Global merchandise trade expanded rapidly over the last 6½ decades and its relationship
with global income has seen ebbs and flows. This paper examines the shifts in this
relationship using time series data over 1950-2014 and situates it in the current and
longer term context. The conjunctural context comes from, among other things, the “great
trade collapse” (GTC) and the global financial crisis (GFC) in 2009, and developments
since then. The longer term context comes from the relative role of “globalization” and
“technology” shocks in accounting for the short and long run variance of global exports
and income. The paper estimates trade and income elasticities using ADL models taking
account of structural breaks, and impulse response functions from structural VARs. The
estimated SVAR model provides a lens to ask whether global trade and income are in a
“new normal’ or only “back to (an old) normal” after the GTC and GFC.
This paper reviews recent advances in the specification and estimation of Bayesian Vector Autoregressive models (BVARs). After describing the Bayesian principle of estimation, we first present the methodology originally developed by Litterman (1986) and Doan et al. (1984) and review alternative priors. We then discuss extensions of the basic model and address issues in forecasting and structural analysis. An application to the estimation of a system of time-varying reaction functions for four European central banks under the European Monetary System (EMS) illustrates how some of the results previously presented may be applied in practice.
Mick Silver, Brian Graf, and Mr. Kimberly D. Zieschang
Transaction-price residential (house) and commercial property price indexes (RPPIs and CPPIs) have inherent problems of sparse data on heterogeneous properties, more so CPPIs. In an attempt to control for heterogeneity, (repeat-sales and hedonic) panel data regression frameworks are typically used for estimating overall price change. We address the problem of sparse data, demonstrate the need to include spatial price spillovers to remove bias, and propose an innovative approach to effectively weight regional CPPIs along with improvements to higher-level weighting systems. The study uses spatial panel regressions on granular CPPIs for the United States (US).
Carlos Janada, Iulia Ruxandra Teodoru, and Ms. Inci Otker
This paper argues that structural weaknesses may make private investment particularly sensitive to business confidence relative to other traditional investment drivers and global shocks. It gauges the importance of confidence over recent years in selected countries in Central America, including Costa Rica, the Dominican Republic, El Salvador, and Guatemala. Using a vector error correction model to carry out the empirical work, a system representing global activity and the domestic economy, including a set of investment drivers (interest rates, unit labor costs, and confidence) is analyzed. The findings suggest that confidence has been, on average, the most important driver of investment in these countries, exceeded only by global factors. Since confidence, arguably, can be influenced by policymakers’ decisions, structural reforms to improve the business climate and reduce uncertainty play an important role in promoting investment and economic growth.
This paper investigates whether Indonesia’s recent currency crisis was due to domestic fundamentals, common external shocks (“monsoons”), or contagion from neighboring countries. Markov-switching models attribute speculative pressure on Indonesia’s currency to domestic political and financial factors and contagion from speculative pressures in Thailand and Korea. In particular, the results from a time-varying transition probability Markov-switching model (which overcomes some drawbacks of previous methods) show that inclusion of exchange rate pressures from Thailand and Korea in the transition probabilities improves the conditional probabilities of crisis in Indonesia. There is also evidence of contagion in the stock market.
Concepcion Verdugo-Yepes, Mr. Peter L. Pedroni, Xingwei Hu, and Mr. Ross B Leckow
This paper studies the transmission of crime shocks to the economy in a sample of 32 Mexican states over the period from 1993 to 2012. The paper uses a panel structural VAR approach which accounts for the heterogeneity of the dynamic state level responses in GDP, FDI and international migration flows, and measures the transmission via the impulse response of homicide rates. The approach also allows the study of the pattern of economic responses among states. In particular, the percentage of GDP devoted to new construction and the perception of public security are characteristics that are shown to be associated with the sign and magnitude of the responses of economic variables to crime shocks.
Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved factors. Similarly, the dynamics in DSGE models are often governed by a handful of state variables and exogenous processes such as preference and/or technology shocks. Boivin and Giannoni(2006) combine a DSGE and a factor model into a data-rich DSGE model, in which DSGE states are factors and factor dynamics are subject to DSGE model implied restrictions. We compare a data-richDSGE model with a standard New Keynesian core to an empirical dynamic factor model by estimating both on a rich panel of U.S. macroeconomic and financial data compiled by Stock and Watson (2008).We find that the spaces spanned by the empirical factors and by the data-rich DSGE model states are very close. This proximity allows us to propagate monetary policy and technology innovations in an otherwise non-structural dynamic factor model to obtain predictions for many more series than just a handful of traditional macro variables, including measures of real activity, price indices, labor market indicators, interest rate spreads, money and credit stocks, and exchange rates.
This paper reexamines the empirical relationship between financial development and economic growth. It presents evidence based on cross-section and panel data using an updated dataset, a variety of econometric methods, and two standard measures of financial development: the level of liquid liabilities of the banking system and the amount of credit issued to the private sector by banks and other financial institutions. The paper identifies two sets of findings. First, in contrast with the recent evidence of Levine, Loayza, and Beck (2001), cross-section and panel-data-instrumental-variables regressions reveal that the relationship between financial development and economic growth is, at best, weak. Second, there is evidence of nonlinearities in the data, suggesting that finance matters for growth only at intermediate levels of financial development. Moreover, using a procedure appropriately designed to estimate long-run relationships in a panel with heterogeneous slope coefficients, there is no clear indication that finance spurs economic growth. Instead, for some specifications, the relationship is, puzzlingly, negative.
We apply a hidden Markov model of the term structure to modeling the Brazilian swap rate curve. We examine the model's characteristics and its performance in describing the cross-sectional and time-series dynamics of the term structure. Two regimes are identified, a high level and a high volatility regime and a low level and low volatility regime. Both regimes are persistent and are explained by the level and the slope of the term structure. The model is estimated using a Bayesian MCM algorithm that produces consistent standard errors and a reliable method for testing the differences between the model parameters.
This paper develops a simple methodology to test for asset integration, and applies it within and between American stock markets. Our technique relies on estimating and comparing expected risk-free rates across assets. Expected risk-free rates are allowed to vary freely over time, constrained only by the fact that they must be equal across (risk-adjusted) assets in well integrated markets. Assets are allowed to have standard risk characteristics, and are constrained by a factor model of covariances over short time periods. We find that implied expected risk-free rates vary dramatically over time, unlike short interest rates. Further, internal integration in the S&P 500 market is never rejected and is generally not rejected in the NASDAQ. Integration between the NASDAQ and the S&P, however, is always rejected dramatically.