Macroeconomic forecasting and structural change

The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.     

STUDYING INEQUALITY IN INCOME DISTRIBUTION OF SINGLE-PERSON HOUSEHOLDS IN FOUR DEVELOPED COUNTRIES

The increasing frequency of single-person households has become a major economic phenomenon, and is likely to become an important political force. This paper focuses on differences related to inequality of income distribution among single-person households in Europe's four largest economies, i.e. France, Germany, Italy and the U.K. Income distribution was modeled in terms of individual characteristics using a parametric model with heterogeneous model parameters. Poverty differences were also broken down using the results of Biewen and Jenkins (2005 ) in order to understand the relationship between poverty and individual characteristics among countries.     

The measurement of rank mobility

In this paper we investigate the problem of measuring social mobility when the social status of individuals is given by their rank. In order to sensibly represent the rank mobility of subgroups within a given society, we address the problem in terms of partial permutation matrices which include standard (“global”) matrices as a special case. We first provide a characterization of a partial ordering on partial matrices which, in the standard case of global matrices, coincides with the well-known “concordance” ordering. We then provide a characterization of an index of rank mobility based on partial matrices and show that, in the standard case of comparing global matrices, it is equivalent to Spearman's ρ index.     

Combining Time Variation and Mixed Frequencies: an Analysis of Government Spending Multipliers in Italy

In this paper, we propose a time‐varying parameter vector autoregression (VAR) model with stochastic volatility which allows for estimation on data sampled at different frequencies. Our contribution is twofold. First, we extend the methodology developed by Cogley and Sargent (Drifts and volatilities: monetary policies and outcomes in the post WWII U.S. Review of Economic Studies 2005; 8 : 262–302) and Primiceri (Time varying structural vector autoregressions and monetary policy. Review of Economic Studies 2005; 72 : 821–852) to a mixed‐frequency setting. In particular, our approach allows for the inclusion of two different categories of variables (high‐frequency and low‐frequency) into the same time‐varying model. Second, we use this model to study the macroeconomic effects of government spending shocks in Italy over the 1988:Q4–2013:Q3 period. Italy—as well as most other euro area economies—is characterized by short quarterly time series for fiscal variables, whereas annual data are generally available for a longer sample before 1999. Our results show that the proposed time‐varying mixed‐frequency model improves on the performance of a simple linear interpolation model in generating the true path of the missing observations. Second, our empirical analysis suggests that government spending shocks tend to have positive effects on output in Italy. The fiscal multiplier, which is maximized at the 1‐year horizon, follows a U‐shape over the sample considered: it peaks at around 1.5 at the beginning of the sample; it then stabilizes between 0.8 and 0.9 from the mid 1990s to the late 2000s, before rising again to above unity during the recent crisis. Copyright © 2015 John Wiley & Sons, Ltd.     

Recent weather fluctuations and agricultural yields: implications for climate change

We summarize recent statistical analyses that link agricultural yields to weather fluctuations. Similar to other sectors, high temperatures play a crucial role in predicting outcomes. Climate change is predicted to significantly increase high temperatures and thereby reduce yields. How good are such models at predicting future outcomes? We show that a statistical model estimated using historic US data on corn and soybean yields from 1950 to 2011 is very capable of predicting aggregate US yields for the years 2012–2015, where 2012 was much hotter than normal and is expected to become the new normal under climate change. We conclude by discussing recent studies on the implication of predicted yield declines with a special focus on adaptation and commodity prices.