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on Forecasting |
| By: | Amel GRAA (Djillali Liabes University); Ismail ZIANE (Djillali Liabes University); Farid BENHAMIDA (Djilali Liabes University) |
| Abstract: | Electricity demand forecasting has become a main field of research in electrical engineering. The effect of the unexpected economic fluctuations and high dependency of the power generation system on the energy resource, results in an electrical energy cost increase and demand fluctuation, so forecasting for electricity market is an predict system. The power industry requires forecasts not only from the production perspective but also from a financial viewpoint. This paper proposes a set of forecasting methods used for the electric energy demand. Similar day approach, trend method, econometric model, end-use approach are the most frequently used techniques for energy forecasting studies. Finally, authors discuss advantages and disadvantages of each method based on the theoretical background. |
| Keywords: | Forecasting approach, load demand, model section, electricity market, similar day method. |
| JEL: | C51 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:3105420&r=for |
| By: | Renáta Géczi-Papp (University of Miskolc) |
| Abstract: | In the decision making process the forecasting and time series analysis are important, but unfortunately the reliability of the prediction is often questionable. In today's rapidly changing business environment, it is crucial that decisions are based on correct information which means a better estimate of the expected economic developments. In this paper I examine the possible applications of using genetic algorithms in time series analysis to improve the reliability of the forecast. I try to submit the most relevant findings in the field of genetic algorithms and forecasting. My goal is to give a thorough description about the possible applications of genetic algorithms (GA) and I like to prove that this method can be useful in the time series analysis. The literature review is focused only to the prediction of stock market data. First I summarize shortly the most important methods of time series analysis, then I introduce the genetic algorithm and its main steps. The essential of the paper is the literature review, where I try to describe the most important applications of GA in finance. There are lots of interesting results in the forecasting of stock market data, which makes the GA more important. Of course the GA model is not perfect, it has some shortcomings and limitations of application. After drawing the conclusionsI hope this study will help the reader to understand better the genetic algorithm and its significance in the forecast. |
| Date: | 2015–10–15 |
| URL: | http://d.repec.org/n?u=RePEc:mic:etpdsw:9&r=for |
| By: | Chan, Joshua C C (Australian National University); Clark, Todd E. (Federal Reserve Bank of Cleveland); Koop, Gary (University of Strathclyde) |
| Abstract: | A knowledge of the level of trend inflation is key to many current policy decisions, and several methods of estimating trend inflation exist. This paper adds to the growing literature which uses survey-based long-run forecasts of inflation to estimate trend inflation. We develop a bivariate model of inflation and long-run forecasts of inflation which allows for the estimation of the link between trend inflation and the long-run forecast. Thus, our model allows for the possibilities that long-run forecasts taken from surveys can be equated with trend inflation, that the two are completely unrelated, or anything in between. By including stochastic volatility and time-variation in coefficients, it extends existing methods in empirically important ways. We use our model with a variety of inflation measures and survey-based forecasts. We find that long-run forecasts can provide substantial help in refining estimates of trend inflation over popular alternatives. But simply equating trend inflation with the long-run forecasts is not appropriate. |
| Keywords: | trend inflation; inflation expectations; state space model; stochastic volatility |
| JEL: | C11 C32 E31 |
| Date: | 2015–10–21 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwp:1520&r=for |
| By: | Anatolyev, Stanislav (New Economic School); Gospodinov, Nikolay (Federal Reserve Bank of Atlanta); Jamali, Ibrahim (American University of Beirut); Liu, Xiaochun (University of Central Arkansas) |
| Abstract: | In this paper, we study the effectiveness of carry trade strategies during and after the financial crisis using a flexible approach to modeling currency returns. We decompose the currency returns into multiplicative sign and absolute return components, which exhibit much greater predictability than raw returns. We allow the two components to respond to currency-specific risk factors and use the joint conditional distribution of these components to obtain forecasts of future carry trade returns. Our results suggest that the decomposition model produces higher forecast and directional accuracy than any of the competing models. We show that the forecasting gains translate into economically and statistically significant (risk-adjusted) profitability when trading individual currencies or forming currency portfolios based on the predicted returns from the decomposition model. |
| Keywords: | exchange rate forecasting; carry trade; positions of traders; return decomposition; copula; joint predictive distribution |
| JEL: | C32 C53 F31 F37 G15 |
| Date: | 2015–08–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedawp:2015-06&r=for |
| By: | Robert Lehmann |
| Abstract: | We evaluate whether survey-based indicators produce lower forecast errors for export growth than indicators obtained from hard data such as price and cost competitiveness measures. Our pseudo out-of-sample analyzes and forecast encompassing tests reveal that survey-based indicators outperform the benchmark model as well as the indicators from hard data for most of our 20 European states and the aggregates EA-18 and EU-28. The most accurate forecasts are on average produced by the confidence indicator in the manufacturing sector, the economic sentiment indicator and the production expectations. However, large country differences in the forecast accuracy of survey-based indicators emerge. These differences are mainly explained with country-specific export compositions. A larger share in raw material or oil exports worsens the accuracy of soft indicators. The accuracy of soft indicators improves if countries have a larger share in exports of machinery goods. For hard indicators, we find only weak evidence for the export composition to explain differences in forecast accuracy. |
| Keywords: | export forecasting; export expectations; price and cost competitiveness |
| JEL: | F01 F10 F17 |
| Date: | 2015–10 |
| URL: | http://d.repec.org/n?u=RePEc:wiw:wiwrsa:ersa15p756&r=for |
| By: | Jia Chen (Institute for Fiscal Studies); Degui Li (Institute for Fiscal Studies); Oliver Linton (Institute for Fiscal Studies and University of Cambridge); Zudi Lu (Institute for Fiscal Studies) |
| Abstract: | In this paper, we consider semiparametric model averaging of the nonlinear dynamic time series system where the number of exogenous regressors is ultra large and the number of autoregressors is moderately large. In order to accurately forecast the response variable, we propose two semiparametric approaches of dimension reduction among the exogenous regressors and auto-regressors (lags of the response variable). In the first approach, we introduce a Kernel Sure Independence Screening (KSIS) technique for the nonlinear time series setting which screens out the regressors whose marginal regression (or auto-regression) functions do not make significant contribution to estimating the joint multivariate regression function and thus reduces the dimension of the regressors from a possible exponential rate to a certain polynomial rate, typically smaller than the sample size; then we consider a semiparametric method of Model Averaging MArginal Regression (MAMAR) for the regressors and auto-regressors that survive the screening procedure, and propose a penalised MAMAR method to further select the regressors which have significant effects on estimating the multivariate regression function and predicting the future values of the response variable. In the second approach, we impose an approximate factor modelling structure on the ultra-high dimensional exogenous regressors and use a well-known principal component analysis to estimate the latent common factors, and then apply the penalised MAMAR method to select the estimated common factors and lags of the response variable which are significant. Through either of the two approaches, we can finally determine the optimal combination of the significant marginal regression and auto-regression functions. Under some regularity conditions, we derive the asymptotic properties for the two semiparametric dimension-reduction approaches. Some numerical studies including simulation and an empirical application are provided to illustrate the proposed methodology. |
| Keywords: | Kernel smoother; penalised MAMAR; principal component analysis; semiparametric approximation; sure independence screening; ultra-high dimensional time series |
| JEL: | C14 C22 C52 |
| Date: | 2015–10 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:cemmap:62/15&r=for |
| By: | Matthieu Droumagueta (Department of Economics, European University Institute); Anders Warneb (Directorate General Research, European Central Bank); Tomasz Wozniakc (Department of Economics, University of Melbourne) |
| Abstract: | We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for posterior inference include a novel block Metropolis-Hastings sampling algorithm for the estimation of the restricted models. We analyze a system of monthly US data on money and income. The results of testing in MS-VARs contradict those in linear VARs: the money aggregate M1 is useful for forecasting income and for predicting the next period’s state. |
| Keywords: | Technical Efficiency, Penalised Splines, Gibbs Sampling |
| JEL: | C11 C12 C32 C53 E32 |
| Date: | 2015–05 |
| URL: | http://d.repec.org/n?u=RePEc:mlb:wpaper:1191&r=for |
| By: | Francine Gresnigt (Erasmus University Rotterdam); Erik Kole (Erasmus University Rotterdam); Philip Hans Franses (Erasmus University Rotterdam, the Netherlands) |
| Abstract: | We develop Hawkes models in which events are triggered through self as well as cross-excitation. We examine whether incorporating cross-excitation improves the forecasts of extremes in asset returns compared to only self-excitation. The models are applied to US stocks, bonds and dollar exchange rates. In-sample, a Lagrange Multiplier test indicates the existence of cross-excitation for these series. Out-of-sample, we find that the models that include spillover effects forecast crashes and the Value-at-Risk significantly more accurately than the models without. |
| Keywords: | Hawkes processes; extremal dependence; Value-at-Risk; financial crashes; spillover |
| JEL: | G01 G17 |
| Date: | 2015–10–23 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20150118&r=for |
| By: | Wohlrabe, Klaus; Bührig, Pascal |
| Abstract: | Macroeconomic variables, such as industrial production or GDP, are regularly and sometimes substantially revised by the official statistical offices. Nevertheless, there are only few attempts in the previous literature to investigate whether it is possible to forecast these revisions systematically. In this paper it is illustrated how revisions of German industrial production can be forecasted with respect both to the direction as well as to the level of the revision. We are the first that use a large data for this purpose. |
| Keywords: | industrial production, revisions, forecasting, large data sets, forecast combination |
| JEL: | C53 E37 E66 |
| Date: | 2015–10–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:67513&r=for |
| By: | Aurelio Volpe (CSIL Centre for Industrial Studies) |
| Abstract: | This report offers a comprehensive picture of the kitchen furniture industry in India, providing trends and forecasts in kitchen furniture production and consumption, kitchen furniture imports and exports. Kitchen furniture prices, as well as the value and weight of the built-in appliances on kitchen furniture supply. Distribution system and top companies operating in the kitchen furniture industry in India are also considered. 2008-2013 data on the kitchen furniture sector and macroeconomic forecast up to 2018. The report provides a breakdown of supply of kitchen furniture by cabinet door material (solid wood, veneer, laminated, thermoplastics, lacquered, melamine, aluminium, glass), by cabinet door colour (white, bright, neutral), by cabinet door lacquering (bright, opaque) and by worktop material (solid surface materials, natural and engineered stone, laminated, tiles, steel, wood, glass). A breakdown of Indian kitchen furniture exports and imports is provided by country and by geographical area. Short company profiles of the top 50 kitchen furniture manufacturers are included. The analysis of distribution channels for kitchen furniture covers: direct sales/contract, kitchen specialists, furniture shops. The competitive system analyses the presence of the major kitchen furniture manufacturers in India and when possible the targeted market (towns, price level). Market shares are included for a sample of 50 companies. Among the considered products: kitchen furniture, kitchens, kitchen cabinet doors, worktops, built-in appliances, hoods, sinks, ovens, hobs, refrigerators, dishwashers. A specific analysis on main players in the distribution is given for the six states and eight megacities that are considered strategic for the future of the kitchen furniture business. Leading furniture importers are listed by city, as well as leading architects and interior designers. Considered states include Delhi-Haryana, Maharashtra, Karnataka, Kerala, West Bengal. In the focused megacities a growth of household expenditure is given by around 50% from 2013 up to 2020, as an average About 200 addresses of key operators (about 50 kitchen furniture manufacturers, other 150 among importers, retailers and architectural companies) are included. The study has been carried out involving direct interviews in Delhi, Mumbai and Bangalore. |
| JEL: | L11 L22 L68 L81 |
| Date: | 2014–04 |
| URL: | http://d.repec.org/n?u=RePEc:mst:csilre:s76&r=for |
| By: | Charles Nathanson (Kellogg School of Management, Northweste); Edward Glaeser (Harvard University) |
| Abstract: | A modest approximation by homebuyers leads house prices to display three attributes that are present in the data but usually missing from perfectly rational models of housing dynamics: momentum at one-year horizons, mean reversion at five-year horizons, and excess longer-term volatility relative to fundamentals. Valuing a house involves forecasting the current and future demand to live in the surrounding area. Buyers forecast using the history of transaction prices. Approximating buyers do not adjust for the expectations of past buyers, and instead assume that past prices reflect only contemporaneous demand. Consistent with survey evidence, this approximation leads buyers to expect increases in the market value of their homes after recent house price increases, to fail to anticipate the price busts that follow booms, and to be overconfident in their assessments of the housing market. |
| Date: | 2015 |
| URL: | http://d.repec.org/n?u=RePEc:red:sed015:1108&r=for |
| By: | Ledenyov, Dimitri O.; Ledenyov, Viktor O. |
| Abstract: | The accurate forecast of the foreign currencies exchange rates at the ultra high frequency electronic trading in the foreign currencies exchange markets is a main topic of our research: 1) the present state of the foreign currencies exchange markets in Asia, Europe and North America; 2) the research review on the classic forecast techniques of the foreign currencies exchange rates dynamics in the foreign currencies exchange markets in the classic finances theory; 3) the description on the quantum forecast techniques of the foreign currencies exchange rates dynamics in the foreign currencies exchange markets with the application of both the wave function and the time dependent / time independent wave equation in the quantum finances theory; 4) the derivation of the time dependent / time independent wave equation in the quantum finances theory; 5) the creation of the quantum system state prediction algorithm, based on both the wave function and the time dependent / time independent wave equation in the quantum finances theory; 6) the discussion on the developed software program with the embedded quantum system state prediction algorithm, using both the wave function and the time dependent / time independent wave equation in the quantum finances theory; 7) the final words on the perspectives of the quantum forecast techniques of the foreign currencies exchange rates dynamics in the foreign currencies exchange markets, applying both the wave function and the time dependent / time independent wave equation in the quantum finances theory. |
| Keywords: | ultra high frequency electronic trading, foreign currencies exchange rates, foreign currencies exchange markets, vehicle currency, interest rate, retail aggregator, liquidity aggregator, interdealer trade orders flow direction, stop-loss order, bid - ask spreads, price discovery process, capital inflow, capital outflow, carry trade strategy, financial liquidity, FX market micro structure, FX rate dynamics, absorption/diffusion/transmission of information, information theory, asymmetric information, autoregressive conditional heteroskedasticity, Wiener filtering theory, Stratanovich-Kalman-Bucy filtering algorithm / filter, particle filter, quantum system state prediction algorithm with wave function, time dependent / time independent wave equation, nonlinearities, artificial intelligence, Ledenyov strategy search algorithm, econophysics, econometrics, global foreign exchange market, global capital market, wealth management. |
| JEL: | C01 C02 C32 C53 C58 G0 G11 G15 G17 G24 |
| Date: | 2015–10–27 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:67470&r=for |