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on Forecasting |
| By: | Anthony S. Tay (Singapore Management University) |
| Abstract: | We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We discover that adding low frequency stock returns (up to annual returns, depending on forecast horizon) to a quarterly AR(1) model improves forecasts of output growth. |
| Keywords: | Forecasting, Mixed Frequencies, Functional linear regression |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:eab:develo:1569&r=for |
| By: | Celia M. Reyes; Sonny N. Domingo; Christian D. Mina; Kathrina G. Gonzales (Philippine Institute for Development Studies) |
| Abstract: | Seasonal Climate Forecast (SCF) is one of the tools, which could help farmers and decision makers better prepare for seasonal variability. Using probabilistic principles in projecting climatic deviations, SCF allows farmers to make informed decisions on the proper choice of crop, cropping schedule, levels of input and use of mitigating measures. However, a cloud of uncertainty looms over the true value of SCF to its target users. To shed light on the true value of SCF in local agricultural decision making and operations, farm and household level survey was conducted. A total of 85 corn farmers from the plains and highlands of Echague and Angadanan, Isabela were interviewed. Results showed that climate and climate-related information were undoubtedly among the major factors being considered by farmers in their crop production activities. All aspects explored on the psychology of corn growers pointed to the high level of importance given to climatic conditions and SCF use. This was evident on the farmers’ perceptions, attitudes, and decision-making processes. Though the high regard of farmers on climate forecast and information cannot be questioned, actual application of such information seemed still wanting. Most corn farmers still started the season by “feel�— relying on the coming of rains and usual seasonal cropping schedules when commencing key farm operations. Reliable indigenous knowledge on climate forecasting was scarce. With corn farmers in Isabela still thirsting for climate-related information, the delivery of appropriate information and accurate forecasts should be addressed through proper extension and provision of support. Overall, SCF still has to solidify its role in the decision making process. Reliable SCFs remain the key to answer the riddle of seasonal variability and allow farmers to securely harness the goodness of the changing seasons. Ultimately, a holistic approach is necessary to truly elevate the productivity in Isabela’s corn lands. |
| Keywords: | Seasonal climate forecast, corn productivity, Isabela corn industry, climate variability, climate information and corn farming |
| JEL: | Q12 Q10 Q54 |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:eab:develo:1802&r=for |
| By: | Claudiu Tiberiu Albulescu (CRIEF, University of Poitiers) |
| Abstract: | The aim of this paper is to develop an aggregate stability index for the Romanian financial system, which is meant to enhance the set of analysis used by authorities to assess the financial system stability. The index takes into consideration indicators related to financial system development, vulnerability, soundness and also indicators which characterise the international economic climate. Another purpose of our study is to forecast the financial stability level, using a stochastic simulation model. The outcome of the study shows an improvement of the Romanian financial system stability during the period 1999-2007. The constructed aggregate index captures the financial turbulences periods like 1998-1999 Romanian banking crisis and 2007 subprime crisis. The forecasted values of the index show a deterioration of financial stability in 2009, influenced by the estimated decline of the financial and economic activity. |
| Keywords: | financial stability, aggregate financial stability index, forecasting systemic stability, stochastic simulation model |
| JEL: | C43 C51 C53 |
| Date: | 2009 |
| URL: | http://d.repec.org/n?u=RePEc:inf:wpaper:2009.4&r=for |
| By: | Tobias Adrian; Arturo Estrella; Hyun Song Shin |
| Abstract: | One of the most robust stylized facts in macroeconomics is the forecasting power of the term spread for future real activity. The economic rationale for this forecasting power usually appeals to expectations of future interest rates, which affect the slope of the term structure. In this paper, we propose a possible causal mechanism for the forecasting power of the term spread, deriving from the balance sheet management of financial intermediaries. When monetary tightening is associated with a flattening of the term spread, it reduces net interest margin, which in turn makes lending less profitable, leading to a contraction in the supply of credit. We provide empirical support for this hypothesis, thereby linking monetary cycles, financial cycles, and the business cycle. |
| Keywords: | Monetary policy ; Intermediation (Finance) ; Interest rates ; Forecasting ; Business cycles |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:421&r=for |
| By: | Tobias Adrian; Emanuel Moench; Hyun Song Shin |
| Abstract: | Fluctuations in the aggregate balance sheets of financial intermediaries provide a window on the joint determination of asset prices and macroeconomic aggregates. We document that financial intermediary balance sheets contain strong predictive power for future excess returns on a broad set of equity, corporate, and Treasury bond portfolios. We also show that the same intermediary variables that predict excess returns forecast real economic activity and various measures of inflation. Our findings point to the importance of financing frictions in macroeconomic dynamics and provide quantitative guidance for preemptive macroprudential and monetary policies. |
| Keywords: | Macroeconomics ; Intermediation (Finance) ; Assets (Accounting) ; Forecasting |
| Date: | 2010 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fednsr:422&r=for |
| By: | Fahad Alturki; Svetlana Vtyurina |
| Abstract: | This paper attempts to explain short- and long-term dynamics of-and forecast-inflation in Tajikistan using the Vector Error Correction Model (VECM) and Autoregressive Moving Average Model (ARMA). By analyzing different transmission channels through the VECM, we were able to evaluate their relative dominance, magnitude, and speed of transition to the equilibrium price level, with the view of identifying those policy tools that will enhance the effectiveness of monetary policy. We found that excess supply of broad money is inflationary in both the short and long term. The dynamic analysis also demonstrates that the exchange rate and international inflation have a strong impact on local prices. Available monetary instruments, such as the refinancing rate, have proven to be ineffective. Therefore, the Tajik monetary authority could greatly benefit from enhancing its monetary instruments toolkit, including by developing the interest rate channel, to improve its monetary policy execution and to achieve stable inflationary conditions. |
| Date: | 2010–01–19 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:10/17&r=for |
| By: | Celia M. Reyes; Kathrina G. Gonzales; Canesio D. Predo; Rosalina G. de Guzman (Philippine Institute for Development Studies) |
| Abstract: | Rainfall variability greatly influences corn production. Thus, an accurate forecast is potentially of value to the farmers because it could help them decide whether to grow their corn now or to delay it for the next cropping opportunity. A decision tree analysis was applied in estimating the value of seasonal climate forecast (SCF) information for corn farmers in Isabela. The study aims to estimate the value of SCF to agricultural decision makers under climate uncertainty. Historical climatic data of Isabela from 1951 to 2006 from PAGASA and crop management practices of farmers were used in the Decision Support System for Agrotechnology Transfer (DSSAT) to test the potential impact of climate change on corn. The approach is developed for a more accurate SCF and to be able to simulate corn yields for wet and dry seasons under different climatic conditions -- El Niño (poor year), La Niña (good year) and Neutral (neutral year) conditions. In order for the forecast to have value, the “with forecast� scenario should lead to better decision making for farmers to eventually get increase production over the “without forecast� scenario. While SCF may potentially affect a number of decisions including crop management practices, fertilizer inputs, and variety selection, the focus of the study was on the effect of climate on corn production. Improving SCF will enhance rainfed corn farmers’ decisionmaking capacity to minimize losses brought about by variable climate conditions. |
| Keywords: | decision tree analysis, seasonal climate forecast (SCF), climate uncertainty, Decision Support System for Agrotechnology Transfer (DSSAT) |
| JEL: | Q54 Q12 Q10 |
| Date: | 2010–01 |
| URL: | http://d.repec.org/n?u=RePEc:eab:develo:1801&r=for |