Modelling the Quarterly GDP - Role of Economic and Surveys Indicators

Celiku, Evelina Josa; Kristo, Ermelinda; Toska, Merita

doi:10.2139/ssrn.2471546

Cited by 3 publications

(3 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As there is no method that can be called as the best one, many countries build a set of different models for prediction of GDP. For example ARIMA models with the seasonal components and indicator models, similar to the bridge models are used in Albania (Celiku et al, 2009).…”

Section: Methods Applied For Prediction Of Economic Indicatorsmentioning

confidence: 99%

Prediction of GDP Based on the Lag Economic Indicators

Stundžienė

2015

View full text Add to dashboard Cite

There are several institutions that constantly announce their predictions of general domestic product (GDP) and lots of institutions that use this information. Frequently the forecasts of different institutions vary because they use different methods, but all the institutions for which this indicator is relevant cannot make the predictions themselves because the models are too sophisticated. The main purpose of this research is to create simple enough but also accurate model for prediction of Lithuanian GDP that can be used by all the institutions that need this indicator. The research is based on the economic data that are measured and published quarterly or monthly by Statistics Lithuania. 154 economic indicators were analysed as possible independent variables for regression model creation. The analysis showed that the regression model with twelve lag independent variables can be quite accurate for a short-term prediction of Lithuanian GDP. It can be forecasted by such indexes as the number of immigrants, the turnover of wholesale and retail trade and repair of motor vehicles and motorcycles, the number of overnight stays in the accommodation establishments, an average hourly earnings, the rate of change in the producer prices of all the industry (except construction) of Lithuanian market, the imports and seasonally adjusted imports, the seasonally adjusted exports, the projected number of employees in the trade enterprises for the next 2-3 months, the industrial production (of all the industry except construction).

show abstract

Section: Methods Applied For Prediction Of Economic Indicatorsmentioning

confidence: 99%

Prediction of GDP Based on the Lag Economic Indicators

Stundžienė

2015

View full text Add to dashboard Cite

show abstract

“…As there is no one the most suitable method, many countries build a set of different models for prediction of quarterly GDP. For example ARIMA models with the seasonal components and indicator models, similar to the bridge models are used in Albania (Celiku, Kristo, Boka, 2009).…”

Section: Description Of the Methods Applied For The Economic Analysismentioning

confidence: 99%

Prediction of Lithuanian Gdp: Are Regression Models or Time Series Models Better?

Stundžienė

2014

ecoman

View full text Add to dashboard Cite

The problem of Lithuanian GDP prediction is relevant. There are several institutions, such as Statistics Lithuania, state's central bank, other banks that constantly announce their predictions of GDP. Frequently the forecasts of different institutions vary because they use different methods. The main purpose of the paper is to investigate whether regression models made of the monthly published economic indicators or time series models are better for Lithuanian GDP prediction. The changes of Lithuanian GDP as well as many other economic indicators that have impact on GDP are published quarterly. Prediction of quarterly economic indicators as it was done by the most researchers can be related to greater errors comparing with the prediction models that are made according to the monthly data. Monthly data can ensure that the newest information is used for prediction of GDP and show how the state's economy is changing in the current quarter, that's why it can reduce the error of prediction. The research is based on the economic data that is measured and published monthly by Statistics Lithuania (154 ratios at all). Various linear and non-linear regression models are made in order to find the best model for Lithuanian GDP prediction. The results of regression models are also compared with the results got by ARIMA (time series) models. The analysis showed that the regression models made of monthly published economic indicators may be better than time series models for prediction or Lithuanian GDP.

show abstract

“…In the developed countries, there is extensive empirical literature on GDP forecasting using the ARIMA model. Many authors have considered univariate time series models (Çeliku et al 2010;Wei et al 2010;Maity and Chatterjee 2012;Shahini and Haderi 2013;Zakai 2014;Wabomba et al 2016), as well as multivariate time series models (Baffigi et al 2004;Kuzin et al 2009;Angelini et al 2010;Buss 2010;Çeliku et al 2010;Barhoumi et al 2011;Cobb et al 2011;Antipa et al 2012;Shahini and Haderi 2013;Dias et al 2015;Dritsaki 2015). Some authors considered time series on an annual basis (Wei et al 2010;Maity and Chatterjee 2012;Zakai 2014;Wabomba et al 2016) while others used time series on an annual basis (Baffigi et al 2004;Nunes 2005;Schumacher and Breitung 2008;Angelini et al 2010;Buss 2010;Çeliku et al 2010;Barhoumi et al 2011;Cobb et al 2011;Antipa et al 2012;Shahini and Haderi 2013;Dias et al 2015;Dritsaki 2015).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Nowcasting GDP of Bosnia and Herzegovina: A Comparison of Forecast Accuracy Models

Abdić

Resić

Rovčanin

2020

South East European Journal of Economics and Business

View full text Add to dashboard Cite

The paper explores the possibilities of creating an econometric model for making short-term forecasts of the Gross Domestic Product of Bosnia and Herzegovina (GDP of B&H). Its aim is to determine the most representative and most efficient model for forecasting the quarterly GDP of B&H. This is the first paper that simultaneously compares ARIMA models, bridge models and factor models in three different time periods. All variables are available for the period of 2006q1-2016q4. The final choice of the model for forecasting the quarterly GDP of B&H was selected on the basis of a comparative analysis of the predictive efficiency of the analysed models. Based on the obtained results, the most efficient model for forecasting quarterly GDP of B&H is the bridge model, which includes four variables as regressor: Retail sale of other goods, Total loans, Manufacturing and Manufacture of food products.

show abstract

Modelling the Quarterly GDP - Role of Economic and Surveys Indicators

Cited by 3 publications

References 2 publications

Prediction of GDP Based on the Lag Economic Indicators

Prediction of GDP Based on the Lag Economic Indicators

Prediction of Lithuanian Gdp: Are Regression Models or Time Series Models Better?

Nowcasting GDP of Bosnia and Herzegovina: A Comparison of Forecast Accuracy Models

Contact Info

Product

Resources

About