Econometric Forecasting Models Research Articles

Среднесрочная межотраслевая эконометрическая модель экономики города Москвы

As the largest Russian region with high socio-economic indicators, Moscow affects economic development of the whole country. Therefore, the present study aims to develop a model to forecast main indicators of the Moscow economy. To accomplish the task, it is necessary to: a) choose a suitable model and forecasting methods; b) transform available regional statistics into an appropriate form; c) select an algorithm for assessing the impact of detailed budget expenditures on the Moscow economy; d) combine the obtained results to construct a forecasting model. The proposed medium-term forecasting model of the Moscow economy includes both interindustry and econometric approaches. The study justified the use of cross-validation metrics for selecting optimal econometric forecasting models. An algorithm for converting budget expenditure data from detailed expenditure codes into economic activities and product data was developed. We assessed the impact of Moscow’s budget expenditures on the economy considering intra-city interindustry connections. According to the model calculations, two complex macroeconomic forecasts were used as scenarios: the base forecast of the Ministry of Economic Development of Russia (April 2023) and the lower-growth forecast of the Institute of Economic Forecasting of the Russian Academy of Sciences (March 2023). The scenario of the Ministry of Economic Development assumes that, in 2023–2025, the Russian gross domestic product (GDP) in constant prices will increase by 1.2, 2.0 and 2.6 %, respectively. The gross regional product (GRP) of Moscow is expected to increase by 0.5, 0.8 and 1.2 %, respectively. The calculations show that, depending on these scenarios, Moscow’s GRP in current prices will grow up to 30.9-31.7 trillion roubles by 2025. Moscow budget expenditure multiplier for GRP is estimated as 0.76-0.77 for 2023-2025.

CONSTRUCTION AND FORECASTING ECONOMETRIC MODELS OF MACROECONOMIC INDICATORS BASED ON INTERNATIONAL STANDARDS OF THE NATIONAL ACCOUNTING SYSTEM

This article describes the theoretical-methodological foundations of forecasting macroeconomic indicators based on the rules of the national accounts system, specific features of gross domestic product calculation, its improvement and forecasting, scientific proposals and recommendations for the development of econometric forecasting models and methodology of macroeconomic indicators. KEY WORDS: system of national accounts, gross domestic product, econometric model, forecasting, comparative indicators, comparative analysis, econometric evaluation, regression model, macroeconomics, production, use, economic potential, statistical analysis, classification

SIMPLIFY AND IMPROVЕ: REVISITING BULGARIA’S REVENUE FORECASTING MODELS

In the thirty years since the end of real socialism, Bulgaria has gone from having a rather radically ‘different’ tax system to adopting flat-rate taxation with marginal tax rates falling from figures as high as 40% to 10% for both the corporate and personal income tax. Crucially, the econometric forecasting models in use at the Bulgarian Ministry of Finance hinted at an increase in tax revenue compatible with the so-called ‘Laffer curve’. Similarly, many economists held the view that revenues should increase. However, reality fell short of those expectations based on forecasting models and rooted in mainstream economic theory. Thus, this paper asks whether there are better-performing forecasting models for personal and corporate income tax revenues in Bulgaria that are readily implementable and overperform the ones currently in use. After articulating a constructive critique of the current forecasting models, the paper offers readily implementable, transparent alternatives and proves their superiority.

Prospects for the Development of Foreign Trade of Kazakhstan with the Countries of the Regional Comprehensive Economic Partnership

The study is devoted to the study of the prospects for the development of Kazakhstan’s foreign trade with the countries of the Comprehensive Regional Economic Partnership (CREP) by analyzing the indicators of the openness of the economy and the use of econometric forecasting models. The relevance of the work is due to the need to consider the prospects for the development of Kazakhstan’s foreign trade relations with the new trade association CREP for further formation of directions for the development of such relations based on the principles of evidence-based policy, since the results obtained in the study can serve as a basis for making public management decisions in the field of trade and customs policy. Within the framework of the article, the authors applied a strategy of combined research methods with a comparative analysis of the results of using each of them. Thus, econometric methods of Box-Jenkins forecasting, exponential smoothing with the use of tests to check their adequacy and validity were used. Forecast errors are calculated, such as the root-mean-square error, the average absolute error and the average absolute error as a percentage. The study resulted in forecasts of Kazakhstan’s foreign trade turnover with the CREP countries for the short term, which allowed us to conclude about the positive dynamics of the development of these trade relations. A more accurate forecast is presented based on the use of the exponential smoothing method, which demonstrates the smallest error size and is easier to apply.

Extending Macroeconomic Impacts Forecasting for NEMS

To comprehensively model the macroeconomic impacts that result from changes in long-term energy-economy forecasts, the United States (U.S.) Department of Energy’s National Energy Technology Laboratory (NETL) partnered with West Virginia University (WVU)’s Regional Research Institute to develop the NETL/WVU econometric input-output (ECIO) model. The NETL/WVU ECIO model is an impacts forecasting model that functions as an extension of the U.S. energy-economic models available from the U.S. Energy Information Administration’s National Energy Modeling System (NEMS) and the U.S. Environmental Protection Agency’s Market Allocation (MARKAL) model. The ECIO model integrates a macroeconomic econometric forecasting model and an input-output accounting framework along derived forecast scenarios detailing a baseline of the U.S. ener-gy-economy and an alternative forecast on how power generation resources can meet future levels of energy demand to generate estimates of the impacts to gross domestic product, employment, and labor income. This manuscript provides an overview of the model design, assumptions, and standard outputs.

Econometric Forecasting models for Air Freight in Indonesia (And How Will It be Affected by COVID-19?)

Forecasting using the economic factor as indicators seems significant related. The experts also suggest predict the number of traffic in transport by approaching the econometric models. This paper predicting the upcoming number of air freight until 2030. The result shows GDP is significant related to number of air freight. However, the economic crisis also contributes to decreasing the value. Also, the Covid-19 impact the economic in the country. Predicting the number of air freight in 2020 is going down. The number seems gradually grow after two years and assume the pandemic is over soon

PKR Exchange Rate Forecasting Through Univariate and Multivariate Time Series Techniques

This study aims to examine and compare the accuracy of time series and econometric forecasting models in the context of the exchange rate as we know that fluctuation in the exchange rate may affect the economic activities at the macro – level. For this purpose, the author has chosen the Pakistani Rupee exchange rate against United States Dollars with the annual data from 1980 to 2018. The results revealed that the exponential model provides the most effective accuracy in forecasting rather than the Naive, ARIMA and ARDL Co-integration model. This paper has also covered the gap of unavailability of literature regarding the application of ARDL and Exponential Smoothing model for the forecasting of the exchange rate in Pakistan. It is also anticipated that historical data do not play a vital role in the forecasting of the future trend of time series i.e. Pakistani Rupees against US Dollars. However, all three-time series anticipated that the recent observations play a significant role in the speculation of the upcoming future trend.
 Keywords: Forecasting, Exchange Rate, Naïve Model, ARDL Co-Integration model, Econometrics

Review of tourism forecasting research with internet data

Internet techniques significantly influence the tourism industry and Internet data have been used widely used in tourism and hospitality research. However, reviews on the recent development of Internet data in tourism forecasting remain limited. This work reviews articles on tourism forecasting research with Internet data published in academic journals from 2012 to 2019. Then, the findings ae synthesized based on the following Internet data classifications: search engine, web traffic, social media, and multiple sources. Results show that among such classifications, search engine data are most widely incorporated into tourism forecasting. Time series and econometric forecasting models remain dominant, whereas artificial intelligence methods are still developing. For unstructured social media and multi-source data, methodological advancements in text mining, sentiment analysis, and social network analysis are required to transform data into time series for forecasting. Combined Internet data and forecasting models will help in improving forecasting accuracy further in future research. • A comprehensive review on tourism forecasting with Internet data is conducted. • We synthesize findings based on search engine, web traffic, social media, and multi-source data. • Search engine data are most widely incorporated into tourism forecasting. • Methodological advancements are required to address the unstructured social media and multi-source data.

Merging macroeconomic and territorial determinants of regional growth: the MASST4 model

While prior econometric forecasting models focus on either macroeconomic or territorial aspects as drivers of regional growth, the fourth version of the MAcroeconomic, Sectoral, Social, Territorial (MASST4) model merges these two conceptual approaches to regional growth. Mechanisms of territorial complexity governing regional development theories, like agglomeration economies, or structural changes in innovation modes, are included into a formal model so that they simultaneously activate regional growth and mediate macroeconomic growth impacts. Moreover, a longer time series than in previous versions of the MASST model now allows to take account of the structural changes taking place in EU economies as a consequence of the recent crisis. The model now also models the effects of the decrease in EU integration stemming from populistic waves in politics taking place in EU countries. The paper also presents an application of the MASST model to a reference scenario.

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19.

Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Earlier detected in Wuhan, Hubei province, in China in December 2019, the deadly virus engulfed China and some neighboring countries, which claimed thousands of lives in February 2020. The proposed hybrid methodology involves the application of discreet wavelet decomposition to the dataset of deaths due to COVID-19, which splits the input data into component series and then applying an appropriate econometric model to each of the component series for making predictions of death cases in future. ARIMA models are well known econometric forecasting models capable of generating accurate forecasts when applied on wavelet decomposed time series. The input dataset consists of daily death cases from most affected five countries by COVID-19, which is given to the hybrid model for validation and to make one month ahead prediction of death cases. These predictions are compared with that obtained from an ARIMA model to estimate the performance of prediction. The predictions indicate a sharp rise in death cases despite various precautionary measures taken by governments of these countries.

Comparison of Econometric Models and Artificial Neural Networks Algorithms for the Prediction of Baltic Dry Index

The shipping market, a major component of the global economy, is characterized by high risk and volatility. The Baltic dry index is an influential indicator in the world shipping market and international trade. Several studies have used a variety of techniques to generate Baltic dry index predictions. The most prominent techniques utilize either econometric or artificial intelligence computing. We compare the forecasting accuracy of two typical univariant econometric models and three artificial neural networks (ANNs)-based algorithms. We find that when using daily data, econometric forecasting models produce better one-step-ahead predictions than ANN-based algorithms. When forecasting weekly and monthly data, ANN-based algorithms produce fewer errors and a higher direction matching rate than econometric models. We also compare the predictive power of a number of different models when applied to the 2008 financial crisis and find that the generalized autoregressive conditional heteroskedasticity model and the back propagation neural network algorithm produce the best one-step-ahead and seven-steps ahead predictions, respectively.

Economically Incentivising Smart Urban Regeneration. Case Study of Port Louis, Mauritius

Port Louis, the capital city of Mauritius, has been the preferred city for hosting the judicial, political and business activities of the country for the past two centuries. However, new policies have created nine new smart cities in greenfield locations within 10 km from Port Louis, so the capital city is facing economic decline as it is losing businesses, as well as administrative functions. This loss equates to an erosion in municipal revenue along with a reduced interest in contributing to the development of the city; all of which takes a toll on its urban economic landscape, as well as on the broader Mauritian economy. This paper builds from the findings of a focus group study to propose a smart urban regeneration model for the City of Port Louis, which could enable the old city to be restored and regenerated rather than redeveloped in modernist architecture, as has happened in the new smart cities model. A smart urban regeneration model is proposed backed by the pillars of smart infrastructure, culture, metabolism and governance. The proposed model is applied to the context of Port Louis to generate an urban regeneration scheme. The potential benefits in terms of financial outcomes, investment attraction and job creation are explored through a combined application of econometric forecasting models. The results support positive figures of both investment and job creation, and the findings of this study aim at informing and providing the governing bodies of Port Louis with a tangible solution for revamping the centuries-old capital city, as well as demonstrating to the world that smart cities can mean sensitive urban regeneration.

ФІНАНСОВА ПРИРОДА КУРОРТНОГО МУЛЬТИПЛІКАТОРА

У статті проаналізовано еволюцію концептуальних підходів до проведення аналізу й оцінювання мультиплікативного ефекту курортно-рекреаційного туризму. Побудовано сценарії розвитку курортно-рекреаційних кластерів на найближче десятиліття. Встановлено, що поєднання концептуальних підходів до оцінювання мультиплікативного ефекту та результатів багатофакторного моделювання впливу курортно-рекреаційного туризму на валовий регіональний продукт формує методологічні засади обґрунтування національної стратегії державного управління курортно-рекреаційною сферою.

OIL PRICE FORECASTS FOR THE LONG TERM: EXPERT OUTLOOKS, MODELS, OR BOTH?

Little is known about the accuracy of expert outlooks, so heavily relied upon by industry participants and policy makers, regarding the future path of oil prices. Using the regular publications by the Energy Information Administration (EIA), we examine the accuracy of annual recursive oil price forecasts generated by the National Energy Modeling System model of the Agency for forecast horizons of up to 15 years. Our results reveal that the EIA model outperforms the benchmark random walk model around the two ends of the forecast horizon spectrum. Additionally, at the longer horizons, simple econometric forecasting models often produce similar, if not better accuracy than the EIA model. Time varying such specifications generally also exhibit stability in their forecast performance. Finally, although combining forecasts does not change the overall patterns, some additional accuracy gains are obtained at intermediate horizons, and in some cases, forecast performance stability is also achieved.

Forecasting of the electricity price on the day-ahead electricity market in Russia

AbstractAfter analyzing the characteristics and pricing models on the Russian wholesale electricity market, some important features for econometric modeling are introduced. This paper suggests econometric forecasting models developed to predict daily and hourly electricity prices on the day-ahead market for two price zones in Russia: European and Siberian ones. A set of 24 models, which are similar in nature but different in included regressors, are introduced. On the basis of the actual database for 2014, different modifications of price formation are offered and analyzed with the help of the Eviews econometric package. Dynamic forecasts on various distances (day, week, and month) are conducted and the most suitable models from the point of minimizing the norms of the vectors residuals are chosen. Constructed ARMA models have high predictive power and are able to reflect the price trend on the base of exogenous factors and the previous price values.

Forecasting of the Electricity Price on the Day-Ahead Electricity Market in Russia

After analyzing the characteristics and pricing models on the Russian wholesale electricity market, some important features for econometric modeling are introduced. This paper suggests econometric forecasting models developed to predict daily and hourly electricity prices on the day-ahead market for two price zones in Russia: European and Siberian ones. A set of 24 models, which are similar in nature but different in included regressors, are introduced. On the basis of the actual database for 2014 different modifications of price formation are offered and analyzed with the help of the E-views econometric package. Dynamic forecasts on various distances (day, week, and month) are conducted and the most suitable models from the point of minimizing the norms of the vectors residuals are chosen. Constructed ARMA models have high predictive power and are able to reflect the price trend on the base of exogenous factors and the previous price values.

The Structure of a Machine-Built Global MacroForecasting System

This article presents an automated forecasting system that provides a macroeconomic forecasting approach that some hedge funds may find useful. The authors describe the structure of an econometric forecasting system designed to produce multiequation econometric forecasting models of national macroeconomies. They also describe the functioning of an automatic model-building system that builds the forecasting equation for each series submitted and produces forecasts of the series without human intervention. The automatic model-building system employs information criteria and cross-validation in the equation building process, and it uses Bayesian model averaging to combine forecasts of individual series. The system outperforms standard benchmarks for a variety of macroeconomic datasets. To demonstrate its use, the automatic system is used to build a fixed-income macro trading system.

Imperfect knowledge, liquidity and bubbles

Insufficient liquidity can lead to substantial movements in asset prices. There is a single asset traded in a centralized market that facilitates exchange in decentralized trade. If the asset is in short supply the price includes a liquidity premium. Traders have imperfect knowledge about future asset prices and estimate, in real-time, an econometric forecasting model. A permanent decrease in the supply of assets, or an increase in collateral requirements, can lead to over-shooting of the price. When price includes a liquidity premium there can be recurrent bubbles and crashes. Liquidity and adaptive learning play key roles in fitting the empirical distribution of price–dividend ratios.

Previsão do volume exportado para a fruticultura brasileira via análise de séries temporais: uma abordagem ARIMA/GARCH

O objetivo deste trabalho foi propor modelos de previsão econométricos para o volume exportado da fruticultura brasileira, com vistas a auxiliar o planejamento e controle de sua produção, motivado também pela constatação de poucos estudos publicados tratando desse tema. Nesse sentido, empregou-se os modelos ARIMA/GARCH, considerando-se, outrossim, a ocorrência de uma sazonalidade estocástica multiplicativa nessas séries históricas. Foram coletadas 300 observações de peso líquido (kg) exportado entre jan/1989 a dez/2013 das seguintes frutas: abacaxi, banana, laranja, limão-lima, maçã, mamão, manga, melancia, melão e uva, cujo critério de seleção foi a sua importância na pauta de exportação frutícola, pois representavam 97% do total de dólares recebidos e 99% do volume vendido em 2010, de uma população de 28 tipos de frutas exportadas. Os resultados indicam que só não foi observada a existência de sazonalidade multiplicativa de 12 meses na banana e na manga. Por outro lado, foram identificadas dois grupos de frutas: (1) as que são exportadas continuamente, e (2) as que possuem picos de exportação. Sobre a qualidade dos modelos de previsão, eles foram considerados satisfatórios para seis das dez frutas analisadas. Quanto à volatilidade, verificou-se uma alta persistência das séries da banana e do mamão, apontando para a existência de uma quebra estrutural na série de dados, que pode estar associada às crises econômicas ocorridas nos últimos 17 anos.

Oil Price Forecasts for the Long-Term: Expert Outlooks, Models, or Both?

Expert outlooks on the future path of oil prices are often relied on by industry participants and policymaking bodies for their forecasting needs. Yet little attention has been paid to the extent to which these area accurate. Using the regular publications by the Energy Information Administration (EIA), we examine the accuracy of annual recursive oil price forecasts generated by the National Energy Modeling System model of the Agency for forecast horizons of up to 15 years. Our results reveal that the EIA model is quite successful at beating the benchmark random walk model, but only at either end of the forecast horizons. We also show that, for the longer horizons, simple econometric forecasting models often produce similar if not better accuracy than the EIA model. Among these, time-varying specifications generally also exhibit stability in their forecast performance. Finally, while combining forecasts does not change the overall patterns, some additional accuracy gains are obtained at intermediate horizons, and in some cases forecast performance stability is also achieved.