Using formal statistical tests, we detect (i) significant volatility increases for various types of capital flows for a period of changes in business cycle comovement among the G7 countries, and (ii) mixed evidence of changes in covariances and correlations with a set of macroeconomic variables.
Oil prices rose sharply prior to the onset of the 2007-2009 recession. Hamilton (2005) noted that nine of the last ten recessions in the United States were preceded by a substantial increase in the price of oil.
Recent research [e.g., DeMiguel, Garlappi and Uppal, (2009), Rev. Fin. Studies] has cast doubts on the out-of-sample performance of optimizing portfolio strategies relative to naive, equally weighted ones. However, existing results concern the simple case in which an investor has a one-month horizon and meanvariance preferences.
We examine whether simple VARs can produce empirical portfolio rules similar to those obtained under a range of multivariate Markov switching models, by studying the effects of expanding both the order of the VAR and the number/selection of predictor variables included.
We use a dynamic latent factor model to analyze comovements in OECD budget surpluses. The
world factor underlying common fluctuations in budget surpluses across countries explains an
average of 28 to 44 percent of the variation in individual country surpluses.
The number of commercial banks in the United States has fallen by more than 50 percent since 1984. This consolidation of the U.S. banking industry and the accompanying large increase in average (and median) bank size have prompted concerns about the effects of consolidation and increasing bank size on market competition and on the number of banks that regulators deem “too–big–to–fail.”
Many studies have documented disparities in the regional responses to monetary policy shocks. However, because of computational issues, the literature has often neglected the richest level of disaggregation: the city. In this paper, we estimate the city-level responses to monetary policy shocks in a Bayesian VAR.
It has become common practice to estimate the response of asset prices to monetary policy actions using market-based measures such as the unexpected change in the federal funds futures rate as proxies for monetary policy shocks.
Nearly all journal rankings in economics use some weighted average of citations to calculate a journal’s impact. These rankings are often used, formally or informally, to help assess the publication success of individual economists or institutions.
This paper develops a framework for inferring common Markov-switching components in a panel data set with large cross-section and time-series dimensions. We apply the framework to studying similarities and differences across U.S. states in the timing of business cycles.
Advances in information-processing technology have significantly eroded the advantages of small scale and proximity to customers that traditionally enabled community banks and other small-scale lenders to thrive.
Welfare gains to long-horizon investors may derive from time diversification that exploits non-zero intertemporal return correlations associated with predictable returns. Real estate may thus become more desirable if its returns are negatively serially correlated.
Using a self-exciting threshold autoregressive model, we confirm the presence of nonlinearities in sectoral real exchange rate (SRER) dynamics across Mexico, Canada and the US in the pre-NAFTA and post-NAFTA periods.
We study the cross-section correlations of net, total, and disaggregated capital flows for the major source and recipient European Union countries. We seek evidence of changes in these correlations since the introduction of the euro to understand whether the European Union can be considered a unique entity with regard to its international capital flows.
We analyze the second-moment properties of the components of international capital flows and their relationship to business cycle variables (output, investment, and real interest rate) in 22 industrial and emerging countries.
Motivated by the common finding that linear autoregressive models often forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but a subset of the coefficients are treated as being local-to-zero. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive MSE-minimizing weights for combining the restricted and unrestricted forecasts. Monte Carlo and empirical analyses verify the practical effectiveness of our combination approach.
U.S. credit unions serve 93 million members, hold 10 percent of U.S. savings deposits, and make 13.2 percent of all non-revolving consumer loans. Since 1985, the share of U.S. depository institution assets held by credit unions has nearly doubled, and the average (inflation-adjusted) size of credit unions has increased over 600 percent.
Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR.
We explore the influence of city-level business cycle fluctuations on crime in 20 large cities in the United States. Our monthly time-series analysis considers seven crimes over an approximately 20-year period: murder, rape, assault, robbery, burglary, larceny, and motor vehicle theft.