nep-for New Economics Papers
on Forecasting
Issue of 2016‒06‒04
seven papers chosen by
Rob J Hyndman
Monash University

  1. Robustness in Foreign Exchange Rate Forecasting Models: Economics-Based Modelling After the Financial Crisis By Carlos Medel; Gilmour Camilleri; Hsiang-Ling Hsu; Stefan Kania; Miltiadis Touloumtzoglou
  2. Applying a Microfounded-Forecasting Approach to Predict Brazilian Inflation By Wagner Piazza Gaglianone; João Victor Issler; Silvia Maria Matos
  3. Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index By Dhanya Jothimani; Ravi Shankar; Surendra S. Yadav
  4. Bayesian Forecasting using Stochastic Search Variable Selection in a VAR Subject to Breaks By Gary Koop; Markus Jochmann; Rodney W. Strachan
  5. Empowering cash managers to achieve cost savings by improving predictive accuracy By Francisco Salas-Molina; Francisco J. Martin; Juan A. Rodr\'iguez-Aguilar; Joan Serr\`a; Josep Ll. Arcos
  6. Estimating Site-Specific Crop Yield Response using Varying Coefficient Models By Li, Xiaofei; Coble, Keith H.; Tack, Jesse B.; Barnett, Barry J.
  7. Detecting the Sources of Information Rigidity: Analyzing Forecast Bias and Smoothing in USDA’s Soybean Forecasts By MacDonald, Stephen; Ash, Mark

  1. By: Carlos Medel; Gilmour Camilleri; Hsiang-Ling Hsu; Stefan Kania; Miltiadis Touloumtzoglou
    Abstract: Exchange rates (FX) typically measures structural misalignments anticipating future short-run dynamics of key macroeconomic variables aiming to correct those misalignments with or without external intervention. The aim of this article is to analyse the out-of-sample behaviour of a bunch of statistical and economics-based models when forecasting FX for the UK, Japan, and the Euro Zone in relation to the US, emphasising the commodity prices boom of 2007-8 and the financial crisis of 2008-9. We analyse the forecasting behaviour of six economic plus three statistical models when forecasting from one up to 60-steps-ahead, comprising from 1981.1 to 2014.6. Our six economicsbased models can be classified in three groups: interest rate spreads, monetary fundamentals, and purchasing power parity with global measures, covering a wide range of macroeconomic indicators. Our results indicate that there are changes of the best models when considering different time spans. In particular, interest-rate-based models tend to be better at predicting before 2008, also showing a better tracking when crisis hit. However, when considering until 2014, the models based on price differentials are more promising, but subject to heterogeneity across countries. These results are important since shed some light on what model specification use and combine when forecast facing different FX volatility.
    Date: 2016–05
  2. By: Wagner Piazza Gaglianone; João Victor Issler; Silvia Maria Matos
    Abstract: In this paper, we investigate whether combining forecasts from surveys of expectations is a helpful strategy for forecasting inflation in Brazil. We employ the FGV-IBRE Economic Tendency Survey, which consists of monthly qualitative information from approximately 2,000 consumers since 2006, and the Focus Survey of the Central Bank of Brazil, with daily forecasts since 1999 from roughly 250 registered professional forecasters. Natural candidates to win a forecast competition in the literature of surveys of expectations are the (consensus) cross-sectional average forecasts (AF). In an exploratory investigation, we first show that these forecasts are a bias ridden version of the conditional expectation of inflation. The no-bias tests are conducted for the intercept and slope using the methods in Issler and Lima (2009) and Gaglianone and Issler (2015). The results reveal interesting data features: consumers systematically overpredict inflation (by 2.01 p.p., on average), whereas market agents underpredict it (by -0.68 p.p. over the same sample). Next, we employ a pseudo out-of-sample analysis to evaluate different forecasting methods: the AR(1) model, the Granger and Ramanathan (1984) forecast combination (GR), the consensus forecast (AF), the Bias-Corrected Average Forecast (BCAF), and the extended BCAF. Results reveal that: (i) the MSE of the AR(1) model is higher compared to the GR (and usually lower compared to the AF); and (ii) the extended BCAF is more accurate than the BCAF, which, in turn, dominates the AF. This validates the view that the bias corrections are a useful device for forecasting using surveys
    Date: 2016–05
  3. By: Dhanya Jothimani; Ravi Shankar; Surendra S. Yadav
    Abstract: Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models. The paper proposes a hybrid approach integrating the advantages of both decomposition model (namely, Maximal Overlap Discrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR) to predict the National Stock Exchange Fifty Index. In first phase, the data is decomposed into a smaller number of subseries using MODWT. In next phase, each subseries is predicted using machine learning models (i.e., ANN and SVR). The predicted subseries are aggregated to obtain the final forecasts. In final stage, the effectiveness of the proposed approach is evaluated using error measures and statistical test. The proposed methods (MODWT-ANN and MODWT-SVR) are compared with ANN and SVR models and, it was observed that the return on investment obtained based on trading rules using predicted values of MODWT-SVR model was higher than that of Buy-and-hold strategy.
    Date: 2016–05
  4. By: Gary Koop (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy); Markus Jochmann (University of Strathclyde, Glasgow, UK and The Rimini Centre for Economic Analysis, Italy); Rodney W. Strachan (University of Queensland, UK and The Rimini Centre for Economic Analysis, Italy)
    Abstract: This paper builds a model which has two extensions over a standard VAR. The first of these is stochastic search variable selection, which is an automatic model selection device which allows for coefficients in a possibly over-parameterized VAR to be set to zero. The second allows for an unknown number of structural breaks in the VAR parameters. We investigate the in-sample and forecasting performance of our model in an application involving a commonly-used US macro-economic data set. We find that, in-sample, these extensions clearly are warranted. In a recursive forecasting exercise, we find moderate improvements over a standard VAR, although most of these improvements are due to the use of stochastic search variable selection rather than the inclusion of breaks. Creation-Date: 200801
  5. By: Francisco Salas-Molina; Francisco J. Martin; Juan A. Rodr\'iguez-Aguilar; Joan Serr\`a; Josep Ll. Arcos
    Abstract: Cash management is concerned with optimizing the short-term funding requirements of a company. To this end, different optimization strategies have been proposed to minimize costs using daily cash flow forecasts as the main input to the models. However, the effect of the accuracy of such forecasts on cash management policies has not been studied. In this article, using two real data sets from the textile industry, we show that predictive accuracy is highly correlated with cost savings when using daily forecasts in cash management models. A new method is proposed to help cash managers estimate if efforts in improving predictive accuracy are proportionally rewarded by cost savings. Our results imply the need for an analysis of potential cost savings derived from improving predictive accuracy. From that, the search for better forecasting models is in place to improve cash management.
    Date: 2016–05
  6. By: Li, Xiaofei; Coble, Keith H.; Tack, Jesse B.; Barnett, Barry J.
    Abstract: This study estimates the site-specific crop yield response function using varying coefficient models. It is widely recognized that the parameters of yield response function vary dramatically across space and over time. Previous studies usually capture this variability of response by using locational and time dummy variables. While that approach reveals the existence of the response variability, the exact pattern of the variability is unknown, and the capacity of ex ante prediction of such models are limited. This study takes a step forward to explicitly explain how the response varies with the actual site characteristic variables, such as soil, water, topography, weather, and other factors that are commonly available to producers. By using the varying coefficient model, the parameters of the response function are specified to change continuously with those site variables. Based on a simulation data set, the varying coefficient model is demonstrate to outperform the site-dummy model by creating better variable rate application (VRA) fertilizer prescriptions. We further propose to apply the model to large sample of high resolution production data, and create ex ante spatially explicit optimal VRA fertilizer recommendations. The ultimate goal is to develop a precision decision system which can statistically turn the soil testing and weather forecasting information into input application prescriptions for producers.
    Keywords: Site-specific crop response, varying coefficient regression, ex ante prediction, precision agriculture, big data, Crop Production/Industries, Farm Management, Production Economics, Productivity Analysis,
    Date: 2016
  7. By: MacDonald, Stephen; Ash, Mark
    Abstract: USDA’s U.S. soybean ending stock forecasts are upwardly biased. To determine the source of this bias, we examine the revision characteristics of the ending stocks forecasts, and examine USDA’s forecasts of other U.S. soybean balance sheet variables and foreign soybean balance sheet variables. Bias in USDA’s soybean export forecasts is the most likely source of ending stock forecast bias. In turn, bias in the U.S. export forecasts has diverse sources, including bias in foreign trade estimates and late in the forecast cycle slow updating of the forecasts to reflect new information.
    Keywords: USDA, forecasting, forecast evaluation, revision efficiency, Agribusiness, Crop Production/Industries, Demand and Price Analysis, International Relations/Trade, Research Methods/ Statistical Methods, C53, F17, Q11, Q17,
    Date: 2016

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