nep-for New Economics Papers
on Forecasting
Issue of 2015‒02‒28
fourteen papers chosen by
Rob J Hyndman
Monash University

  1. Stochastic Population Analysis: A Functional Data Approach By Lei Fang ; Wolfgang K. Härdle ; ;
  2. Monitoring the world business cycle By Maximo Camacho ; Jaime Martinez Martin
  3. Energy forecasting: Past, present and future By Tao Hong
  4. An Infinite Hidden Markov Model for Short-term Interest Rates By John M. Maheu ; Qiao Yang
  5. Lessons for Forecasting Unemployment in the U.S.: Use Flow Rates, Mind the Trend By Meyer, Brent ; Tasci, Murat
  6. Modeling and Forecasting Realized Covariance Matrices with Accounting for Leverage By Stanislav Anatolyev ; Nikita Kobotaev
  7. Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand By Branislav Saxa
  8. Forecasting the intraday market price of money By Andrea Monticini ; Francesco Ravazzolo
  9. A multivariate model for the prediction of stock returns in an emerging market: A comparison of parametric and non-parametric models By Bonga-Bonga, Lumengo ; Mwamba, Muteba
  10. Forecasting Generalized Quantiles of Electricity Demand: A Functional Data Approach By Brenda Lopez Cabrera ; Franziska Schulz ; ;
  11. A new approach to forecasting based on exponential smoothing with independent regressors By Ahmad Farid Osman ; Maxwell L. King
  12. Fuzzy interaction regression for short term load forecasting By Tao Hong ; Pu Wang
  13. 13 lucky tips to juggle the analytics of forecasting By Tao Hong
  14. Medium-term forecasting of euro-area macroeconomic variables with DSGE and BVARX models By Lorenzo Burlon ; Simone Emiliozzi ; Alessandro Notarpietro ; Massimiliano Pisani

  1. By: Lei Fang ; Wolfgang K. Härdle ; ;
    Abstract: Based on the Lee-Carter (LC) model, the benchmark in population forecasting, a variety of extensions and modifications are proposed in this paper. We investigate one of the extensions, the Hyndman-Ullah (HU) method and apply it to Asian demographic data sets: China, Japan and Taiwan. It combines ideas of functional principal component analysis (fPCA), nonparametric smoothing and time series analysis. Based on this stochastic approach, the demographic characteristics and trends in different Asian regions are calculated and compared. We illustrate that China and Japan exhibited a similar demographic trend in the past decade. We also compared the HU method with the LC model. The HU method can explain more variation of the demographic dynamics when we have data of high quality, however, it also encounters problems and performs similarly as the LC model when we deal with limited and scarce data sets, such as Chinese data sets due to the substandard quality of the data and the population policy.
    Keywords: Functional principal component analysis; Nonparametric smoothing; Mortality forecasting; Fertility forecasting; Asian demography; Lee-Carter model, Hyndman-Ullah method
    JEL: C14 C32 C38 J11 J13
    Date: 2015–02
  2. By: Maximo Camacho ; Jaime Martinez Martin
    Abstract: We propose a Markov-switching dynamic factor model to construct an index of global business cycle conditions, to perform short-term forecasts of world GDP quarterly growth in real time and to compute realtime business cycle probabilities. To overcome the real-time forecasting challenges, the model accounts for mixed frequencies, for asynchronous data publication and for leading indicators. Our pseudo real time results show that this approach provides reliable and timely inferences of the world quarterly growth and of the world state of the business cycle on a monthly basis.
    Keywords: Economic Analysis, Global, Research, Working Paper
    JEL: E32 C22 E27
    Date: 2015–02
  3. By: Tao Hong
    Abstract: When turning on the switch, people expect the light would be on. However, the business to keep the lights on is not that straightforward. This paper offers a practical overview of energy forecasting, an important task that electric utilities have been doing every day for over a century.
    Keywords: Energy forecasting; Electricity price forecasting; Load forecasting; Smart grid
    JEL: Q41 Q47
    Date: 2013–12–31
  4. By: John M. Maheu (DeGroote School of Business, McMaster University and University of Toronto, Canada; The Rimini Centre for Economic Analysis, Italy ); Qiao Yang (Department of Economics, University of Toronto, Canada )
    Abstract: The time-series dynamics of short-term interest rates are important as they are a key input into pricing models of the term structure of interest rates. In this paper we extend popular discrete time short-rate models to include Markov switching of infinite dimension. This is a Bayesian nonparametric model that allows for changes in the unknown conditional distribution over time. Applied to weekly U.S. data we find significant parameter change over time and strong evidence of non-Gaussian conditional distributions. Our new model with an hierarchical prior provides significant improvements in density forecasts as well as point forecasts. We find evidence of recurring regimes as well as structural breaks in the empirical application.
    Date: 2015–02
  5. By: Meyer, Brent (Federal Reserve Bank of Atlanta ); Tasci, Murat (Federal Reserve Bank of Cleveland )
    Abstract: This paper evaluates the ability of autoregressive models, professional forecasters, and models that leverage unemployment flows to forecast the unemployment rate. We pay particular attention to flows-based approaches—the more reduced form approach of Barnichon and Nekarda (2012) and the more structural method in Tasci (2012)—to generalize whether data on unemployment flows is useful in forecasting the unemployment rate. We find that any approach that leverages unemployment inflow and outflow rates performs well in the near term. Over longer forecast horizons, Tasci (2012) appears to be a useful framework, even though it was designed to be mainly a tool to uncover long-run labor market dynamics such as the “natural” rate. Its usefulness is amplified at specific points in the business cycle when unemployment rate is away from the longer-run natural rate. Judgmental forecasts from professional economists tend to be the single best predictor of future unemployment rates. However, combining those guesses with flows based approaches yields significant gains in forecasting accuracy.
    Keywords: Unemployment Forecasting; Natural Rate; Unemployment Flows; Labor Market Search
    JEL: C53 E24 E32 J64
    Date: 2015–02–13
  6. By: Stanislav Anatolyev (New Economic School ); Nikita Kobotaev (New Economic School )
    Abstract: The existing dynamic models for realized covariance matrices do not account for an asymmetry with respect to price directions. We modify the recently proposed conditional autoregressive Wishart (CAW) model to allow for the leverage effect. In the conditional threshold autoregressive Wishart (CTAW) model and its variations the parameters governing each asset's volatility and covolatility dynamics are subject to switches that depend on signs of previous asset returns or previous market returns. We evaluate the predictive ability of the CTAW model and its restricted and extended specifications from both statistical and economic points of view. We find strong evidence that many CTAW specifications have a better in-sample fit and tend to have a better out-of-sample predictive ability than the original CAW model and its modfications.
    Date: 2015–02
  7. By: Branislav Saxa
    Abstract: This paper examines the usefulness of Google Trends data for forecasting mortgage lending in the Czech Republic. While the official monthly statistics on mortgage lending come with a publication lag of one month, the data on how often people search for mortgage-related terms on the internet are available without any lag on a weekly basis. Growth in searches for mortgages and growth in mortgages actually provided are strongly correlated. The lag between these two growth rates is two months. Evaluation of out-of-sample forecasts shows that internet search data improve mortgage lending predictions significantly. In addition to forecasting performance evaluation, an experimental indicator of restrictively tight mortgage credit standards and conditions is proposed. Nowadays many countries run bank lending surveys to monitor the tightness of bank lending standards and conditions. The proposed indicator represents a complementary tool to such a survey.
    Keywords: Credit demand, credit standards and conditions, credit supply, forecast evaluation, forecasting, Google econometrics, Internet search data, mortgage, smoothing
    JEL: C22 C82 E27 E51
    Date: 2014–12
  8. By: Andrea Monticini (Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore ); Francesco Ravazzolo (Norges Bank and BI Norwegian Business School )
    Abstract: Central banks' operations and eciency arguments would suggest that the intraday interest rate should be set to zero. However, a liquidity crisis introduces frictions related to news, which can cause an upward jump of the intraday rate. This paper documents that these dynamics can be partially predicted during turbulent times. Long memory approaches or a combination of them to account for model uncertainty outperform random walk, autoregressive and moving average benchmarks in terms of point and density forecasting. The relative accuracy is higher when the full distribution is predicted. We also document that such statistical accuracy can provide economic gains in investment strategies based on lending in the intraday market.
    Keywords: interbank market, intraday interest rate, forecasting, density forecasting, policy tools.
    JEL: C22 C53 E4 E5
    Date: 2014–02
  9. By: Bonga-Bonga, Lumengo ; Mwamba, Muteba
    Abstract: This paper compares the forecasting performance of three structural econometric models, namely the non-parametric, ARIMAX and the Kalman filter models, in predicting stock returns in an emerging market economy using South Africa as case study. The proposed models have different functional forms. Each of the functional forms accounts for specific characteristics and properties of stock returns in general and in a small open economy in particular. The findings of the paper indicate the importance of the US stock returns in predicting stock returns in an emerging market economy. Moreover, the results of the Diebold-Mariano statistics show that the Kalman filter and ARIMAX model both outperform the non-parametric model indicating the dominant characteristics of nonlinearity and Markov properties of stock market returns in South Africa.
    Keywords: stock returns, emerging markets, ARIMAX, Kalman-filter, Non-parametric
    JEL: C14 C53 C58 G1 G17
    Date: 2015–02–09
  10. By: Brenda Lopez Cabrera ; Franziska Schulz ; ;
    Abstract: Electricity load forecasts are an integral part of many decision-making pro- cesses in the electricity market. However, most literature on electricity load forecasting concentrates on deterministic forecasts, neglecting possibly impor- tant information about uncertainty. A more complete picture of future demand can be obtained by using distributional forecasts, allowing for a more efficient decision-making. A predictive density can be fully characterized by tail mea- sures such as quantiles and expectiles. Furthermore, interest often lies in the accurate estimation of tail events rather than in the mean or median. We pro- pose a new methodology to obtain probabilistic forecasts of electricity load, that is based on functional data analysis of generalized quantile curves. The core of the methodology is dimension reduction based on functional principal components of tail curves with dependence structure. The approach has sev- eral advantages, such as flexible inclusion of explanatory variables including meteorological forecasts and no distributional assumptions. The methodol- ogy is applied to load data from a transmission system operator (TSO) and a balancing unit in Germany. Our forecast method is evaluated against other models including the TSO forecast model. It outperforms them in terms of mean absolute percentage error (MAPE) and achieves a MAPE of 2.7% for the TSO.
    Keywords: Electricity, Load forecasting, FPCA
    JEL: G19 G29 G22 Q14 Q49 Q59
    Date: 2014–05
  11. By: Ahmad Farid Osman ; Maxwell L. King
    Abstract: In There is evidence that exponential smoothing methods as well as time varying parameter models perform relatively well in forecasting comparisons. The aim of this paper is to introduce a new forecasting technique by integrating the exponential smoothing model with regressors whose coefficients are time varying. In doing this, we construct an exponential smoothing model with regressors by extending Holt's linear exponential smoothing model. We then translate it into an equivalent state space structure so that the parameters can be estimated via the maximum likely-hood estimation procedure. Due to the potential problem in the updating equation for the regressor coefficients when the change in regressor is too small, we propose an alternative structure of the state space model which allows the updating process to be put on hold until sufficient information is available. An empirical study of forecast accuracy shows that the new model performs better than the existing exponential smoothing model as well as the linear regression model.
    Keywords: State space model, Single source of error, Time varying parameter, Time series, Forecast accuracy
    JEL: C51 C53
    Date: 2015
  12. By: Tao Hong ; Pu Wang
    Abstract: Electric load forecasting is a fundamental business process and well-established analytical problem in the utility industry. Due to various characteristics of electricity demand series and the business needs, electric load forecasting is a classical textbook example and popular application field in the forecasting community. During the past 30 plus years, many statistical and artificial intelligence techniques have been applied to short term load forecasting (STLF) with varying degrees of success. Although fuzzy regression has been tried for STLF for about a decade, most research work is still focused at the theoretical level, leaving little value for practical applications. A primary reason is that inadequate attention has been paid to the improvement of the underlying linear model. This application-oriented paper proposes a fuzzy interaction regression approach to STLF. Through comparisons to three models (two fuzzy regression models and one multiple linear regression model) without interaction effects, the proposed approach shows superior performance over its counterparts. This paper also offers critical comments to a notable but questionable paper in this field. Finally, tips for practicing forecasting using fuzzy regression are discussed.
    Keywords: Load forecasting; Fuzzy regression; Interaction regression
    JEL: C22 C53 Q41 Q47
    Date: 2013–12–31
  13. By: Tao Hong
    Abstract: Energy forecasting is one of those areas of great importance to electric grid that gets little attention - even from power industry insiders. But you need to know how to make the best of your forecasting process. Here are 13 tips to get you started.
    Keywords: Energy forecasting; Forecast accuracy; Forecast combination; Load forecasting
    JEL: C53 Q41 Q47
    Date: 2014–10–30
  14. By: Lorenzo Burlon (Bank of Italy ); Simone Emiliozzi (Bank of Italy ); Alessandro Notarpietro (Bank of Italy ); Massimiliano Pisani (Bank of Italy )
    Abstract: The paper assesses the performance of medium-term forecasts of euro-area GDP and inflation obtained with a DSGE model and a BVARX model currently in use at the Bank of Italy. The performance is compared with that of simple univariate models and with the Eurosystem projections; the same real time assumptions underlying the latter are used to condition the DSGE and the BVARX forecasts. We find that the performance of both forecasts is similar to that of Eurosystem forecasts and overall more accurate than that of simple autoregressive models. The DSGE model shows a relatively better performance in forecasting inflation, while the BVARX model fares better in forecasting
    Keywords: forecasting, DSGE, BVARX, euro area
    JEL: C53 E32 E37
    Date: 2015–01

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