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

Stundžienė, Alina

doi:10.5755/j01.em.18.4.5041

Cited by 4 publications

(5 citation statements)

References 23 publications

(12 reference statements)

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“…Any created model for prediction of Lithuanian GDP still cannot be called as the best. The previous researches made by (Stundziene, 2013) showed that regression model is better than time series model for prediction Lithuanian GDP. After the analysis of 154 economic indicators measured and published monthly by Statistics Lithuania showed that the best multiple linear regression model that can be applied for prediction of Lithuanian GDP is Y = 0.002826•X127 + 0.473039•X147 + 4.573546•X153.…”

Section: The Background Of a New Model For Prediction Of Lithuanian Gdpmentioning

confidence: 97%

“…Commonly two things must be coordinated: the precision of the model and its complexity. Usually the better prediction can be obtained by complicated model, but because of its complexity only a small part of stakeholders can use it, and on the contrary, if we use a simple model the precision of it is not sufficient (Stundziene, 2013). Thus, the goal of this article is to reconcile these two thinks.…”

Section: Introductionmentioning

confidence: 99%

“…if one indicator changes the other indicators react as well. So regression analysis can be one of the possibilities to describe these relations (Stundziene, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of GDP Based on the Lag Economic Indicators

Stundžienė

2015

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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).

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Section: The Background Of a New Model For Prediction Of Lithuanian Gdpmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Prediction of GDP Based on the Lag Economic Indicators

Stundžienė

2015

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“…One is to forecast GDP based on its lag periods, such as with the autoregressive moving average (ARIMA) model [5,6], grey prediction model and its extended form [7,8], BP neural network model [9], etc. But these methods have certain downsides; therefore, some scholars have included GDP-related variables in the prediction, mainly employing same-frequency data [10,11]. In order to avoid information loss, Ghysels et al [3,4] proposed the MIDAS model, which was initially designed to analyse and predict the stock market's volatility based on mixed-frequency data.…”

Section: Literature Reviewmentioning

confidence: 99%

Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product

Wang,

Su,

Zhang

et al. 2023

Mathematics

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As an important indicator that can reflect a country’s macroeconomic situation and future trend, experts and scholars have long focused on analyses and predictions of gross domestic product (GDP). Combining principal component analysis (PCA), the mixed-frequency data sampling (MIDAS) model and the error correction model (ECM), this investigation constructs the principal-component-based ECM-MIDAS and co-integration MIDAS (CoMIDAS) models, respectively. After that, this investigation uses the monthly consumption, investment and trade data to build a mixed-frequency model to predict quarterly GDP. The empirical results can be summarized as follows: First, the predictive effectiveness of the mixed-frequency model is better than that of the same-frequency model. Second, the three variables have a strong correlation, and applying the principal component idea when modelling the same and mixed frequencies can lead to more favourable predictive effectiveness. Third, adding an error correction term to the principal-component-based mixed-frequency model has a significant coefficient and a higher predictive accuracy. Based on the above, it can be concluded that combining the MIDAS model with error correction and a principal component is effective; thus, this combination may be applied to support real-time and accurate macroeconomic prediction.

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“…Mathematics is the backbone of modern economics, as it plays a basic role in creating ideas, constructing new theories, and empirically testing ideas and theories. Stundziene (2013) mentioned prediction of the state's economic situation is very important, as it shows the competitiveness of the state, production, sales, profit of the firm and human welfare. In fact, the prediction of economic outcomes in the contemporary literatures are difficult to explain without mathematics.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Approach for Predicting the Gross Domestic Product of Malaysia

Ayob

Rohni

2020

MJSSH

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Gross domestic product (GDP) is a monetary measure of the market value of overall final goods and services produced in a given year, and serves as a gauge of the economy’s overall health and size. The GDP prediction is significant, as it can capture and understand the future developments of a country’s economy. In this paper, three different mathematical models have been used to predict Malaysia’s gross domestic product using regressions. The models discussed in this paper are linear, exponential and parabolic regressions. In developing the models, data from year 1970 to 2014 has been employed and data from year 2015 to 2019 has been used to examine the models' accuracy. The models are then observed to identify the most appropriate to express the relationship between the years and Malaysia’s gross domestic product. In this study, it is found that the parabolic regression model is more accurate compared to the linear and exponential regression models. The parabolic regression model is also the most appropriate since it is adjusted to the real conditions of Malaysia's gross domestic product which is the main subject of this paper. Finally, it is obtained that the prediction values of GDP in Malaysia will increase for the next ten years (2020 - 2029). Keywords: Gross domestic product, Linear regression, Exponential regression, Parabolic regression

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

Cited by 4 publications

References 23 publications

Prediction of GDP Based on the Lag Economic Indicators

Prediction of GDP Based on the Lag Economic Indicators

Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product

Mathematical Approach for Predicting the Gross Domestic Product of Malaysia

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