We develop a data-rich measure of expected macroeconomic skewness in the US economy. Expected macroeconomic skewness is strongly procyclical, mainly reflects the cyclicality in the skewness of real variables, is highly correlated with the cross-sectional skewness of firm-level employment growth, and is distinct from financial market skewness. Revisions in expected skewness lead to business cycle fluctuations nearly indistinguishable from those induced by the main business cycle shock of Angeletos et al. (2020). This result is robust to controlling for macroeconomic volatility and uncertainty, and alternative macroeconomic shocks. Our findings suggest an important role of higher-order dynamics for business cycle theories.

We compute a common factor summarising asymmetries in the expected distributions of a large set of survey-based economic data series for the euro area. This expected skewness factor is distinct from lower-moment factors and can help improve forecasts of risks to economic activity and inflation. In addition, within a monthly vector autoregression (VAR), we show that revisions to survey-based expected skewness have macroeconomic and financial implications, even when the average assessment and expected volatility reflected in the surveys remain unchanged. The skewness measure could benefit economic policy institutions by supporting timely quantitative assessments of the balance of risks.

We examine the robustness of R&D and productivity relationship in a panel of 16 OECD countries. We control for fifteen productivity determinants predicted by different theoretical models. Following the advances in non-stationary panel data econometrics, we estimate four variants of thirteen specifications. All models appear co-integrated. Results are rigorously scrutinized through extensive bootstrap simulations and sensitivity checks. R&D and human capital emerge robust in all specifications making them universal drivers of productivity across nations. Most other determinants are also significant. Productivity relationships are heterogonous across countries depending on their accumulated stocks of knowledge and human capital.

We use a time-varying parameter dynamic factor model with stochastic volatility estimated using Bayesian methods to disentangle the relative importance of the common component in Federal Housing Finance Agency house price movements from state-specific shocks, over the quarterly period of 1975Q2 to 2017Q4. We find that the contribution of the national factor in explaining fluctuations in house prices is critical. We then use a Bayesian change-point vector autoregressive model that allows for different regimes throughout the sample period, to study the impact of aggregate supply, aggregate demand, (conventional) monetary policy, and term-spread shocks, identified based on sign restrictions on the national component of house price movements. While monetary policy and other shocks are found to be quite dominant early on, we find evidence that the national factor has been detached from the identified macroeconomic shocks since 2014, thus suggesting that a “national bubble” might be brewing again in the US housing market.

This article identifies shocks to the Federal Reserve's inflation target as vector autoregression innovations that make the largest contribution to future movements in long-horizon inflation expectations. The effectiveness of this scheme is documented via Monte-Carlo experiments. The estimated impulse responses indicate that a positive shock to the target is associated with a large increase in inflation and long-term interest rates in the United States. Target shocks are estimated to be a vital factor behind the increase in inflation during the pre-1980 period and are an important driver of the decline in long-term interest rates over the last two decades.

Long-term interest rates of small open economies (SOE) correlate strongly with the USA long-term rate. Can central banks in those countries decouple from the United States? An estimated Dynamic Stochastic General Equilibrium (DSGE) model for the UK (vis-á-vis the USA) establishes three structural empirical results: (1) Comovement arises due to nominal fluctuations, not through real rates or term premia; (2) the cause of comovement is the central bank of the SOE accommodating foreign inflation trends, rather than systematically curbing them; and (3) SOE may find themselves much more affected by changes in USA inflation trends than the United States itself. All three results are shown to be intuitive and backed by off-model evidence.

We estimate a novel state-space model to jointly identify international technology trend shocks originating in the US economy as well as shocks that are specific to the UK economy. We further differentiate between technological innovations arising from changes in total factor productivity (TFP) and changes in investment specific technology (IST). The long run restrictions used to identify the structural trends in the data are informed by a standard twocountry structural model. We find that international non-stationary technology shocks explain about 26% of the variance of UK GDP. About two thirds of this contribution is driven by the international IST shock. UK-specific disturbances account for the bulk of the volatility in the data. When estimating the effects of international IST and TFP shocks on the remaining G7 countries, we find results are consistent with those for the UK in that the international productivity shocks play a relevant role in explaining aggregate fluctuations. An impulse response function matching exercise shows that the structural model, which informed the long-run restrictions used in our empirical investigation, can generate dynamics consistent with those in the data.

In this paper we assess the macroeconomic effects of two of the flagship unconventional monetary policies used by the Bank of England during the later stages of the global economic crisis: additional quantitative easing (QE) and the introduction of the funding for lending scheme (FLS). We argue that these policies can be seen as complements, as QE effectively bypasses the banks by attempting to reduce risk-free yields directly in order to have a wider effect on asset prices, while FLS operates directly through banks by reducing their funding costs and increasing incentives to lend. We attempt to quantify the effects of these policies by estimating their impact on long-term interest rates and bank funding costs, respectively, and then tracing out their wider effects on the macroeconomy using simulations from a large Bayesian vector autoregression (VAR), which are cross-checked with a simpler Auto-regressive distributed lag (ARDL) approach. We find that the second round of the Bank's QE purchases during 2011-2012 and the initial phase of the FLS each boosted GDP in the UK by around 0.5-0.8%. Their effect on inflation was also broadly positive reaching around 0.6 pp, at its peak. (c) 2018 Board of Trustees of the University of Illinois. Published by Elsevier Inc. All rights reserved.

This paper uses a ‘trendy’ approach to understand UK inflation dynamics. It focuses on the time series to isolate a low-frequency and slow-moving component of inflation (the trend) from deviations around this trend. We find that this slow-moving trend explains a substantial share of UK inflation dynamics. International prices are significantly correlated with the short-term cyclical movements in inflation around its trend, and the exchange rate is significantly correlated with movements in the slow-moving, persistent trend. Other variables emphasized in standard inflation models — such as slack and inflation expectations — may also play some role, but their significance varies and the magnitude of their effects is substantially smaller than for commodity prices and the exchange rate. These results highlight the sensitivity of UK inflation dynamics to events in the rest of the world. They also provide guidance on when deviations of inflation from target are more likely to be temporary, and when (and how quickly) a monetary policy response is appropriate.

We present a new method for estimating Bayesian vector auto-regression (VAR) models using priors from a dynamic stochastic general equilibrium (DSGE) model. We use the DSGE model priors to determine the moments of an independent Normal-Wishart prior for the VAR parameters. Two hyper-parameters control the tightness of the DSGE-implied priors on the autoregressive coefficients and the residual covariance matrix respectively. Determining these hyper-parameters by selecting the values that maximize the marginal likelihood of the Bayesian VAR provides a method for isolating subsets of DSGE parameter priors that are at odds with the data. We illustrate the ability of our approach to correctly detect incorrect DSGE priors for the variance of structural shocks using a Monte Carlo experiment. We also demonstrate how posterior estimates of the DSGE parameter vector can be recovered from the BVAR posterior estimates: a new ‘quasi-Bayesian’ DSGE estimation. An empirical application on US data reveals economically meaningful differences in posterior parameter estimates when comparing our quasi-Bayesian estimator with Bayesian maximum likelihood. Our method also indicates that the DSGE prior implications for the residual covariance matrix are at odds with the data.

This paper uses structural VARs to show that the response of US stock prices to fiscal shocks changed in 1980. Over the period 1955–1979 an expansionary spending or revenue shock was associated with higher stock prices. After 1980 the response of stock prices to the same shock became negative. Using a DSGE model with a detailed fiscal sector, we show the pre-1980 results may be driven by an expansion in supply after the fiscal shock. In contrast, endogenous growth mechanisms appear to be weaker in the post-1980 period with positive fiscal shocks pushing down consumption and TFP and causing inflation and the real interest rate to rise.

This paper documents state dependence in labour market fluctuations. Using a Threshold Vector Autoregression model (TVAR), we establish that the unemployment rate, the job separation rate, and the job finding rate exhibit a larger response to productivity shocks during periods with low aggregate productivity. A Diamond-Mortensen-Pissarides model with endogenous job separation and on-the-job search replicates these empirical regularities well. We calibrate the model to match the standard deviation of the job-transition rates explained by productivity shocks in the TVAR, and show that the model explains 88 percent of the state dependence in the unemployment rate, 76 percent for the separation rate and 36 percent for the job finding rate. The key channel underpinning state dependence in both job separation and job finding rates is the interaction of the firm's reservation productivity level and the distribution of match-specific idiosyncratic productivity. Results are robust across several variations to the baseline model.

This paper documents state dependence in labor market fluctuations. Using a Threshold Vector Autoregression model (TVAR), we establish that the unemployment rate, the job separation rate, and the job finding rate exhibit a larger response to productivity shocks during periods with low aggregate productivity. A Diamond-Mortensen-Pissarides model with endogenous job separation and on-the-job search replicates these empirical regularities well. We calibrate the model to match the standard deviation of the job-transition rates explained by productivity shocks in the TVAR, and show that the model explains 88 percent of the state dependence in the unemployment rate, 76 percent for the separation rate and 36 percent for the job finding rate. The key channel underpinning state dependence in both job separation and job finding rates is the interaction of the _rm's reservation productivity level and the distribution of match-specific idiosyncratic productivity. Results are robust across several variations to the baseline model.

This paper identifies a precautionary banking liquidity shock via a set of sign, zero and forecast variance restrictions imposed. The shock proxies the banking sector’s reluctance to lend to the real economy induced by an exogenous preference change for liquid assets. Through the lens of a DSGE model, the precautionary liquidity shock is shown to work through two channels: reserves (balance sheet) and the deposit rate (intertemporal effect). The overall effect is a downward co-movement in output, consumption, investment, and prices, which is amplified the higher are the long-run risks in the economy and banks’ responsiveness to potential risk.

We develop a VAR that allows the estimation of the impact of monetary policy shocks on volatility. Estimates for the US suggest that an increase in the policy rate by 1% is associated with a rise in unemployment and inflation volatility of about 15%. Using a New Keynesian model, with search and matching labour frictions and Epstein-Zin preferences we show that these volatility effects are driven by the coexistence of agents' fears of unemployment and concerns about the (in) ability of the monetary authority to reverse deviations from the policy rule with the impact magnified by the agents' preferences. (c) 2019 Elsevier B.V. All rights reserved.

Forecasts play a critical role at inflation-targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. Commonly used statistical procedures, however, implicitly put a lot of weight on type I errors (or false positives), which result in a relatively low power of tests to identify forecast breakdowns in small samples. We develop a procedure which aims at capturing the policy cost of missing a break. We use data-based rules to find the test size that optimally trades off the costs associated with false positives with those that can result from a break going undetected for too long. In so doing, we also explicitly study forecast errors as a multivariate system. The covariance between forecast errors for different series, though often overlooked in the forecasting literature, not only enables us to consider testing in a multivariate setting but also increases the test power. As a result, we can tailor the choice of the critical values for each series not only to the in-sample properties of each series but also to how the series for forecast errors covary.

This article develops a change-point VAR model that isolates four major macroeconomic regimes in the US since the 1960s. The model identifies shocks to demand, supply, monetary policy, and spread yield using restrictions from a general equilibrium model. The analysis discloses important changes to the statistical properties of key macroeconomic variables and their responses to the identified shocks. During the crisis period, spread shocks became more important for movements in unemployment and inflation. A counterfactual exercise evaluates the importance of lower bond-yield spread during the crises and suggests that the Fed's large-scale asset purchases helped lower the unemployment rate by about 0.6 percentage points, while boosting inflation by about 1 percentage point.

This article investigates if the impact of uncertainty shocks on the U.S. economy has changed over time. To this end, we develop an extended factor augmented vector autoregression (VAR) model that simultaneously allows the estimation of a measure of uncertainty and its time-varying impact on a range of variables. We find that the impact of uncertainty shocks on real activity and financial variables has declined systematically over time. In contrast, the response of inflation and the short-term interest rate to this shock has remained fairly stable. Simulations from a nonlinear dynamic stochastic general equilibrium (DSGE) model suggest that these empirical results are consistent with an increase in the monetary authorities' antiinflation stance and a flattening of the Phillips curve. Supplementary materials for this article are available online.