverde et al. (2011) must 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 and the contribution of interest rate risk shocks to business cycle volatility increase. 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.
The argument that uncertainty about monetary and fiscal policy has been holding back the recovery in the U.S. during the Great Recession has a large popular appeal. This paper uses an estimated New Keynesian model to analyze the role of policy risk in explaining business cycles. We directly measure risk from aggregate data and find a moderate amount of time-varying policy risk. The ‘pure uncertainty’ effect of this policy risk is unlikely to play a major role in business cycle fluctuations. In the estimated model, output effects are relatively small because policy risk shocks are (i) too small and (ii) not sufficiently amplified.
Central banks regularly communicate about financial stability issues. This article asks how such communications affect financial markets, based on a unique dataset covering more than 1,000 releases of Financial Stability Reports (FSRs) and speeches by 37 central banks over the past 14 years. The findings suggest that optimistic FSRs lead to significant and potentially long-lasting positive abnormal stock market returns, whereas no such effect is found for pessimistic FSRs. Speeches and interviews, in contrast, have smaller effects on market returns during tranquil times but have been influential during the 2007–10 global financial crisis.
This paper analyzes the contribution of anticipated capital and labor tax shocks to business cycle volatility in an estimated New Keynesian business cycle model. While fiscal policy accounts for about 15% of output variance at business cycle frequencies, this mostly derives from anticipated government spending shocks. Tax shocks, both anticipated and unanticipated, contribute little to the fluctuations of real variables. However, anticipated capital tax shocks do explain a sizable part of inflation fluctuations, accounting for up to 12% of its variance. In line with earlier studies, news shocks in total account for about 50% of output variance. Further decomposing this news effect, we find permanent total factor productivity news shocks to be most important. When looking at the federal level instead of total government, the importance of anticipated tax and spending shocks significantly increases, suggesting that fiscal policy at the subnational level typically counteracts the effects of federal fiscal policy shocks.
Does the fiscal multiplier depend on the exchange rate regime? To address this question, we first estimate a panel vector autoregression (VAR) model on time-series data for OECD countries. We identify the effects of unanticipated government spending shocks in countries with fixed and floating exchange rates, while controlling for anticipated changes in government spending. In a second step, we interpret the evidence through the lens of a New Keynesian small open economy model. We find that government spending multipliers are considerably larger under fixed exchange rate regimes and that the New Keynesian model provides a satisfactory account of the evidence.
In response to the financial crisis of 2007–10, many central banks have been given responsibility for macroprudential supervision. This paper argues that central bank communication will play a central role for that purpose, and makes the point that it should be generally geared towards clarity, transparency and predictability, in order to enhance the effectiveness of macroprudential policies and ensure central bank accountability. Moreover, central banks must manage expectations by clearly communicating what macroprudential policy can achieve, and what its limitations are, to counteract reputational risks. This paper also provides empirical evidence showing that financial markets react significantly and systematically to central bank communication about financial stability issues. However, some forms of communication – such as through speeches – may at times raise volatility and uncertainty, in particular during crises, thus underlining the importance of choosing carefully a communication strategy on macroprudential policy which is suited for a given market environment.
Government spending shocks are frequently identified 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 annual time-series data.
We propose simple and robust diagnostic tests for spatial error autocorrelation and spatial lag dependence. The idea is to reformulate the testing problem such that the outer product of gradients (OPG) variant of the LM test can be employed. Our versions of the tests are based on simple auxiliary regressions, where ordinary regression t- and F-statistics can be used to test for spatial autocorrelation and lag dependence. An important advantage of the proposed test statistics is that they are robust against heteroscedastic errors. Therefore, our approach gives practitioners an easy to implement and robust alternative to existing tests.