In this issue we have: Forecasting Profitability; Monetary policy expectations; Estimation Errors and Prediction Errors; and Prediction of Macroeconomic Performance in Pacific Island Countries.

Forecasting Profitability
Date: 201308 By: Mark Rosenzweig (Economic Growth Center, Yale University)
christopher Udry (Economic Growth Center, Yale University)URL: http://d.repec.org/n?u=RePEc:egc:wpaper:1029&r=for We use newlyavailable Indian panel data to estimate how the returns to plantingstage 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 interannual variability in plantingstage 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 underinvest, 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. Keywords: agriculture, forecasting, investment JEL: D24 
Estimation of flexible fuzzy GARCH models for conditional density estimation
Date: 20130731 By: Almeida, R.J.
Basturk, N.
Kaymak, U.
Costa Sousa, J.M. daURL: http://d.repec.org/n?u=RePEc:dgr:eureri:1765040785&r=for In this work we introduce a new flexible fuzzy GARCH model for conditional density estimation. The model combines two different types of uncertainty, namely fuzziness or linguistic vagueness, and probabilistic uncertainty. The probabilistic uncertainty is modeled through a GARCH model while the fuzziness or linguistic vagueness is present in the antecedent and combination of the rule base system. The fuzzy GARCH model under study allows for a linguistic interpretation of the gradual changes in the output density, providing a simple understanding of the process. Such a system can capture different properties of data, such as fat tails, skewness and multimodality in one single model. This type of models can be useful in many fields such as macroeconomic analysis, quantitative finance and risk management. The relation to existing similar models is discussed, while the properties, interpretation and estimation of the proposed model are provided. The model performance is illustrated in simulated time series data exhibiting complex behavior and a real data application of volatility forecasting for the S&P 500 daily returns series. Keywords: Linguistic descriptions; Volatility forecasting;Conditional density estimation;Fuzzy GARCH models 
Monetary policy expectations at the zero lower bound
Date: 2013 By: Michael D. Bauer
Glenn D. RudebuschURL: http://d.repec.org/n?u=RePEc:fip:fedfwp:201318&r=for Obtaining monetary policy expectations from the yield curve is difficult near the zero lower bound (ZLB). Standard dynamic term structure models, which ignore the ZLB, can be misleading. Shadowrate models are better suited for this purpose, because they account for the distributional asymmetry in projected short rates induced by the ZLB. Besides providing better interest rate fit and forecasts, our shadowrate models deliver estimates of the future monetary policy liftoff from the ZLB that are closer to survey expectations. We also document significant improvements for inference about monetary policy expectations when macroeconomic factors are included in the term structure model. Keywords: Monetary policy ; Macroeconomics – Econometric models 
Estimation Errors in InputOutput Tables and Prediction Errors in Computable General Equilibrium Analysis
Date: 201308 By: Nobuhiro Hosoe (National Graduate Institute for Policy Studies) URL: http://d.repec.org/n?u=RePEc:ngi:dpaper:1316&r=for We used 199520002005 linked inputoutput (IO) tables for Japan to examine estimation errors of updated IO tables and the resulting prediction errors in computable general equilibrium (CGE) analysis developed with updated IO tables. As we usually have no true IO tables for the target year and therefore need to estimate them, we cannot evaluate estimation errors of updated IO tables without comparing the updated ones with true ones. However, using the linked IO tables covering three different years enables us to make this comparison. Our experiments showed that IO tables estimated with more detailed and recent data contained smaller estimation errors and led to smaller quantitative prediction errors in CGE analysis. Despite the quantitative prediction errors, prediction was found to be qualitatively correct. As for the performance of updating techniques of IO tables, a crossentropy method often outperformed a leastsquares method in IO estimation with only aggregate data for the target year but did not necessarily outperform the leastsquares method in CGE prediction. 
Does Tourism Predict Macroeconomic Performance in Pacific Island Countries?
By: Paresh Kumar Narayan (Deakin University)
Susan S Sharma (Deakin University)
Deepa Bannigidadmath (Deakin University)URL: http://d.repec.org/n?u=RePEc:dkn:ecomet:fe_2013_03&r=for In this paper we examine whether tourism predicts macroeconomic variables in Pacific Island countries (PICs), namely, Fiji, the Solomon Islands, PNG, Vanuatu, Samoa, and Tonga. We form seven panels of PICs—one full panel of six countries and six panels where, onebyone, each country is excluded from the panel. We apply the Westerlund and Narayan (2012a) panel regression framework, where the null hypothesis is that visitor arrivals do not predict macroeconomic variables, which we proxy with 11 indicators, for panels of countries. We find that visitor arrivals consistently predict exports and money supply, and to a lesser extent, exchange rates and GDP. Keywords: Tourism; Macroeconomic variables; GDP; Money Supply; Panel Data; Predictive Regression Model.
Taken from the NEPFOR mailing list edited by Rob Hyndman.