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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

Analysis to China's Urban and Rural CPI Data

SUN, FEI January 2012 (has links)
No description available.
2

Analýza sezónnosti v českém stavebnictví / Analysis of Seasonality in the Czech Construction

Šimpach, Ondřej January 2010 (has links)
The output of the National economy of the Czech Republic is conditioned by a sum of important factors. There are sectors, which increased power during the last two decades, mainly due to expansion of modern technologies and knowledge workers. One of this is Construction. Construction is specific to its position in the economy and in particular is characterized by the greatest seasonality ever. However, this is not a problem for statistical analysis, rather a benefit. Modern approaches allow us to analyze seasonal fluctuations. From selected data we are able to construct evolutionary forecasts. The work will be performed for the most important indicators in the Czech Construction. The outcome of the paper will be conditional forecasts of these indicators. It will also make analyze of the relationship between these indicators and other variables that might affected it. The work is practical application of stochastic modeling approach by Box and Jenkins, augmented by more modern approaches, such as verification of Granger causality and co-integration and testing of seasonal unit roots by Hylleberg et al.
3

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.
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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