Econometric Forecasts Research Articles

A General Framework for Time Series Forecasting Model Using Autoregressive Integrated Moving Average-ARIMA and Transfer Functions

Major current econometric stochastic series forecast research are established on the failure of the scholastic process tests to differentiate between finite and stationary alternative samples of the unit root hypothesis results. The importance of forecast evaluation allows researchers to reasonably monitor and improve forecast performance. While a structured improved forecast framework have often been suggested as one possible alternative, an extended the multivariate model which incorporate distributed-lag period for independent variable gives a unique advantage over the traditional distributed-lag model and the mathematical formulation does essentially guarantee that predicated equation irrespective of the values of the predictor variables. Hence, the primary objective is mainly to determine the likelihood of autoregressive integrated moving average (ARIMA) method for practicable process choice used for predicting key economic variables for a set of market data. Once the process has been known, parameters have been obtained, and the adequacy of the model has been determined, forecasts can be checked for reliability.

Forecasting banking sectors in Indian stock markets using machine intelligence

The study analyze stock index closing from myriad set of technical and fundamental analysis variables extracted from real market data to assist forecast of market closing. For this, major service sector indices of Bombay stock exchange (BSE) and National stock exchange (NSE) with historical data we re taken from banking industry. The predictive model performance of index closing using statistical procedures like automatic linear modeling, time-series based econometric forecasting, vector auto regression as with artificial neural network based models were simulated and analyzed. The results indicate that BSE had higher forecast accuracy using autoregressive models and market volatility factor had major influence. Whereas, NSE was impacted by quarterly performance that can be modeled using neural networks. The empirical results were contrasted with latest state-of-art research theories to provide agenda and future research challenges of market forecast systems.

Long-term forecasting of El Niño events via dynamic factor simulations

We propose a new forecasting procedure which particularly explores opportunities for improving the precision of medium and long-term forecasts of the Niño3.4 time series that is linked with the well-known El Niño phenomenon. This important climatic time series is subject to an intricate dynamic structure and is interrelated to other climatological variables. The procedure consists of three steps. First, a univariate time series model is considered for producing prediction errors. Second, signal paths of the prediction errors are simulated via a dynamic factor model for the errors and explanatory variables. From these simulated errors, ensemble time series for Niño3.4 are constructed. Third, forecasts are generated from the ensemble time series and their sample average is our final forecast. As part of these dynamic factor simulations, we also obtain the forecast of the El Niño event which is a categorical variable. We present empirical evidence that our procedure can be superior in its forecasting performance when compared to other econometric forecasting methods.

Metropolitan Hotel Sector Forecast Accuracy in El Paso

This study evaluates the accuracy of previously published econometric forecasts for seven lodging sector variables that measure hotel activity in El Paso, Texas. The hotel forecasts have been generated annually using an econometric model of the El Paso metropolitan economy from 2006 forward. Predictive accuracy is evaluated relative to random walk benchmarks. Assessment is completed using both descriptive forecast error summary statistics as well as formal statistical tests. The econometric model outperforms the random walk benchmarks for a majority of the variables analyzed. However, statistical tests of forecast error differentials do not yield conclusive evidence in favor of the econometric historical track record. Tests of directional forecast accuracy also produce mixed results. Although the structural econometric model of hotel business conditions appears to provide useful predictive information, analysts and planners should also monitor recent history closely.

Assessing the accuracy of electricity production forecasts in developing countries

This study assesses the accuracy of time series econometric methods for forecasting electricity production in developing countries. An analysis of the historical time series for 106 developing countries over the period 1960–2012 demonstrates that econometric forecasts are highly accurate for the majority of these countries. These forecasts have much smaller errors than the predictions of simple heuristic models, which assume that electricity production grows at an exogenous rate or is proportional to the real GDP growth. However, the quality of the forecasts diminishes for the countries and regions, where rapid economic and structural transformation makes it difficult to establish stable historical production trends.

THE RESILIENCE OF REGIONS TO ECONOMIC RECESSION: THE ANALYSIS OF EMPLOYMENT TRENDS

The aim of the paper is to identify the reactions of Polish regions to the negative macroeconomic shocks triggered by the global economic crisis dated for 2008–2010. In the course of the research, econometric forecasting and statistical hypothesis testing methods were used. The research was based on the analysis covering dynamics of increase in the employment using the indicator of employed persons in enterprise sector (as a fixed-based index of dynamics). The findings showed that the crisis of the end of the first decade of the 21st century had different course and impact on regional economies in Poland. Negative impact of the breakdown on regional labour markets prevailed, while positive outcome (a new, more dynamic growth rate of employment) was recorded in only one region.

Cluster Effect of International Transport Routes

An econometric forecast to 2025 of the volume of container shipments along the Primor’e-1 and Primor’e-2 transport routes reveals the preconditions for the formation of transborder transport and logistics clusters in northeast Asia. The article argues that international transport corridors are important for inclusion of the economic entities of the Primorsky Krai in the international system of intermodal and multimodal shipping services. In addition, it concludes that the implementation of comprehensive projects is important for the integration of Russian and global multimodal operators and also for the creation of transport and logistics centers in the border regions of the Primorsky Krai.

Economic growth, inflation, investments and incomes in the Russian Federation: Econometric model-based analysis and forecast

Economic growth, inflation, investments and incomes in the Russian Federation: Econometric model-based analysis and forecast

Heavy Moving Averages and Their Application in Econometric Forecasting

ABSTRACTThis paper presents the heavy ordered weighted moving average (HOWMA) operator. It is an aggregation operator that uses the main characteristics of two well-known techniques: the heavy ordered weighted averaging (OWA) and the moving averages. Therefore, this operator provides a parameterized family of aggregation operators from the minimum to the total operator and includes the OWA operator as a special case. It uses a heavy weighting vector in the moving average formulation and it represents the information available and the knowledge of the decision maker about the future scenarios of the phenomenon, according to his attitudinal character. Some of the main properties of this operator are studied, including a wide range of families of HOWMA operators such as the heavy moving average and heavy weighted moving average operators. The HOWMA operator is also extended using generalized and quasi-arithmetic means. An example concerning the foreign exchange rate between US dollars and Mexican pesos is also presented.

Population forecast for Poland based on econometric forecasts of migration flows

The aim of this article is to present the results of a population forecast for Poland for the years 2015—2060, based on an econometric model of migration flows. This approach differs from existing forecasts and projections in three significant ways: the projected population are residents, the four migration flows (i.e. inflow and outflow divided into foreigners and Polish citizens) are estimated with econometric tools and the uncertainty of the obtained estimates is taken into account (in a formal manner). The results obtained indicate consistency of the applied econometric models of migration flows with theories of migration. Immigration to Poland is expected to intensify during the next four decades. Depopulation as well as ageing are expected nevertheless, although the pace of these two phenomena will be slower than predicted in other demographic analyses.

Optimal Portfolio Strategy for Risk Management in Toll Road Forecasts and Investments

The study leveraged modern portfolio theory and stochastic time series models to develop a risk management strategy for future traffic projections along brownfield toll facilities. Uncertainty in future traffic forecasts may raise concerns about performance reliability and revenue potential. Historical time series traffic data from brownfield corridors were used for developing econometric forecast estimates, and Monte Carlo simulation was used to quantify a priori risks or variance to develop optimal forecasts by using mean-variance optimization strategies. Numerical analysis is presented with historical toll transactions along the Massachusetts Turnpike system. Suggested diversification strategies were found to achieve better long-term forecast efficiencies with improved trade-offs between anticipated risks and returns. Planner and agency forecast performance expectations and risk propensity are thus jointly captured.

Structural Breaks and Future Growth of the Green Industry

Every industry undergoes changes, but structural changes can abruptly and radically alter business for many firms in that industry. Identifying the substantial shifts in the green industry with regard to consumer spending can help the industry better understand its history. Using an econometric model of that same consumer spending data to look forward to the future can show firms what might lie ahead. We analyzed the personal consumption expenditures for two items measured by the U.S. Bureau of Economic Analysis to identify structural breaks in the green industry. We then conducted an econometric forecast using that same data to predict future consumer spending projections to the year 2020. To accomplish this, we analyzed household expenditures for Tools (including gardening tools and equipment) and Plants (including seeds, flowers, and plants) from 1959 to 2014. We identified one industry structural break using the Schwarz criteria for Tools in 2006 and another one for Plants in 2007 and (separately) identified four breaks using Bayesian Information criteria: one break for Tools in 2006 and three breaks in Plants in 1986, 2003, and 2008. The potential causes of these breaks are discussed (e.g. housing bubble, financial market stress). Lastly, we employed an econometric model to forecast spending and show that it will grow from $65.15 and $86.52 in 2015 to $71.17 and $96.97, for Tools and Plants respectively, in 2020.

Forecasting the LIBOR‐Federal Funds Rate Spread During and After the Financial Crisis

In this paper, we examine the point and density forecast accuracy of econometric models, surveys and futures rates in predicting the LIBOR‐Federal Funds Rate (LIBOR‐FF) spread during and after the financial crisis. We provide evidence that the futures market forecast outperforms all competing forecasts during and after the financial crisis and that its predictive density is well calibrated. Our results also suggest that the predictive accuracy of the econometric models improves in the post‐crisis period. We argue that the post‐2009 improvement in the econometric models' forecasts is attributable to the absence of LIBOR manipulation. The economic significance of the uncovered predictability is assessed using a trading strategy. Our results suggest that trading based on the futures market and econometric forecasts generates positive risk‐adjusted returns. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:345–374, 2016

Hedge Fund Flows and Performance Streaks: How Investors Weigh Information

We examine the relative weights hedge fund investors attach to past information in the fund selection process. The weighting scheme appears inconsistent with the one of econometric forecast models that predict fund returns, alphas or Sharpe ratios. In particular, investor flows are highly sensitive to performance streaks despite their limited predictive power regarding fund performance. Further, allocations based on forecast models' out-of-sample predictions beat investors allocations by a significant margin, which suggests that the latter are suboptimal and reflect overreaction to certain types of information. Our findings do not support the notion that sophisticated investors have superior information or superior information processing abilities.

Comment on "A New Procedure for Selecting and Ranking Ground-Motion Prediction Equations (GMPEs): The Euclidean Distance-Based Ranking (EDR) Method" by Ozkan Kale and Sinan Akkar

The evaluation of ground‐motion prediction equations (GMPEs) has recently been a popular activity (Bindi et al. , 2006; Drouet et al. , 2007; Hintersberger et al. , 2007; Stafford et al. , 2008; Delavaud et al. , 2009; Douglas and Mohais, 2009; Scasserra et al. , 2009; Shoja‐Taheri et al. , 2010; Kaklamanos and Baise, 2011; Arango et al. , 2012; Beauval et al. , 2012; Delavaud et al. , 2012; Mousavi et al. , 2012; Vilanova et al. , 2012). Evaluating the performance using scores has the advantage, compared with other techniques like residual analysis, of simplifying model comparison. Using scores to select models has been a routine activity in some fields of decision sciences, such as econometric forecasting (Armstrong, 2001) and weather forecasting (Jolliffe and Stephenson, 2012). Kale and Akkar (2013) briefly reviewed the scores used in GMPE evaluations and proposed a new score called Euclidean distance‐based ranking (EDR). Their score was considered by a subsequent evaluation study on GMPEs designed for induced earthquakes (Edwards and Douglas, 2013). Here, we point out that their score is not strictly proper (defined below) and will lead to erroneous results under some conditions. It is a principle in decision sciences to avoid using improper scores. Developers of …

Accuracy and bias of experts’ adjusted forecasts

This study investigates whether experts’ group-based judgmental adjustments to econometric forecasts of tourism demand improve the accuracy of the forecasts and whether the adjusted forecasts are unbiased. The Delphi method was used to aggregate experts’ judgmental adjustments and a range of error measures and statistical tests were employed to evaluate forecast accuracy. Regression analysis was used to investigate whether the statistical and judgmentally-adjusted forecasts were unbiased. The hypothesis tests suggested that, on average, the adjustments of the Delphi panel improved forecast accuracy though the group-adjusted forecasts were found to be biased for some of the individual markets. In-depth interviews with the Delphi panellists provided further insights into the biases that were associated with the Delphi surveys.

The future of global trade and the WTO

Purpose – The global governance of trade is in a deadlock and the WTO is suffering from a long standing crisis of legitimacy. This paper aims to analyse the main issues which might influence the governance of world trade from now until 2030 and present quantitative projections of international trade. Design/methodology/approach – The research on the main issues which might influence the governance of world trade from now until 2030 draws on a detailed analysis of the WTO and trade policies. Four scenarios of the world economy are presented, which are derived from the international AUGUR research project “Challenges for Europe in the world in 2030” coordinated by Paris Nord University. The analysis takes into account econometric forecasting of world trade conducted in the framework of this project. Findings – First, the failure of the Doha Round of multilateral trade negotiations to reach its ambitious agenda derives from the discrepancy between the governance of world trade and the new power relationship prevailing in the world economy, with new emerging powers (China, India, etc.) rapidly increasing their share of world trade. Second, the continuous restructuring of world trade and economy, which goes together with new forms of globalization, will increase pressure for a profound reform of the governance of world trade in the next few years. Research limitations/implications – This paper calls for a reform of world trade governance, especially of the missions of WTO within a renovated economic world order. Future research could investigate more deeply the potential for regional trade integration, which is reinforced by international production networks. Regional trade agreements might be an increasing alternative to multilateral trade agreements. Originality/value – This paper brings new ideas by raising the issue of the governance of world trade using a prospective approach, with the aim to identify the key channels through which international trade integration will impact the world economy. This study bases its analysis on potential scenarios from now on until 2030, each of these scenarios corresponding to a specific institutional configuration.

Перспективы и эффективность экономической деятельности в современной России

The demography of the organizations operating in national economy is
 investigated. The indicator of growth of number of the organizations is entered.
 It is established that growth rates of number of the organizations decrease. The
 econometric forecast which shows prospects reduction of number of the
 organizations and business in the country since 2014 accruing rates is executed.
 The assessment of average cost of the capital in the country during 2005–2011
 is received. Profitability of own capital on economic profit (EVA) of set of the
 organizations on economy branches during 2005–2011 is calculated. It is
 established that the majority of kinds of activity from the economic point of view
 are unprofitable. After a world economic crisis efficiency of activity of the
 organizations of the majority of branches increased only in 2010. In 2011 their
 efficiency began to decrease again. It allows considering limited prospects of
 development of economic activity and business in the country.

Air mobility of people and airport growth potential in regions of Russia

AbstractThe article is about Russian regional airports, air mobility of people in regions of Russia, geographical distribution and results of econometric forecasting of these figures based on demographic and economic factors. Also some historical reviews, a description of the current situation and international comparisons are provided. The research revealed that people’s income and investment (both current and accumulated) are the main factors affecting air transportation growth in the studied airports. In the long-term perspective air traffic of Russian airports can triple by 2030, while Russian air mobility figures can exceed the present ones in Western Europe.

Econometric Forecasting of Irrigation Water Demand Conserves a Valuable Natural Resource

Natural causes (such as droughts), non-natural causes (such as competing uses), and government policies limit the supply of water for agriculture in general and irrigating crops in particular. Under such reduced water supply scenarios, existing physical models reduce irrigation proportionally among crops in the farmer's portfolio, disregarding temporal changes in economic and/or institutional conditions. Hence, changes in crop mix resulting from expectations about risks and returns are ignored. A method is developed that considers those changes and accounts for economic substitution and expansion effects. Forecasting studies based on this method with surface water in Georgia and Alabama demonstrate the relative strength of econometric modeling vis-à-vis physical methods. Results from a study using this method for ground water in Mississippi verify the robustness of those findings. Results from policy-induced simulation scenarios indicate water savings of 12% to 27% using the innovative method developed. Although better irrigation water demand forecasting in crop production was the key objective of this pilot project, conservation of a valuable natural resource (water) has turned out to be a key consequence.