Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods

Kishwer Sultana; Adila Rahim; Nighat Moin; Sajida Aman; Saghir Pervaiz Ghauri

doi:doi:10.11648/j.ijber.20130206.17

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Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods

Kishwer Sultana, Adila Rahim, Nighat Moin, Sajida Aman, Saghir Pervaiz Ghauri

Published in International Journal of Business and Economics Research (Volume 2, Issue 6)

Received: 30 December 2013 Published: 30 January 2014

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Abstract

Forecasting of time series is important subject in macroeconomics. We use two time series methods. One of the most simple and basic method for forecasting time series is decomposition. Decomposing the time series means breaking the time series into four components, i.e., trend, cycle, seasonal and irregular. The second method is based on ARIMA model. In this paper we forecast the macroeconomic variables CPI, and LSM for period July 2013 to September 2013, based on decomposition of actual series of these variables and ARIMA model for monthly series from July 2008 to June 2013. We compare the out-of-sample forecast of two methods based on the mean absolute deviation (MAD) & sum of square of errors (SSE) and decide on which method provides the best forecasting accuracy which policy makers can rely on in forecasting inflation (CPI) and Economic growth (LSM).

Published in	International Journal of Business and Economics Research (Volume 2, Issue 6)
DOI	10.11648/j.ijber.20130206.17
Page(s)	174-178
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Time Series Decomposition, Trend, Season, Cycles, Irregular and ARIMA Model

References

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[10]	Kamruzzaman J. & Sarker RA. 2003. Forecasting of currency exchange rates using ANN:A case study. IEEE Int. Conf. Neural Networks & Signal Processing 14-17
[11]	Lee U. 2012. Forecasting inflation for inflation-targeted countries: A comparison of the predictive performance of alternative inflation forecasting models. Journal of Business & Economic Studies 18 (1) 75-95.
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[13]	Ljung GM. & Box GEP. 1978. On the measure of lack of fit in time series models. Biometrika 65 297-303.
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[16]	Newbold P.1983. ARIMA model building and the timeseries analysis approach to forecasting. Journal of Forecasting 2: 23-35
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APA Style

Kishwer Sultana, Adila Rahim, Nighat Moin, Sajida Aman, Saghir Pervaiz Ghauri. (2014). Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods. International Journal of Business and Economics Research, 2(6), 174-178. https://doi.org/10.11648/j.ijber.20130206.17

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ACS Style

Kishwer Sultana; Adila Rahim; Nighat Moin; Sajida Aman; Saghir Pervaiz Ghauri. Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods. Int. J. Bus. Econ. Res. 2014, 2(6), 174-178. doi: 10.11648/j.ijber.20130206.17

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AMA Style

Kishwer Sultana, Adila Rahim, Nighat Moin, Sajida Aman, Saghir Pervaiz Ghauri. Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods. Int J Bus Econ Res. 2014;2(6):174-178. doi: 10.11648/j.ijber.20130206.17

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@article{10.11648/j.ijber.20130206.17,
  author = {Kishwer Sultana and Adila Rahim and Nighat Moin and Sajida Aman and Saghir Pervaiz Ghauri},
  title = {Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods},
  journal = {International Journal of Business and Economics Research},
  volume = {2},
  number = {6},
  pages = {174-178},
  doi = {10.11648/j.ijber.20130206.17},
  url = {https://doi.org/10.11648/j.ijber.20130206.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20130206.17},
  abstract = {Forecasting of time series is important subject in macroeconomics. We use two time series methods. One of the most simple and basic method for forecasting time series is decomposition. Decomposing the time series means breaking the time series into four components, i.e., trend, cycle, seasonal and irregular. The second method is based on ARIMA model. In this paper we forecast the macroeconomic variables CPI, and LSM for period July 2013 to September 2013, based on decomposition of actual series of these variables and ARIMA model for monthly series from July 2008 to June 2013. We compare the out-of-sample forecast of two methods based on the mean absolute deviation (MAD) & sum of square of errors (SSE) and decide on which method provides the best forecasting accuracy which policy makers can rely on in forecasting inflation (CPI) and Economic growth (LSM).},
 year = {2014}
}

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TY  - JOUR
T1  - Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods
AU  - Kishwer Sultana
AU  - Adila Rahim
AU  - Nighat Moin
AU  - Sajida Aman
AU  - Saghir Pervaiz Ghauri
Y1  - 2014/01/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ijber.20130206.17
DO  - 10.11648/j.ijber.20130206.17
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 174
EP  - 178
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20130206.17
AB  - Forecasting of time series is important subject in macroeconomics. We use two time series methods. One of the most simple and basic method for forecasting time series is decomposition. Decomposing the time series means breaking the time series into four components, i.e., trend, cycle, seasonal and irregular. The second method is based on ARIMA model. In this paper we forecast the macroeconomic variables CPI, and LSM for period July 2013 to September 2013, based on decomposition of actual series of these variables and ARIMA model for monthly series from July 2008 to June 2013. We compare the out-of-sample forecast of two methods based on the mean absolute deviation (MAD) & sum of square of errors (SSE) and decide on which method provides the best forecasting accuracy which policy makers can rely on in forecasting inflation (CPI) and Economic growth (LSM).
VL  - 2
IS  - 6
ER  -

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Author Information

Kishwer Sultana

Lecturer, Govt. Degree College, Bufferzone, Karachi, Pakistan
Adila Rahim

Assistant Professor, Jinnah University for Women, Karachi , Pakistan
Nighat Moin

Assistant Professor, Jinnah University for Women, Karachi , Pakistan
Sajida Aman

MS student, Jinnah University for Women, Karachi, Pakistan
Saghir Pervaiz Ghauri

Joint Director, Research Department, State Bank of Pakistan

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Plain Text BibTeX RIS

APA Style

Kishwer Sultana, Adila Rahim, Nighat Moin, Sajida Aman, Saghir Pervaiz Ghauri. (2014). Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods. International Journal of Business and Economics Research, 2(6), 174-178. https://doi.org/10.11648/j.ijber.20130206.17

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ACS Style

Kishwer Sultana; Adila Rahim; Nighat Moin; Sajida Aman; Saghir Pervaiz Ghauri. Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods. Int. J. Bus. Econ. Res. 2014, 2(6), 174-178. doi: 10.11648/j.ijber.20130206.17

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AMA Style

Kishwer Sultana, Adila Rahim, Nighat Moin, Sajida Aman, Saghir Pervaiz Ghauri. Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods. Int J Bus Econ Res. 2014;2(6):174-178. doi: 10.11648/j.ijber.20130206.17

Copy | Download

@article{10.11648/j.ijber.20130206.17,
  author = {Kishwer Sultana and Adila Rahim and Nighat Moin and Sajida Aman and Saghir Pervaiz Ghauri},
  title = {Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods},
  journal = {International Journal of Business and Economics Research},
  volume = {2},
  number = {6},
  pages = {174-178},
  doi = {10.11648/j.ijber.20130206.17},
  url = {https://doi.org/10.11648/j.ijber.20130206.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijber.20130206.17},
  abstract = {Forecasting of time series is important subject in macroeconomics. We use two time series methods. One of the most simple and basic method for forecasting time series is decomposition. Decomposing the time series means breaking the time series into four components, i.e., trend, cycle, seasonal and irregular. The second method is based on ARIMA model. In this paper we forecast the macroeconomic variables CPI, and LSM for period July 2013 to September 2013, based on decomposition of actual series of these variables and ARIMA model for monthly series from July 2008 to June 2013. We compare the out-of-sample forecast of two methods based on the mean absolute deviation (MAD) & sum of square of errors (SSE) and decide on which method provides the best forecasting accuracy which policy makers can rely on in forecasting inflation (CPI) and Economic growth (LSM).},
 year = {2014}
}

Copy | Download

TY  - JOUR
T1  - Forecasting Inflation and Economic Growth of Pakistan by Using Two Time Series Methods
AU  - Kishwer Sultana
AU  - Adila Rahim
AU  - Nighat Moin
AU  - Sajida Aman
AU  - Saghir Pervaiz Ghauri
Y1  - 2014/01/30
PY  - 2014
N1  - https://doi.org/10.11648/j.ijber.20130206.17
DO  - 10.11648/j.ijber.20130206.17
T2  - International Journal of Business and Economics Research
JF  - International Journal of Business and Economics Research
JO  - International Journal of Business and Economics Research
SP  - 174
EP  - 178
PB  - Science Publishing Group
SN  - 2328-756X
UR  - https://doi.org/10.11648/j.ijber.20130206.17
AB  - Forecasting of time series is important subject in macroeconomics. We use two time series methods. One of the most simple and basic method for forecasting time series is decomposition. Decomposing the time series means breaking the time series into four components, i.e., trend, cycle, seasonal and irregular. The second method is based on ARIMA model. In this paper we forecast the macroeconomic variables CPI, and LSM for period July 2013 to September 2013, based on decomposition of actual series of these variables and ARIMA model for monthly series from July 2008 to June 2013. We compare the out-of-sample forecast of two methods based on the mean absolute deviation (MAD) & sum of square of errors (SSE) and decide on which method provides the best forecasting accuracy which policy makers can rely on in forecasting inflation (CPI) and Economic growth (LSM).
VL  - 2
IS  - 6
ER  -

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