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This paper develops a semi-structural model to jointly estimate "stars" - long-run levels of output (its growth rate), the unemployment rate, the real interest rate, productivity growth, price inflation, and wage inflation. It features links between survey expectations and stars, time-variation in macroeconomic relationships, and stochastic volatility. Survey data help discipline stars' estimates and have been crucial in estimating a high-dimensional model since the pandemic. The model has desirable real-time properties, competitive forecasting performance, and superior fit to the data compared to variants without the empirical features mentioned above. The by-products are estimates of various objects of great interest to the broader profession. [ABSTRACT FROM AUTHOR]

We show that macroeconomic uncertainty can be considered as exogenous when assessing its effects on the U.S. economy. Instead, financial uncertainty can at least in part arise as an endogenous response to some macroeconomic developments, and overlooking this channel leads to distortions in the estimated effects of financial uncertainty shocks on the economy. We obtain these empirical findings with an econometric model that simultaneously allows for contemporaneous effects of both uncertainty shocks on economic variables and of economic shocks on uncertainty. While the traditional econometric approaches do not allow us to simultaneously identify both of these transmission channels, we achieve identification by exploiting the heteroskedasticity of macroeconomic data. Methodologically, we develop a structural VAR with time-varying volatility in which one of the variables (the uncertainty measure) impacts both the mean and the variance of the other variables. We provide conditional posterior distributions for this model, which is a substantial extension of the popular leverage model of Jacquier, Polson, and Rossi (2004), and provide an MCMC algorithm for estimation. [ABSTRACT FROM AUTHOR]

The paper considers a New Keynesian framework in which agents form expectations based on a combination of autoregressive mis-specified forecasts and myopia. The proposed expectations formation process is shown to be consistent with all three empirical facts on consensus inflation forecasts. However, while mis-specified forecasts can be both sufficient and necessary to match all three facts, myopia alone is neither. The paper then derives the general equilibrium solution consistent with the proposed expectations formation process and estimates the model with likelihood-based Bayesian methods, yielding three novel results: (i) macroeconomic data strongly prefer a combination of autoregressive mis-specified forecasting rules - of the VAR(1) or AR(1) type - and myopia over other alternatives; (ii) no strong evidence is found in favor of VAR(1) forecasts over simple AR(1) rules; and (iii) frictions such as habit in consumption, which are typically necessary for models with full-information rational expectations, are significantly less important, because the proposed expectations generate substantial internal persistence and amplification to exogenous shocks. Simulated inflation expectations data from the estimated general equilibrium model reflect the three empirical facts on forecasting data. [ABSTRACT FROM AUTHOR]

This paper seeks to understand the general equilibrium effects of time-varying geopolitical risk in oil markets. Answering this question requires simultaneously modeling several features including macroeconomic disasters and geopolitically driven oil production disasters, oil storage and precautionary savings, and the endogenous determination of uncertainty about output and the price of oil. We find that oil price uncertainty tends to be driven by macroeconomic uncertainty. Shifts in the probability of a geopolitically driven major oil supply disruption have meaningful effects on the price of oil and the macro economy, but the resulting oil price uncertainty is not a major driver of fluctuations in macroeconomic aggregates. [ABSTRACT FROM AUTHOR]

An increasing share of corporate loans, a critical source of firm credit, are sold off banks' balance sheets and actively traded in a secondary over-the-counter market. We develop a microfounded equilibrium search-theoretic model with labor, credit, and financial markets to explore how this secondary loan market affects the real economy, highlighting a trade-off: while the market reduces the steady-state level of unemployment by 0.6pp, it amplifies its response to a 1% productivity drop from 3.6% to 4.3%. Secondary market frictions matter significantly: eliminating them would not only reduce unemployment by 1.2pp, but also dampen its volatility down to 2.7%. [ABSTRACT FROM AUTHOR]

The article discusses the rising interest of the banking disintermediation shocks emanating from and propagating through the banking system, in the macroeconomics literature due to fixed deposit rate ceilings. Topics discussed include nonlinear impact on monetary policy on bank level credit growth, identifying credit supply shifts in the empirical literature, and bank capitalization.

It is common in applied work to estimate responses of macroeconomic aggregates to news shocks derived from surprise changes in daily futures prices around the date of policy announcements. This requires mapping the daily surprises into a monthly shock that may be used as an external instrument in a monthly VAR model or local projection. The standard approach has been to sum these daily surprises over the course of a given month when constructing the monthly proxy variable, ignoring the accounting relationship between daily and average monthly price data. In this paper, I provide a new approach to constructing monthly proxies from daily surprises that takes account of this link and revisit the question of how to use OPEC announcements to identify news shocks in VAR models of the global oil market. The proposed approach calls into question the interpretation of the identified shock as oil supply news and implies quantitatively and qualitatively different estimates of the macroeconomic impact of OPEC announcements. [ABSTRACT FROM AUTHOR]

Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the "data speak." Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions. [ABSTRACT FROM AUTHOR]

Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a "synthesis" function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from the euro area's Survey of Professional Forecasters. The second combines density forecasts of US inflation produced by many regression models involving different predictors. Both applications demonstrate the benefits - in terms of improved forecast accuracy and interpretability - of modeling the synthesis function nonparametrically. [ABSTRACT FROM AUTHOR]

Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the "data speak." Simulation evidence and an application revisiting GDP growth uncertainties in the US demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions. [ABSTRACT FROM AUTHOR]

This paper clarifies the conditions under which the state-of-the-art approach to identifying TFP news shocks in Kurmann and Sims (2021, KS) identifies not only news shocks but also surprise shocks. We examine the ability of the KS procedure to recover responses to these shocks from data generated by a conventional New Keynesian DSGE model. Our analysis shows that the KS response estimator tends to be strongly biased even in the absence of measurement error. This bias worsens in realistically small samples, and the estimator becomes highly variable. Incorporating a direct measure of TFP news into the model and adapting the identification strategy accordingly removes this asymptotic bias and greatly reduces the RMSE when TFP news are correctly measured. However, the high variability of this alternative estimator in small samples suggests caution in interpreting empirical estimates. We examine to what extent empirical estimates of the responses to news and surprise shocks from a range of VAR models based on alternative measures of TFP news are economically plausible. [ABSTRACT FROM AUTHOR]

We use long-run annual cross-country data for 10 macroeconomic variables to evaluate the long-horizon forecast distributions of six forecasting models. The variables we use range from ones having little serial correlation to ones having persistence consistent with unit roots. Our forecasting models include simple time series models and frequency domain models developed in Müuller and Watson (2016). For plausibly stationary variables, an AR(1) model and a frequency domain model that does not require the user to take a stand on the order of integration appear reasonably well calibrated for forecast horizons of 10 and 25 years. For plausibly non-stationary variables, a random walk model appears reasonably well calibrated for forecast horizons of 10 and 25 years. No model appears well calibrated for forecast horizons of 50 years. [ABSTRACT FROM AUTHOR]

This paper uses a large vector autoregression to measure international macroeconomic uncertainty and its effects on major economies. We provide evidence of significant commonality in macroeconomic volatility, with one common factor driving strong comovement across economies and variables. We measure uncertainty and its effects with a large model in which the error volatilities feature a factor structure containing time-varying global components and idiosyncratic components. Global uncertainty contemporaneously affects both the levels and volatilities of the included variables. Our new estimates of international macroeconomic uncertainty indicate that surprise increases in uncertainty reduce output and stock prices, adversely affect labor market conditions, and in some economies lead to an easing of monetary policy. [ABSTRACT FROM AUTHOR]

Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks to macroeconomic indicators. In this paper we examine various choices in the specification of quantile regressions for macro applications, for example, choices related to how and to what extent to include shrinkage, and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, using for evaluation both quantile scores and quantile-weighted continuous ranked probability scores at a range of quantiles spanning from the left to right tail. We find that shrinkage is generally helpful to tail forecast accuracy, with gains that are particularly large for GDP applications featuring large sets of predictors and unemployment and ination applications, and with gains that increase with the forecast horizon. [ABSTRACT FROM AUTHOR]

There have been more than 700,000 opioid overdose deaths since 2000. To analyze the opioid epidemic, a model is constructed where individuals choose whether to use opioids recreationally, knowing the probabilities of addiction and dying. These odds are functions of recreational opioid usage. The model is fit to estimated Markov chains from the US data that summarize the transitions into and out of opioid addiction as well as to a deadly overdose. The epidemic is broken down into two subperiods: 2000– 2010 and 2010–2019. The opioid epidemic’s drivers, their impact on employment, and the impact of medical interventions are examined. Lax prescribing practices and misinformation about the risk of addiction are important drivers of the first half of the epidemic. Falling prices for black-market opioids combined with an increase in their lethality are found to be important for the second half. [ABSTRACT FROM AUTHOR]

The provision of bank credit to firms and households affects macroeconomic performance. We use survey measures of changes in bank lending standards, disaggregated by loan category, to quantify the effect of changes in banks’ attitudes toward lending on aggregate output, inflation, and interest rates. Bank lending to businesses is particularly important for macroeconomic outcomes, with peak effects on output of around half a percentage point after four quarters of the initial shock. These effects depend on the stage of the business cycle and the proximity of the short-term interest rate to its effective lower bound. The effects are larger when output is growing below trend and when the interest rate is away from its lower bound. We also find that the response of the economy to lending-standards shocks is asymmetric, with tightening shocks having larger effects on output. [ABSTRACT FROM AUTHOR]

In this paper we propose a hierarchical shrinkage approach for multi-country VAR models. In implementation, we consider three different scale mixtures of Normals priors -- specifically, Horseshoe, Normal-Gamma, and Normal-Gamma-Gamma priors. We provide new theoretical results for the Normal-Gamma prior. Empirically, we use a quarterly data set for the G7 economies to examine how model specifications and prior choices affect the forecasting performance for GDP growth, inflation, and a short-term interest rate. We find that hierarchical shrinkage, particularly as implemented with the Horseshoe prior, is very useful in forecasting inflation. It also has the best density forecast performance for output growth and the interest rate. Adding foreign information yields benefits, as multi-country models generally improve on the forecast accuracy of single-country models. [ABSTRACT FROM AUTHOR]

A recent literature within quantitative macroeconomics has advocated the use of continuous-time methods for dynamic programming problems. In this paper we explore the relative merits of continuous-time and discrete-time methods within the context of stationary and nonstationary income fluctuation problems. For stationary problems in two dimensions, the continuous-time approach is both more stable and typically faster than the discrete-time approach for any given level of accuracy. In contrast, for convex lifecycle problems (in which age or time enters explicitly), simply iterating backwards from the terminal date in discrete time is superior to any continuous-time algorithm. However, we also show that the continuous-time framework can easily incorporate nonconvexities and multiple controls—complications that often require either problem-specific ingenuity or nonlinear root-finding in the discrete-time context. In general, neither approach unequivocally dominates the other, making the choice of one over the other an art, rather than an exact science. [ABSTRACT FROM AUTHOR]

As of 2022, the share of U.S. income accruing to labor is at its lowest level since the Great Depression. Updating previous studies with more recent observations, I document the continuing decline of the labor share for the United States, other countries, and various industries. I discuss how changes in technology and product, labor, and capital markets affect the trend of the labor share. I also examine its relationship with other macroeconomic trends, such as rising markups, higher concentration of economic activity, and globalization. I conclude by offering some perspectives on the economic and policy implications of the labor share decline. [ABSTRACT FROM AUTHOR]

This paper studies the effects of Federal Open Market Committee (FOMC) forward guidance language. I estimate two policy surprises at FOMC meetings: a change in the current federal funds rate and an orthogonal change in the expected path of the federal funds rate. From February 2000 to June 2003, the FOMC only gave forward guidance about risks to the economic outlook, and a surprise increase in the expected federal funds rate path had expansionary effects. This is consistent with models of central bank information effects, where a positive economic outlook causes private agents to revise up their expectations for the economy. From August 2003 to May 2006, the FOMC also gave forward guidance about policy inclinations, and a surprise increase in the federal funds rate path had contractionary effects. These results are consistent with standard macroeconomic models of forward guidance. Overall, the effects of forward guidance depend on the FOMC's choice to use one or both of the economicoutlook and policy-inclination aspects of forward guidance. [ABSTRACT FROM AUTHOR]

Using supervisory data from large U.S. bank holding companies (BHCs), we document that operational loss recovery rates decrease in macroeconomic downturns. This procyclical relationship varies by business lines and loss event types and is robust to alternative data aggregations, macroeconomic measurement horizons, and estimation methodologies. Further analysis shows that resource constraints faced by BHC risk management functions are a plausible explanation for these patterns. Our findings offer new evidence on how economic shocks transmit to banking industry losses with implications for risk management and supervision. [ABSTRACT FROM AUTHOR]

This paper develops a threshold-augmented dynamic multi-country model (TG-VAR) to quantify the macroeconomic effects of Covid-19. We show that there exist threshold effects in the relationship between output growth and excess global volatility at individual country levels in a significant majority of advanced economies and in the case of several emerging markets. We then estimate a more general multi-country model augmented with these threshold effects as well as long term interest rates, oil prices, exchange rates and equity returns to perform counterfactual analyses. We distinguish common global factors from trade-related spillovers, and identify the Covid-19 shock using GDP growth forecast revisions of the IMF in 2020Q1. We account for sample uncertainty by bootstrapping the multi-country model estimated over four decades of quarterly observations. Our results show that the Covid-19 pandemic will lead to a significant fall in world output that is most likely long-lasting, with outcomes that are quite heterogenous across countries and regions. While the impact on China and other emerging Asian economies is estimated to be less severe, the United States, the United Kingdom, and several other advanced economies may experience deeper and longer-lasting effects. Non-Asian emerging markets stand out for their vulnerability. We show that no country is immune to the economic fallout of the pandemic because of global interconnections as evidenced by the case of Sweden. We also find that long-term interest rates could fall significantly below their recent lows in core advanced economies, but this does not seem to be the case in emerging markets. [ABSTRACT FROM AUTHOR]

This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, classical and Bayesian quantile regressions, quantile MIDAS regressions) and also different methods for data reduction (either forecasts from models that incorporate data reduction or the combination of forecasts from smaller models). Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves forward within a quarter, making additional data available, with monthly data more important to accuracy than weekly data. Accuracy also typically improves with the use of financial indicators in addition to a base set of macroeconomic indicators. The better-performing models or methods include the Bayesian regression model with stochastic volatility, Bayesian quantile regression, some approaches to data reduction that make use of factors, and forecast averaging. In contrast, simple quantile regression and quantile MIDAS regression perform relatively poorly. [ABSTRACT FROM AUTHOR]

A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and has relied on quantile regression methods to estimate tail risks. In this paper we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification featuring a common volatility factor that is a function of past financial conditions. Even though the conditional predictive distributions from the VAR models are symmetric, our estimated models featuring time-varying volatility yield more time variation in downside risk as compared to upside risk--a feature highlighted in other work that has advocated for quantile regression methods or focused on asymmetric conditional distributions. Overall, the BVAR models perform comparably to quantile regression for estimating tail risks, with, in addition, some gains in standard point and density forecasts. [ABSTRACT FROM AUTHOR]

This paper studies the macroeconomic effects of seven key TCJA provisions, including the tax cuts for individuals and businesses, the bonus depreciation of equipment, the amortization of R&D expenses, and the limits on interest deductibility. I use a dynamic general equilibrium model with interest deductibility and accelerated depreciation. I find that, initially, the tax reform had a small positive impact on output and investment. In the medium term, however, the effect on output will diminish, and the effect on investment will turn negative. The tax reform will depress investment in R&D. Government debt will surge. [ABSTRACT FROM AUTHOR]

Financial integration generates macroeconomic spillovers that may require international monetary policy coordination. We show that individual central banks may set nominal interest rates too low or too high relative to the cooperative outcome. We identify three sufficient statistics that determine whether the Nash equilibrium exhibits under-tightening or over-tightening: the output gap, sectoral differences in labor intensity, and the trade balance response to changes in nominal rates. Independently of the shocks hitting the economy, we find that under-tightening is possible during economic expansions or contractions. For large shocks, the gains from coordination can be substantial. [ABSTRACT FROM AUTHOR]

Is the "information effect" of monetary policy quantitatively important? We first use a simple model to show that under asymmetric information, monetary policy surprises are correlated with the unobserved state of the economy. This correlation implies that monetary policy surprises provide information about the state of the economy, and at the same time, explains why the estimation of the information effect may be biased. We then develop a New Keynesian DSGE model under asymmetric information and calibrate model parameters to match macroeconomic dynamics in the US and forecasting accuracy in the Greenbook. Under our calibration, both the central bank and the private sector initially have noisy information. Over time, the information effect of monetary policy mitigates information frictions by enhancing the two-way learning between the central bank and the private sector. [ABSTRACT FROM AUTHOR]

We develop a exible semi-structural time-series model to estimate jointly several macroeconomic "stars" -- i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, price ination, and wage ination. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on ination and interest rate modeling, we explicitly model the links between long-run survey expectations and stars to improve the stars' econometric estimation. Our approach permits time variation in the relationships between various components, including time variation in error variances. To tractably estimate the large multivariate model, we use a recently developed precision sampler that relies on Bayesian methods. The by-products of this approach are the time-varying estimates of the wage and price Phillips curves, and the pass-through between prices and wages, both of which provide new insights into these empirical relationships' instability in US data. Generally, the contours of the stars echo those documented elsewhere in the literature - estimated using smaller models - but at times the estimates of stars are different, and these differences can matter for policy. Furthermore, our estimates of the stars are among the most precise. Last, we document the competitive real-time forecasting properties of the model and, separately, the usefulness of stars' estimates as steady-state values in external models. [ABSTRACT FROM AUTHOR]

This paper documents a strong negative correlation between macroeconomic uncertainty and real GDP growth since the Great Recession. Prior to that event the correlation was weak, even when conditioning on recessions. At the same time, many central banks reduced their policy rate to its zero lower bound (ZLB), which we contend contributed to the strong correlation between macroeconomic uncertainty and real GDP growth. To test that theory, we use a model where the ZLB occasionally binds. The model roughly matches the correlation in the data--away from the ZLB the correlation is weak but strongly negative when the ZLB binds. [ABSTRACT FROM AUTHOR]

This paper examines the effectiveness of forward guidance in an estimated New Keynesian model with imperfect central bank credibility. Forward guidance and the credibility of the central bank are uniquely modeled by utilizing a game-theoretic evolutionary framework. We estimate credibility for the U.S. Federal Reserve with Bayesian methods exploiting survey data on interest rate expectations from the Survey of Professional Forecasters (SPF). The results provide important takeaways: (1) The estimate of Federal Reserve credibility in terms of forward guidance announcements is relatively high, which indicates a degree of forward guidance effectiveness, but still one that is below the fully credible case. (2) If a central bank is perceived as less credible, anticipation effects are attenuated and, accordingly, output and inflation do not respond as favorably to forward guidance announcements. (3) Imperfect credibility and forward guidance are an important aspect to resolve the so-called "forward guidance puzzle," which the literature shows arises from the unrealistically large responses of macroeconomic variables to forward guidance statements in structural models with perfect credibility. (4) Imperfect central bank credibility can also explain the evidence of forecasting error predictability based on forecasting disagreement found in the SPF data. Thus, accounting for imperfect credibility is important to model the formation of expectations in the economy and to understand the transmission mechanism of forward guidance announcements. [ABSTRACT FROM AUTHOR]

This paper develops a theory of subjective beliefs that departs from rational expectations, and shows that biases in household beliefs have quantitatively large effects on macroeconomic aggregates. The departures are formalized using model-consistent notions of pessimism and optimism and are disciplined by data on household forecasts. The role of subjective beliefs is quantified in a business cycle model with goods and labor market frictions. Consistent with the survey evidence, an increase in pessimism generates upward biases in unemployment and inflation forecasts and lowers economic activity. The underlying belief distortions reduce aggregate demand and propagate through frictional goods and labor markets. As a by-product of the analysis, solution techniques that preserve the effects of time-varying belief distortions in the class of linear solutions are developed. [ABSTRACT FROM AUTHOR]

This paper shows that the inability to use monetary policy for macroeconomic stabilization leaves a government more vulnerable to a rollover crisis. We study a sovereign default model with selfful fulfilling rollover crises, foreign currency debt, and nominal rigidities. When the government lacks monetary autonomy, lenders anticipate that the government will face a severe recession in the event of a liquidity crisis, and are therefore more prone to run on government bonds. By contrast, a government with monetary autonomy can stabilize the economy and can easily remain immune to a rollover crisis. In a quantitative application, we find that the lack of monetary autonomy played a central role in making the Eurozone vulnerable to a rollover crisis. A lender of last resort can help ease the costs from giving up monetary independence. [ABSTRACT FROM AUTHOR]

The recent global financial crisis reflects numerous breakdowns in the prudential discipline of financial firms. This paper discusses ways to strengthen micro- and macroprudential supervision and restore credible market discipline. The discussion notes that microprudential supervisors are typically assigned a variety of goals that sometimes have conflicting policy implications. In such a setting, the structure of the regulatory agencies and the priority given to prudential goals are critical to achieving those goals. The analysis of macroprudential supervision emphasizes that this supervisor must be both bold and modest: bold in seeking to understand the sources and distributions of systemically important risks and modest about what a supervisor can do without imposing overly restrictive regulations. Finally, the paper argues that the primary responsibility for risk management must rest with firms, not government supervisors. Unfortunately, systemic risk concerns have led governments to shield the private sector from the full losses that dull their incentive to discipline risk taking. This section of the paper suggests that deposit insurance reform, special resolutions for systemically important firms, and requirements that firms plan for their own resolution and contingent capital may all have a role to play in restoring effective market discipline. [ABSTRACT FROM AUTHOR]

We measure the effects of the COVID-19 outbreak on uncertainty, and we assess the consequences of the uncertainty for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on uncertainty. The model includes additional latent volatility states in order to accommodate outliers in volatility, to reduce the influence of extreme observations from the COVID period. Our estimates yield large increases in macroeconomic and financial uncertainty since the onset of the COVID-19 period. These increases have contributed to the downturn in economic and financial conditions, but the contributions of uncertainty are small compared to the overall movements in many macroeconomic and financial indicators. That implies that the downturn is driven more by other dimensions of the COVID crisis than shocks to aggregate uncertainty (as measured by our method). [ABSTRACT FROM AUTHOR]

This paper uses a rational expectations macroeconomic model in which economic agents formulate the probability about the sustainability of the economic policy--that is, policy credibility--using current and lagged values of government expenditures and lagged values of the inflation rate. The estimation of the model is based on Hamilton's switching regime procedure. The contribution of this paper is the empirical estimation of the credibility of the stabilization program implemented in Peru in August 1990. The results of the estimation show that there are two different regimes in the government expenditure process. According to the economic agents' inferences, the stabilization program in Peru is not credible. This lack of credibility in the economic policy of the government authority explains the presence of hysteresis in currency substitution between August 1990 and June 1995. The estimation involves an expected inflation rate that includes the credibility of the economic policy in its formulation. [ABSTRACT FROM AUTHOR]

Athanasios Orphanides and John C. Williams' excellent conference paper, "Inflation Scares and Forecast-Based Monetary Policy," contributes importantly to the new and rapidly growing branch of the literature on bounded rationality and learning in macroeconomics. Their paper, like many others, derives interesting and useful theoretical results that show how the introduction of bounded rationality and learning impacts on the effects of monetary policy shocks and the characteristics of optimal monetary policy rules. This note suggests that some additional empirical work--some "irrational expectations econometrics," if you will-- might serve to make these purely theoretical results seem more relevant and convincing. [ABSTRACT FROM AUTHOR]

Recent research has shown that a reliable vector autoregressive model (VAR) for forecasting and structural analysis of macroeconomic data requires a large set of variables and modeling time variation in their volatilities. Yet, there are no papers jointly allowing for stochastic volatilities and large datasets, due to computational complexity. Moreover, homoskedastic VAR models for large datasets so far restrict substantially the allowed prior distributions on the parameters. In this paper we propose a new Bayesian estimation procedure for (possibly very large) VARs featuring time varying volatilities and general priors. This is important both for reduced form applications, such as forecasting, and for more structural applications, such as computing response functions to structural shocks. We show that indeed empirically the new estimation procedure performs very well for both tasks. [ABSTRACT FROM AUTHOR]

Flexible exchange rates can facilitate price adjustments that buffer macroeconomic shocks. We test this hypothesis using adjustments to the gold standard during the Great Depression. Using prices at the goods level, we estimate exchange rate pass-through. Using novel monthly data on city-level economic activity, combined with employment composition and sectoral export data, we show that American exporting cities were significantly affected by changes in bilateral exchange rates. With those results we calibrate a general equilibrium model to obtain aggregate effects from cross-sectional estimates. We show that the gold standard deepened the Great Depression, and abandoning it was a key driver of the economic recovery. [ABSTRACT FROM AUTHOR]

We develop a exible semi-structural time-series model to estimate jointly several macroeconomic "stars" -- i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, the price ination, and wage ination. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on ination and interest rate modeling, we explicitly model the links between long-run survey expectations and stars to improve the stars' econometric estimation. Our approach permits time variation in the relationships between various components, including time variation in error variances. To tractably estimate the large multivariate model, we use a recently developed precision sampler that relies on Bayesian methods. The by-products of this approach are the time-varying estimates of the wage and price Phillips curves, and the pass-through between prices and wages, both of which provide new insights into these empirical relationships' instability in US data. Generally, the contours of the stars echo those documented elsewhere in the literature - estimated using smaller models - but at times the estimates of stars are different, and these differences can matter for policy. Furthermore, our estimates of the stars are among the most precise. Lastly, we document the competitive real-time forecasting properties of the model and, separately, the usefulness of stars' estimates if they were used as steady-state values in external models. [ABSTRACT FROM AUTHOR]

What are the consequences of a nationwide reform of a transfer system based on means-testing toward one of unconditional transfers? I answer this question with a quantitative model to assess the general equilibrium, inequality, and welfare effects of substituting the current US income security system with a universal basic income (UBI) policy. To do so, I develop an overlapping generations model with idiosyncratic income risk that incorporates intensive and extensive margins of the labor supply, on-the-job learning, and child-bearing costs. The tax-transfer system closely mimics the US design. I calibrate the model to the US economy and conduct counterfactual analyses that implement reforms toward a UBI. I find that an expenditure-neutral reform has moderate impacts on agents' labor supply response but induces aggregate capital and output to grow due to larger precautionary savings. A UBI of $1,000 monthly requires a substantial increase in the tax rate of consumption used to clear the government budget and leads to an overall decrease in the macroeconomic aggregates, stemming from a drop in the labor supply. In both cases, the economy has more equally distributed disposable income and consumption. The UBI economy constitutes a welfare loss at the transition if it is expenditure-neutral and results in a gain in the second scenario. [ABSTRACT FROM AUTHOR]

This paper constructs hybrid forecasts that combine both short- and longterm conditioning information from external surveys with forecasts from a standard fi xed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of variables, including those that are not directly tilted but are affected through spillover effects from tilted variables. The forecast accuracy gains for infl ation are substantial, statistically significant, and are competitive with the forecast accuracy from both time-varying VARs and univariate benchmarks. We view our proposal as an indirect approach to accommodating structural change and moving end points. [ABSTRACT FROM AUTHOR]

We investigate the macroeconomic effects of changes in extreme weather in the United States over the past sixty years by incorporating the Actuaries Climate Index (ACI) into a smooth transition vector autoregressive analysis of the United States economy. The ACI tracks changes in the distribution of extreme temperatures, heavy rainfall, drought, high wind, and sea level. While the effects of extreme weather events are negligible at the beginning of the sample, they become more significant later: An increase in the index now persistently reduces the growth rate of industrial production while raising the unemployment rate and inflation. [ABSTRACT FROM AUTHOR]

Sectoral responses to oil price shocks help determine how these shocks are transmitted through the economy. Textbook treatments of oil price shocks often emphasize negative supply effects on oil importing countries. By contrast, the seminal contribution of Lee and Ni (2002) has shown that almost all U.S. industries experience oil price shocks largely through a reduction in their respective demands. Only industries with very high oil intensities face a supply-driven reduction. In this paper, we re-examine this seminal finding using two additional decades of data. Further, we apply updated empirical methods, including structural factor-augmented vector autoregressions, that take into account how industries are linked among themselves and with the remainder of the macro-economy. Our results confirm the original finding of Lee and Ni that demand effects of oil price shocks dominate in all but a handful of U.S. industries [ABSTRACT FROM AUTHOR]

We study the use of a zero mean first difference model to forecast the level of a scalar time series that is stationary in levels. Let bias be the average value of a series of forecast errors. Then the bias of forecasts from a misspecified ARMA model for the first difference of the series will tend to be smaller in magnitude than the bias of forecasts from a correctly specified model for the level of the series. Formally, let P be the number of forecasts. Then the bias from the first difference model has expectation zero and a variance that is O(1/P²), while the variance of the bias from the levels model is generally O(1/P). With a driftless random walk as our first difference model, we confirm this theoretical result with simulations and empirical work: random walk bias is generally one-tenth to one-half that of an appropriately specified model fit to levels. [ABSTRACT FROM AUTHOR]