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This paper analyzes recent changes in the relative importance of the determinants of capital flows to emerging market economies. For this purpose, we estimate vector autoregressive (VAR) models for the period 2009-2020. Based on these models, we estimate the effects on debt flows from shocks to their determinants. Then, we quantify the contribution of each of the variables included in the model to explain the evolution of these flows in each month of the sample through a historical decomposition analysis. The main results indicate that the contribution of global risk aversion to explain the evolution of debt flows increased during March 2020 compared to the past, although its relative importance has decreased since, particularly as the performance of financial markets improved.

This paper analyzes the relationship between central bank independence and inflation in a panel of 182 countries for the period from 1970 to 2018. To measure the degree of independence, two measures are used, the Garriga (2016) index, constructed from the laws and internal regulations of central banks, and the Dreher et al. (2008) index, based on the turnover rate of governors. The results indicate that greater central bank independence is associated with lower levels of inflation, both for highincome countries and for low and middle-income countries. There is also a negative relationship between inflation volatility and central bank independence, although the results are statistically significant only when using the full sample of countries. The results are robust to the use of the two alternative measures of Independence and to the use of two alternative approaches to avoid simultaneity.

In this paper we analyze the impact and persistence of shocks to global (push) and domestic (pull) factors on each component of the financial account for the Mexican Balance of Payments at the highest degree of disaggregation, including investment by foreign residents in Mexican public and private sector securities, as well as investment by domestic residents in foreign securities. To this end, we estimate impulse response functions from vector autoregressive models for the period 1995-2015. We find that an increase in the foreign interest rate leads to lower portfolio investment, particularly in Mexican public sector securities. An increase in global risk generates lower portfolio investment, particularly in private sector securities. Foreign investors respond to a higher extent to foreign interest rate and liquidity shocks compared to domestic investors.

This article evaluates the use of financial data sampled at high frequencies to improve short-term forecasts of quarterly GDP for Mexico. In particular, the mixed data sampling (MIDAS) regression model is employed to incorporate both quarterly and daily frequencies while remaining parsimonious. To preserve parsimony, factor analysis and forecast combination techniques are used to summarize the information contained in a dataset containing 392 daily financial series. Our findings suggest that the MIDAS model that incorporates daily financial data lead to improvements for quarterly forecasts of GDP growth over traditional models that either rely only on quarterly macroeconomic data or average daily financial data. Furthermore, we explore the ability of the MIDAS model to provide forecast updates for GDP growth (nowcasting).

This paper examines the level of interdependence and volatility transmission in global agricultural futures markets. We follow a multivariate GARCH approach to explore the dynamics and cross-dynamics of volatility across major exchanges of corn, wheat, and soybeans between the United States, Europe, and Asia. We account for the potential bias that may arise when considering exchanges with different closing times. The results indicate that agricultural markets are highly interrelated and there are both own- and cross-volatility spillovers and dependence among most of the exchanges. The results also show the major role Chicago plays in terms of spillover effects over the other markets, particularly for corn and wheat. Additionally, the level of interdependence between exchanges has only increased in recent years for some of the commodities.

Financial advisors typically recommend that a long-term investor should hold a higher percentage of his wealth in stocks than a short-term investor. However, part of the academic literature disagrees with this advice. We use a spatial dominance test which is suited for comparing alternative investments when their distributions are time-varying. Using daily data for the US from 1965 to 2008, we test for dominance of cumulative returns series for stocks versus bonds at different investment horizons from one to ten years. We find that bonds second order spatially dominate stocks for one and two year horizons. For horizons of nine years or longer, we find evidence that stocks dominate bonds. When different portfolios of stocks and bonds are compared, we find that for long investment horizons, only those portfolios with a suffciently high proportion of stocks are effcient in the sense of spatial dominance.

This paper analyzes the pass-through of exchange rate to different price indexes in Mexico. The analysis is based on a vector autoregressive model (VAR) using monthly data from January 1997 to December 2010. The pass-through effects are calculated by means of accumulated impulse response functions to a recursively identified exchange rate shock. The results show that the exchange rate pass-through to import prices is complete, but it declines along the distribution chain in such a way that the impact on consumer prices is below 20 percent. Moreover, we find that the exchange rate pass-through seems to have decreased substantially from 2001 onwards, which coincides with the adoption of an inflation targeting regime by Banco de Mexico.

In this paper we apply a dynamic factor model to generate out of sample forecasts for the inflation rate in Mexico. We evaluate the role of using a wide range of macroeconomic variables with particular interest on the importance of using CPI disaggregated data to forecast inflation. Our data set contains 54 macroeconomic series and 243 CPI subcomponents from 1988 to 2008. Our results indicate that: i) Factor models outperform the benchmark autoregressive model at horizons of one, two, four and six quarters, ii) Using disaggregated price data improves forecasting performance, and iii) The factors are related to key variables in the economy such as output growth and inflation.