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Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly. JEL Klassifikation: C10, C53, G1.

We use newly-available Indian panel data to estimate how the returns to planting-stage investments vary by rainfall realizations. We show that the forecasts significantly affect farmer investment decisions and that these responses account for a substantial fraction of the inter-annual variability in planting-stage investments, that the skill of the forecasts varies across areas of India, and that farmers respond more strongly to the forecast where there is more forecast skill and not at all when there is no skill. We show, using an IV strategy in which the Indian government forecast of monsoon rainfall serves as the main instrument, that the return to agricultural investment depends substantially on the conditions under which it is estimated. Using the full rainfall distribution and our profit function estimates, we find that Indian farmers on average under-invest, by a factor of three, when we compare actual levels of investments to the optimal investment level that maximizes expected profits. Farmers who use skilled forecasts have increased average profit levels but also have more variable profits compared with farmers without access to forecasts. Even modest improvements in forecast skill would substantially increase average profits.

This chapter will discuss real-time forecasting in a macroeconomic policy context. I will begin by talking about the Survey of Professional Forecasters (SPF), a survey of private-sector forecasters. Next, I will discuss research on real-time data analysis and its importance in forecasting. Finally, I will discuss real-time forecasting in the 1990s.

The behavior of the individual Spanish voter has come to be rather well-understood, thanks to a growing research literature. However, no models have appeared to explain, or to forecast, national election outcomes. The presence of this research gap contrasts sharply with the extensive election forecasting work done on other leading Western democracies. Here we fill this gap. The model, developed from core political economy theory, is parsimonious but statistically robust. Further, it promises considerable prediction accuracy of Spanish general election outcomes, six months before the contest actually occurs. After presenting the model, and carrying out extensive regression diagnostics, we offer an ex ante forecast of the 2012 general election. ; Fundação para a Ciência e a Tecnologia ...

Wage inequality in Chile has remained high for decades and it is currently at the center of the political agenda. Increasing education of workers is expected to contribute to reduce wage inequality. Based on historical trends of age, education, and returns to education, this paper attempts to forecast wage inequality. Despite an increase in average earnings due to higher levels of education of workers, high levels of wage inequality within age groups and within education groups produce that forecasted wage inequality remains high for the next 10-year period. The structure of the Chilean labor market appears to imply that there is a high level of underlying wage inequality. Nevertheless, the good news are that the labor market structure seems to prevent further deteriorations of wage inequality.

This paper presents an early warning system as a set of multi-period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data for the period 1972:1-2014:12. Pseudo-real-time forecasts are generated from: (a) sets of autoregressive and factor-augmented vector autoregressions (VARs), and (b) sets of autoregressive and factor-augmented quantile projections. Our key finding is that forecasts obtained with AR and factor-augmented VAR forecasts significantly underestimate tail risks, while quantile projections deliver fairly accurate forecasts and reliable early warning signals for tail real and financial risks up to a 1-year horizon.