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.
The real interest rate plays a central role in many important financial and macroeconomic models, including the consumption-based asset pricing model, neoclassical growth model, and models of the monetary transmission mechanism. We selectively survey the empirical literature that examines the time-series properties of real interest rates.
We explore the relationship between disaggregated trading flows, the Canada/U.S. dollar (CAD/USD) market and U.S. macroeconomic announcements with a novel data set of unprecedented breadth and length. <a href="http://research.stlouisfed.org/econ/cneely/Data_Appendix_The_Dynamic_Interaction.pdf">Data Appendix</a>.
We use multivariate regime switching vector autoregressive models to characterize the time-varying linkages among short-term interest rates (monetary policy) and stock returns in the Irish, the US and UK markets.
This article deals with using panel data to infer regime changes that are common to all of the cross section. The methods presented here apply to Markov switching vector autoregressions, dynamic factor models with Markov switching and other multivariate Markov switching models.
We use recently proposed tests to extract jumps and cojumps from three types of assets: stock index futures, bond futures, and exchange rates. We then characterize the dynamics of these discontinuities and informally relate them to U.S. macroeconomic releases before using limited dependent variable models to formally model how news surprises explain (co)jumps.
In the context of an international portfolio diversification problem, we find that small capitalization equity portfolios become riskier in bear markets, i.e. display negative co-skewness with other stock indices and high co-kurtosis. Because of this feature, a power utility investor ought to hold a well-diversified portfolio, despite the high risk premium and Sharpe ratios offered by small capitalization stocks.
In this paper we propose a contemporaneous threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex ante probabilities that latent regime-specific variables exceed certain threshold values.