Using Bayesian Monte Carlo methods, we augment a stochastic distance function measure of bank efficiency and productivity growth with indicators of financial stability, profitability, and capitalization. Our novel Multiple Indicator-Multiple Cause (MIMIC)-style model provides more precise estimates of policy-relevant parameters, including bank efficiency and productivity growth. Analyzing EU-15 banks from 2008 to 2015, we find significant disparities in efficiency, revealing a ‘two-speed’ banking sector. Productivity growth has declined, driven primarily by technological regress rather than managerial inefficiencies. Small and peripheral banks exhibit lower efficiency than larger, core-EU banks, though productivity growth appears stronger among smaller institutions. We show that greater technical efficiency is associated with higher profitability, capitalization, and financial stability, as well as reduced earnings volatility.

We document the forecasting gains achieved by incorporating measures of signed, finite and infinite jumps in forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016.We consider the SPY and 20 stocks that vary by sector, volume and degree of jump activity.We use extended HAR-RV models, and consider different frequencies (5, 60 and 300 seconds), forecast horizons (1, 5, 22 and 66 days) and the use of standard and robust-to-noise volatility and threshold bipower variation measures.Incorporating signed finite and infinite jumps generates significantly better real-time forecasts than the HAR-RV model, although no single extended model dominates.In general, standard volatility measures at the 300-second frequency generate the smallest real-time mean squared forecast errors.Finally, the forecasts from simple model averages generally outperform forecasts from the single best model.

The role of real estate during the global financial and economic crisis has prompted efforts to better incorporate housing and financial channels into macro models, improve housing models, develop macroprudential tools, and reform the financial system. This article provides an overview of major, recent contributions to the literature in relation to earlier research on what drives housing prices and how they affect economic activity. Particularly emphasized are studies, both theoretical and more strongly evidence-based, that connect housing markets with credit markets, house price expectations, financial stability, and the wider economy. The literature reveals much diversity in the international and regional behavior of house prices and the need to improve data tracking key housing supply and demand influences. Also reviewed are studies examining how monetary, macroprudential, and other policies affect house prices and access to housing. This survey is designed to help readers navigate the plethora of recent studies and understand the unsettled issues and avenues for further research. The findings should be of interest to policy makers concerned with financial stability as well as those dealing with the role of housing in the wider economy (JEL E32, E44, E63, G01, G21, R31).

We develop a Mobility and Engagement Index (MEI) based on a range of mobility metrics from Safegraph geolocation data, and validate the index with mobility data from Google and Unacast. We construct MEIs at the county, MSA, state and nationwide level, and link these measures to indicators of economic activity. According to our measures, the bulk of sheltering-in-place and social disengagement occurred during the week of March 15 and simultaneously across the U.S. At the national peak of the decline in mobility in early April, localities that engaged in a 10% larger decrease in mobility than average saw an additional 0.6% of their populations claiming unemployment insurance, an additional 2.8 percentage point reduction in small businesses employment, an additional 2.6 percentage point increase in small business closures, and an additional 3.2 percentage point reduction in new-business applications. A gradual and broad-based resumption of mobility and engagement started in the third week of April.

We study and model the determinants of exposure at default (EAD) for large U.S. construction and land development loans from 2010 to 2017. EAD is an important component of credit risk, and commercial real estate (CRE) construction loans are more risky than income producing loans. This is the first study modeling the EAD of construction loans. The underlying EAD data come from a large, confidential supervisory dataset used in the U.S. Federal Reserve’s annual Comprehensive Capital Assessment Review (CCAR) stress tests. EAD reflects the relative bargaining ability and information sets of banks and obligors. We construct OLS and Tobit regression models, as well as several other machine-learning models, of EAD conversion measures, using a four-quarter horizon. The popular LEQ and CCF conversion measure is unstable, so we focus on EADF and AUF measures. Property type, the lagged utilization rate and loan size are important drivers of EAD. Changing local and national economic conditions also matter, so EAD is sensitive to macro-economic conditions. Even though default and EAD risk are negatively correlated, a conservative assumption is that all undrawn construction commitments will be fully drawn in default.

Using Bayesian Monte Carlo methods, we augment a stochastic distance function measure of bank efficiency and productivity growth with indicators of capitalization, return and risk. Our novel Multiple Indicator-Multiple Cause (MIMIC) style model generates more precise estimates of policy relevant parameters such as returns to scale, technical inefficiency and productivity growth. We find considerable variation in the performance of EU-15 banks over the period 2008 to 2015. For the vast majority of banks, productivity growth – the sum of efficiency and technical changes – is negative, implying that the industry would benefit from innovation. We show that greater technical efficiency is associated with higher profitability, higher capital, a lower probability of default and lower return volatility.

Are inflation dynamics well captured by Phillips Curve models, or has this framework become less relevant over time? The evidence for the U.S. suggests that the slopes of the price and wage Phillips Curves? the short-run inflation-unemployment trade-offs ? are low and have got a little flatter. For example, the recursive estimate of the unemployment coefficient in the core PCE Phillips Curve has fallen a little from -0.09 to -0.07 since the Great Recession. However, the decline is not statistically significant. Dynamic forecasts from the wage and price Phillips Curves estimated using data ending in 2007q4, almost 10 years ago, are pretty close to inflation today. This suggests that (i) low current inflation is not that surprising, and (ii) factors such as increased globalization, increased e-commerce activity, changes in concentration, the aging of the U.S. population and mismeasurement of the NAIRU are not that important (or offset each other). The Phillips Curve is still a useful, albeit imprecise, framework for understanding inflation.

We quantify the magnitude of market segmentation in US consumer market and explore the underlying factors behind this segmentation, using a quarterly panel of retail prices for 45 products in 48 US cities from 1985 to 2009. The extent of market segmentation is estimated using city-pair price differences within the framework of both linear autoregressive (AR) and nonlinear threshold autoregressive (TAR) models. We find that the magnitude of market segmentation varies from one product to another, but even more across city pairs in each product. Contrary to a widespread perception, market segmentation within the US is not necessarily larger for non-tradable services compared to tradable goods. We identify potential drivers of market segmentation by relating the cross-city and cross-product variations of market segmentation to location-specific and product-specific characteristicsdistance, relative city sizes, differences in wage and rent, type of product and proximity to marketplace. Distance, which captures more than transport costs, turns out to be the most salient factor even after controlling for a range of other potential factors. The effect of distance, however, varies substantially across products, with perishable products and locally produced products showing larger distance effect on market segmentation. We find that the magnitude of market segmentation has been somewhat stable during the sample period, but intercity price differences have become more sensitive to distance over time in many products under study.

Although major changes in mortgage finance have occurred since the subprime bust, several issues remain unresolved, centering on the roles of Fannie Mae, Freddie Mac, and the FHA. We analyze how some reforms might affect house prices in a framework rich enough to simulate the impact of several reforms which change mortgage interest rates and/or loan-to-value (LTV) ratios of first time home buyers, the key drivers of house prices in recent decades. Simulations suggest that ending the GSE interest rate subsidy would have small effects, while changes in capital requirements or maximum FHA loan size limits would have larger effects.

National surveys suggest Texans have a relatively low level of financial literacy that can adversely affect decision-making. Since state lawmakers mandated high school financial coursework in 2007, consumer credit measures of young Texas adults have improved.

A combination of much less household debt, revived access to consumer credit and recovering asset prices have holstered U.S. consumer spending. This trend will likely continue despite an estimated 50 percent reduction since the mid-2000s of the housing wealth effect—an important amplifier during the boom years.

National surveys suggest Texans have a relatively low level of financial literacy that can adversely affect decision-making. Since state lawmakers mandated high school financial coursework in 2007, consumer credit measures of young Texas adults have improved.

The UK has a large and persistent current account deficit. Policy makers argue that the deficit is easily financed, temporary and therefore not a sign of structural weakness. We examine the conditions in which it is appropriate for a country to overspend its current income with the intention of repaying loans out of future income. These include: an increase in international capital mobility, larger net holdings of foreign assets, and expectations of faster productivity growth. All three apply to the UK, so official reasoning is, in part, correct. Even so, for several decades there has been a trend deterioration of UK trade performance, most markedly in manufacturing, and we detect no underlying improvement in the 1980s. We highlight structural deficiencies in both demand and supply. On the demand side, we show the importance of rising house prices and easier consumer credit in fuelling the consumer boom to an unwarranted extent. This we trace to unjustifiable subsidies to house ownership. On the supply side, we stress vastly inadequate investment in skill formation, education and physical capital. These structural deficiencies lead us to a less optimistic view than official policy. We draw the policy lessons for the UK and consider whether continental Europe will face similar problems as financial markets are liberalized.

Economic activity in the U.S. overall will benefit from the oil price collapse. The decline will, however, negatively affect oil-producing states such as Texas and North Dakota.

Liquidity mismatch—the risk of a bank being unable to fund increases in assets or meet its obligations as they come due—increased in the U.S. banking sector during the run-up to the financial crisis, especially at the largest institutions, contributing to bank failure and distress.

This article provides an introduction to the JMCB special issue on housing bubbles, the global financial crisis, and the ensuing recessions in countries that experienced housing busts. We focus on five themes that are important for policymakers and researchers alike: the domestic and international factors driving housing booms and busts, the relevance of the housing sector for the real economy, how monetary policy should react to housing booms and busts, how housing and mortgage finance reform could affect financial stability, and the broad lessons learned for macroeconomics and macroprudential policy.

Liquidity mismatch—the risk of a bank being unable to fund increases in assets or meet its obligations as they come due—increased in the U.S. banking sector during the run-up to the financial crisis, especially at the largest institutions, contributing to bank failure and distress.

In 2008, US corporate bond spreads almost reached Great Depression levels. The Fed was a lender of last resort in commercial paper, but not corporate bonds. The Fed’s FRB/US macroeconomic model is used to simulate the effects of the Fed successfully capping the BBB-10 year Treasury spread at 100 basis points above the 1970–2006 average spread. The simulations suggest that real GDP might have been one percentage point higher and the unemployment rate one-half percentage point lower.