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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Forecast Comparison of Models Based on SARIMA and the Kalman Filter for Inflation

Nikolaisen Sävås, Fredrik January 2013 (has links)
Inflation is one of the most important macroeconomic variables. It is vital that policy makers receive accurate forecasts of inflation so that they can adjust their monetary policy to attain stability in the economy which has been shown to lead to economic growth. The purpose of this study is to model inflation and evaluate if applying the Kalman filter to SARIMA models lead to higher forecast accuracy compared to just using the SARIMA model. The Box-Jenkins approach to SARIMA modelling is used to obtain well-fitted SARIMA models and then to use a subset of observations to estimate a SARIMA model on which the Kalman filter is applied for the rest of the observations. These models are identified and then estimated with the use of monthly inflation for Luxembourg, Mexico, Portugal and Switzerland with the target to use them for forecasting. The accuracy of the forecasts are then evaluated with the error measures mean squared error (MSE), mean average deviation (MAD), mean average percentage error (MAPE) and the statistic Theil's U. For all countries these measures indicate that the Kalman filtered model yield more accurate forecasts. The significance of these differences are then evaluated with the Diebold-Mariano test for which only the difference in forecast accuracy of Swiss inflation is proven significant. Thus, applying the Kalman filter to SARIMA models with the target to obtain forecasts of monthly inflation seem to lead to higher or at least not lower predictive accuracy for the monthly inflation of these countries.
2

Forecast Performance Between SARIMA and SETAR Models: An Application to Ghana Inflation Rate

AIDOO, ERIC January 2011 (has links)
In recent years, many research works such as Tiao and Tsay (1994), Stock and Watson (1999), Chen et al. (2001), Clements and Jeremy (2001), Marcellino (2002), Laurini and Vieira (2005) and others have described the dynamic features of many macroeconomic variables as nonlinear. Using the approach of Keenan (1985) and Tsay (1989) this study shown that Ghana inflation rates from January 1980 to December 2009 follow a threshold nonlinear process.  In order to take into account the nonlinearity in the inflation rates we then apply a two regime nonlinear SETAR model to the inflation rates and then study both in-sample and out-of-sample forecast performance of this model by comparing it with the linear SARIMA model. Based on the in-sample forecast assessment from the linear SARIMA and the nonlinear SETAR models, the forecast measure MAE and RMSE suggest that the nonlinear SETAR model outperform the linear SARIMA model. Also using multi-step-ahead forecast method we predicted and compared the out-of-sample forecast of the linear SARIMA and the nonlinear SETAR models over the forecast horizon of 12 months during the period of 2010:1 to 2010:12. From the results as suggested by MAE and RMSE, the forecast performance of the nonlinear SETAR models is superior to that of the linear SARIMA model in forecasting Ghana inflation rates. Thought the nonlinear SETAR model is superior to the SARIMA model according to MAE and RMSE measure but using Diebold-Mariano test, we found no significant difference in their forecast accuracy for both in-sample and out-of-sample.
3

Análise e previsão de curto prazo do vento através de modelagem estatística em áreas de potencial eólico no nordeste do Brasil.

SILVA, Pollyanna Kelly de Oliveira. 13 August 2018 (has links)
Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-08-13T15:28:50Z No. of bitstreams: 1 POLLYANNA KELLY DE OLIVEIRA SILVA - TESE (PPGMet) 2017.pdf: 11004478 bytes, checksum: 0d5e098181f432beffc2fd8155027f1e (MD5) / Made available in DSpace on 2018-08-13T15:28:50Z (GMT). No. of bitstreams: 1 POLLYANNA KELLY DE OLIVEIRA SILVA - TESE (PPGMet) 2017.pdf: 11004478 bytes, checksum: 0d5e098181f432beffc2fd8155027f1e (MD5) Previous issue date: 2017-08-30 / CNPq / O vento como fonte para geração de energia elétrica é analisado neste trabalho através de sua variabilidade e da obtenção de previsões de curto prazo para o ano de 2010, período de atuação de El Niño-Oscilação Sul (ENOS) moderado. Modelos de séries temporais propostos por Box-Jenkins e o indicador de desempenho de predição MMREE são usados para obter as melhores estimativas da velocidade do vento com base nas séries observadas. São utilizados dados anemométricos do Projeto SONDA situado às margens do Rio São Francisco em Petrolina – PE, e de dois parques eólicos localizados no litoral do Estado do Ceará: Quixaba (litoral leste), na cidade de Aracati, e Lagoa Seca (litoral oeste), na cidade de Acaraú. O ciclo diário do vento tem velocidades mais baixas (altas) no período da madrugada-início da manhã (pela manhã e final da noite, com exceção do litoral oeste, cujas máximas ocorrem no final da tarde). Um cisalhamento vertical negativo, no vento local, é observado em períodos distintos do dia nas três áreas de estudo. No Ceará ele ocorre no período da manhã (início da tarde e meio da noite) no litoral leste (oeste) e no Lago de Sobradinho durante a noite até o início da manhã. Foi observado que no litoral leste os ventos são mais fortes, provavelmente devido à curvatura côncava do litoral. As estimativas da velocidade do vento no horizonte de 24 horas pelo modelo SARIMA, com dados horários dos 30 dias anteriores ao dia da previsão para treino (Caso 2), mostraram redução nos erros e melhora significativa na série estimada no período da madrugada-início da manhã; no Lago de Sobradinho essas estimativas são mais precisas, quando comparadas àquelas feitas com base em toda a série de dados (Caso 1). Os resultados indicam que o modelo SARIMA com período de entrada de dados menor pode ser aplicado para a previsão da velocidade do vento em áreas de potencial eólico, dando suporte ao operador da rede elétrica na programação da geração despachável para o dia seguinte. / The wind as a source for power generation is analyzed in this work by means of its variability and short-range wind forecasts for the year of 2010, period of moderate El Niño-Southern Oscillation (ENSO). Time series models proposed by Box-Jenkins and the indicator of forecast accuracy MMREE are used to obtain the best wind speed estimates based on the observed series. Anemometric data of the SONDA Project located on the shore of the São Francisco River in Petrolina-PE, and of two wind power plants located on the coast of the Ceará State, Quixaba (east coast), in the city of Aracati, and Lagoa Seca (west coast), in the city of Acaraú, are used. The daily wind cycle has lower (higher) speeds in late night-early morning (in the morning and end of the night, with exception of the west coast, whose maxima occur in late afternoon). A negative vertical shear in the local wind is observed in distinct periods of the day in the three study areas. In Ceará it occurs in the morning (early afternoon and middle of the night) on the east (west) coast and on Sobradinho Lake at night until early in the morning. It was observed that the winds are stronger on the east coast, probably due to the coast’s concave curvature. The wind speed estimates in a 24-hour horizon by the SARIMA model, with hourly data of the 30 days that precede the forecast day for training (Case 2), showed reduction in the errors and significant improvement in the estimated series in late night-early morning; in Sobradinho Lake these estimates are more accurate, as compared to the estimates based on the entire data series (Case 1). The results indicate that the SARIMA model with horter time series as input may be applied to forecast wind speed in areas of eolic potential, giving support to the system operator in programming the dispatchable distributed generation for the next day.
4

MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS

AIDOO, ERIC January 2010 (has links)
Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.

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