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Cover -- Title Page -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgments -- 1 Qualitative Overview -- 1.1 Introduction -- 1.2 Forecasting Mortality -- 1.2.1 The Data -- 1.2.2 The Patterns -- 1.2.3 Scientific versus Optimistic Forecasting Goals -- 1.3 Statistical Modeling -- 1.4 Implications for the Bayesian Modeling Literature -- 1.5 Incorporating Area Studies in Cross-National Comparative Research -- 1.6 Summary -- Part I: Existing Methods for Forecasting Mortality -- 2 Methods without Covariates -- 2.1 Patterns in Mortality Age Profiles -- 2.2 A United Statistical Framework -- 2.3 Population Extrapolation Approaches -- 2.4 Parametric Approaches -- 2.5 A Nonparametric Approach: Principal Components -- 2.5.1 Introduction -- 2.5.2 Estimation -- 2.6 The Lee-Carter Approach -- 2.6.1 The Model -- 2.6.2 Estimation -- 2.6.3 Forecasting -- 2.6.4 Properties -- 2.7 Summary -- 3 Methods with Covariates -- 3.1 Equation-by-Equation Maximum Likelihood -- 3.1.1 Poisson Regression -- 3.1.2 Least Squares -- 3.1.3 Computing Forecasts -- 3.1.4 Summary Evaluation -- 3.2 Time-Series, Cross-Sectional Pooling -- 3.2.1 The Model -- 3.2.2 Postestimation Intercept Correction -- 3.2.3 Summary Evaluation -- 3.3 Partially Pooling Cross Sections via Disturbance Correlations -- 3.4 Cause-Specific Methods with Microlevel Information -- 3.4.1 Direct Decomposition Methods Modeling -- 3.4.2 Microsimulation Methods -- 3.4.3 Interpretation -- 3.5 Summary -- Part II: Statistical Modeling -- 4 The Model -- 4.1 Overview -- 4.2 Priors on Coefficients -- 4.3 Problems with Priors on Coeffcients -- 4.3.1 Little Direct Prior Knowledge Exists about Coefficients -- 4.3.2 Normalization Factors Cannot Be Estimated -- 4.3.3 We Know about the Dependent Variable, Not the Coefficients -- 4.3.4 Difficulties with Incomparable Covariates

An updated new edition of the comprehensive guide to better business forecasting Many companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. Demand-Driven Forecasting equips you with solutions that can sense, shape, and predict future demand using highly sophisticated methods and tools. From a review of the most basic forecasting methods to the most a

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.