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Journal articles on the topic 'Macroeconomics – Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Chatziantoniou, Ioannis, Stavros Degiannakis, Bruno Eeckels, and George Filis. "Forecasting tourist arrivals using origin country macroeconomics." Applied Economics 48, no. 27 (December 29, 2015): 2571–85. http://dx.doi.org/10.1080/00036846.2015.1125434.

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2

Maldonado, Isabel, and Carlos Pinho. "Yield curve dynamics with macroeconomic factors in Iberian economies." Global Journal of Business, Economics and Management: Current Issues 10, no. 3 (November 26, 2020): 193–203. http://dx.doi.org/10.18844/gjbem.v10i3.4691.

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Abstract The aim of this paper is to analyse the bidirectional relation between the term structure of interest rates components and macroeconomic factors. Using a factor augmented vector autoregressive model, impulse response functions and forecasting error variance decompositions we find evidence of a bidirectional relation between yield curve factors and the macroeconomic factors, with increased relevance of yield factors over it with increased forecasting horizons. The study was conduct for the two Iberian countries using information of public debt interest rates of Spain and Portugal and macroeconomic factors extracted from a set of macroeconomic variables, including indicators of activity, prices and confidence. Results show that the inclusion of confidence and macroeconomic factors in the analysis of the relationship between macroeconomics and interest rate structure is extremely relevant. The results obtained allow us to conclude that there is a strong impact of changes in macroeconomic factors on the term structure of interest rates, as well as a significant impact factors of the term structure in the future evolution of macroeconomic factors.

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3

Diebold, F. X., and Kenneth D. West. "Forecasting and empirical methods in finance and macroeconomics." Journal of Econometrics 105, no. 1 (November 2001): 1–3. http://dx.doi.org/10.1016/s0304-4076(01)00067-7.

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4

Li, Cao. "Macroeconomic Short-Term High-Precision Combined Forecasting Algorithm Based on Grey Model." Security and Communication Networks 2021 (September 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/7026064.

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Using the characteristics of grey forecasting, which requires a small amount of sample data and a simple modeling process, to predict the main macroeconomic indicators in the early stage, combined with the filtering decomposition method and the production function method, establishes a short-term high-precision combination forecasting algorithm for macroeconomics based on the grey model. The algorithm uses the improved HP filter method in the HP filter method to study whether the potential economic growth rate can be more accurately measured, and the production function method is used to calculate the potential economic growth rate. First, the two methods are used to calculate the potential economic growth rate. The accuracy of this method finally established a combined model based on the two models for short-term forecasting. Under the premise of considering economic factors, the input data is preprocessed, and the high-precision combined forecast is used to finally obtain the macroeconomic forecast results. The calculation examples in the paper show that the method is feasible and effective.

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Kurovskiy, Gleb. "Using Textual Information to Predict In Macroeconomics." Moscow University Economics Bulletin 2019, no. 6 (December 30, 2019): 39–58. http://dx.doi.org/10.38050/01300105201965.

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The paper shows how textual information can be used to predict and study cause-effect relationships in macroeconomics. I consider a special case of forecasting - nowcasting on the example of unemployment. The key feature of nowcasting is that the forecast is built for a period that has already passed, but which has not yet come out statistics. As textual information, Internet requests are used. The paper is new in several direction. For the first time in the literature, information from two search engines, Yandex and Google, is used at once for forecasting. Information provided by search engines complements each other and allows performing suitable words’ selection from the bunch of users’ internet-requests. For the first time, the popularity of online systems as sources of information on job availability is taken into account. In Russia, the popularity of the Internet as a source of information on the availability of jobs has more than tripled from 2008 to 2018. If the researcher uses only the dynamics of related internet-requests then the results will show the dynamics of internetservices’ popularity rather than unemployment. Most of the models with internet query words show significant quality improvement in fore(now)casting unemployment. The paper proposes the procedure how to use query data for macroeconomic nowcasting

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6

Syamsudin, Moch. "Pengujian Kembali Volatilitas Kebijakan Trilemma Terhadap Variabel Makroekonomi di Indonesia." Jurnal Ekonomi Akuntansi dan Manajemen 20, no. 1 (April 8, 2021): 1. http://dx.doi.org/10.19184/jeam.v20i1.18880.

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The trilemma policy is a hypothesis stating a Mundell-Fleming macroeconomic development framework in which there is a state that cannot simultaneously choose three policies because it must sacrifice one policy so that the realization of policies that leads to economic stability is desired. The research aims to see the effect of the policy volatility on macroeconomic variables in Indonesia. The method used is the vector error correction model (VECM). The results show that the volatility of the trilemma policy adopted by Indonesia in the short and long term Affects the rate of economic growth and inflation. Economic shocks and uncertainties in the world economy externally affect macroeconomic variables. Viewing the results of forecasting for the trilemma policy and macroeconomic variables show that the inflation rate is so high and the level of economic openness is very low. This result recommends that there is a need for harmonization of policies undertaken by Bank Indonesia as the monetary authority and the government as a fiscal authority so as to achieve the level of financial stability that impacts on economic stability. Keywords: Trilemma Policy, Macroeconomics, Vector Error Correction Model (VECM), Forecasting

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7

Ahmadi, Ahmadi, and R. Adisetiawan. "Multivariate Time Series in Macroeconomics." Eksis: Jurnal Ilmiah Ekonomi dan Bisnis 11, no. 2 (November 23, 2020): 151. http://dx.doi.org/10.33087/eksis.v11i2.209.

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Gold is one of the most popular commodities and investment alternatives. Gold prices are thought to be influenced by several other factors such as the US Dollar, oil price, inflation rate, and stock exchange so that gold price modeling is not only influenced by its own value. This research was conducted to determine the best forecasting model and to find out what factors influence the price of gold. This research modeled the price of gold in a multivariate and reviewed the univariate modeling that will be used as a comparison model of multivariate modeling. Univariate modeling is done using ARIMA model where the modeling results state that gold price fluctuations as white noise. Multivariate gold price modeling is done using Vector Error Correction Model with gold, oil, US Dollar and Dow Jones indices, and inflation rate as predictors. The results showed that the VECM model has been able to model the gold price well and all the factors studied influenced the gold price. The US dollar and oil prices are negatively correlated with gold prices, while the inflation rate is positively correlated with gold prices. The Dow Jones index was positively correlated with gold prices in just two periods.

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8

Fischer, Charles C. "On the Design and Use of Forecasting Experiments in Teaching Macroeconomics." Simulation & Gaming 22, no. 1 (March 1991): 75–82. http://dx.doi.org/10.1177/1046878191221006.

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9

Britton, Andrew. "Seasonal Patterns in the British Economy." National Institute Economic Review 117 (August 1986): 33–42. http://dx.doi.org/10.1177/002795018611700105.

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Applied macroeconomists commonly regard the seasonal variations of the economy as a hindrance rather than a help to the understanding of behaviour. Thus both in commenting informally on economic developments and in the more formal tasks of modelbuilding and forecasting seasonally-adjusted data are almost invariably used in preference to raw data when both are published. The nature of the patterns displayed by seasonal variation is very little discussed. One purpose of this note is simply to describe seasonal variation as it is estimated in some of the official data series, providing some tables which may be useful for general reference. But the aim is not just descriptive. It will be argued that seasonal variations may throw useful light on some controversial issues in macroeconomics.

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10

Diebold, Francis X., and Kenneth D. West. "Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance: Editors' Introduction." International Economic Review 39, no. 4 (November 1998): 811. http://dx.doi.org/10.2307/2527339.

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11

Cicceri, Giovanni, Giuseppe Inserra, and Michele Limosani. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study." Mathematics 8, no. 2 (February 13, 2020): 241. http://dx.doi.org/10.3390/math8020241.

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In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.

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12

Toroptsev, E. L., A. S. Marakhovskii, and R. R. Duszynski. "Intersectoral modeling of transients." Economic Analysis: Theory and Practice 19, no. 3 (March 30, 2020): 564–85. http://dx.doi.org/10.24891/ea.19.3.564.

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Subject. The article considers structural transition processes in macroeconomics. Objectives. The aim is to present our own concept and mathematical tools to study structural transitions in macroeconomics. Dynamic inter-industry balance enables to formalize the problem in the form of a Koshi task for ordinary differential equations. Methods. The methodology components include the basics of inter-industry and numerical analysis and modeling of linear or linearized dynamic systems, integral criteria of system dynamics, stability and quality of transitional processes. We also apply a technique for analyzing the own dynamic properties of economic systems that solve the same sustainability-related challenges, but on the basis of algebraic methods and criteria. Results. We offer methods and mathematical tools for numerical study of sustainability and structural dynamics of macroeconomics. These methods are focused on integrating high-dimensional balance models and integral criteria for the quality of transition periods in the economy. The paper unveils advantages of calculating the matrix exponential and its integral in tasks involving analysis and forecasting, over other numerical methods. The proposed method permits to effectively build a difference scheme to integrate with any step of observation of the solution. In this case, the work step of integration is generated in the algorithm automatically, depending on changes in gross output. Conclusions. The paper presents a unique option to analyze transitional processes in macroeconomics. It is designed to develop and evaluate the results of pursued economic policy.

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13

Navalina, Ivana Larasati Putri, Nur Indah Riwajanti, Sugeng Sulistyono, and Ludfi Djajanto. "FORECASTING PRODUKSI PERIKANAN LAUT YANG DIJUAL DI TPI (TON) DENGAN METODE SINGLE EXPONENTIAL SMOOTHING." Media Mahardhika 18, no. 2 (January 30, 2020): 206. http://dx.doi.org/10.29062/mahardika.v18i2.149.

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The purpose of this study was to determine the results of forecasting the production of fish sold at TPI in 2018-2020. This is expected to help the government in the formulation of plans and strategies related to the production of marine fish to increase the GRDP of fisheries in Java (regional level) and fisheries GDP in Indonesia (national level) and to contribute in the field of information and macroeconomics. This research used descriptive quantitative research and used data obtained through the official website of the Central Statistics Agency. This study used the Single Exponential Smoothing method. The results of this study have shown that the areas with the lowest sea fish production are in the DI Yogyakarta area, so the government must devise a strategy to maximize fish production in order to increase the PRDB contribution in Yogyakarta.

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14

Brandt, Patrick T., and John R. Freeman. "Advances in Bayesian Time Series Modeling and the Study of Politics: Theory Testing, Forecasting, and Policy Analysis." Political Analysis 14, no. 1 (2006): 1–36. http://dx.doi.org/10.1093/pan/mpi035.

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Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential value of these models in international relations, political economy, and other fields of our discipline. We review recent developments in Bayesian multi-equation time series modeling in theory testing, forecasting, and policy analysis. Methods for constructing Bayesian measures of uncertainty of impulse responses (Bayesian shape error bands) are explained. A reference prior for these models that has proven useful in short- and medium-term forecasting in macroeconomics is described. Once modified to incorporate our experience analyzing political data and our theories, this prior can enhance our ability to forecast over the short and medium terms complex political dynamics like those exhibited by certain international conflicts. In addition, we explain how contingent Bayesian forecasts can be constructed, contingent Bayesian forecasts that embody policy counterfactuals. The value of these new Bayesian methods is illustrated in a reanalysis of the Israeli-Palestinian conflict of the 1980s.

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15

Wulansari, Mifti Anisa, I. Wayan Suparta, and Arivina Ratih. "Analisis Indikator Ekonomi Makro Di Negara-Negara ASEAN Terhadap Perangkap Negara Berpendapatan Menengah." Jurnal Ekonomi Pembangunan 8, no. 3 (November 5, 2019): 158–68. http://dx.doi.org/10.23960/jep.v8i3.47.

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This study observes how the opportunities of middle-income countries located in ASEAN avoid the Middle Income Trap. Human Development Index, Foreign Direct Investments, Goods and Services Exports, and the Government Effectiveness Index are regressed to GNI per capita with panel analysis. Secondary data are used and was published officially by the World Bank and the United Nations Development Program (UNDP) in 5 ASEAN Regional Countries, namely Indonesia, the Philippines, Malaysia, Thailand and Vietnam in the period 2004-2017. Also, this study discusses the contribution of the Incremental Capital Output Ratio (ICOR) coefficient to Gross Domestic Product. The results of the study state that there are significant and positive effects of the independent variables on the dependent variable. Expected that, it's essential to give priority to macroeconomics as a result of this research. For Advanced Research, you can use bonus demographic and investment variables to provide forecasting to avoid the Middle Income Trap.

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16

Karabachinskiy, A. L., G. N. Mezentsev, and S. I. Tseluyko. "THE REGION OF EXISTENCE AND MATHEMATICAL MODEL TAX-FREE ECONOMY." Bulletin of the Tver State Technical University. Series «Social Sciences and Humanities», no. 3 (2020): 89–113. http://dx.doi.org/10.46573/2409-1391-2020-3-89-113.

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The article raises the theoretical problem of determining the possible sustainable functioning of the economy in the emission formation of budget revenues in compliance with the conditions of the absence of inflation or its availability within controlled limits. Describes the historical conditionality of occurrence of the tax economy, the interaction between the processes of circulation of money and commodity masses, but also the inefficiency of the tax system in the conditions of monetary circulation. The possibility of an evolutionary transition to the tax-exempt economy in the regime of controlled inflation for developed countries economies. On the basis of the modified equation of exchange Fisher performed a quantitative assessment of the timing of this transition. The possibility is shown of the emission budget for both developing and developed economies. Developed and tested on real economic statistics phenomenological nonlinear mathematical model of macroeconomics that allow forecasting and planning of economic processes. This model with some modification is used to describe the functioning of tax-free economy.

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17

Yang, Can, and Xuemei Li. "Research on Railway Freight Volume Prediction Based on Neural Network." E3S Web of Conferences 143 (2020): 01050. http://dx.doi.org/10.1051/e3sconf/202014301050.

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Railway freight volume is an important part of the total social freight volume and an important indicator of the national economy. Scientific prediction of railway freight volume can provide decision support for the formulation of China's railway policy and railway investment planning, and is of great significance for adjusting transportation structure and building an efficient transportation network. In order to improve the prediction accuracy, this paper constructs a combined prediction model based on GRA-GABP. The model uses grey correlation analysis to screen out the key influencing factors of railway freight volume, and optimizes the weight and threshold of BP neural network based on genetic algorithm to improve the prediction accuracy. This paper comprehensively considers the influencing factors of macroeconomics, market demand, logistics competition and railway supply. The historical data of railway freight transport from 1978 to 2018 is selected for case analysis. The results show that the prediction accuracy of the GRA-GA-BP based combination prediction model is significantly improved and can be used as an effective tool for railway freight volume forecasting.

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18

Bok, Brandyn, Daniele Caratelli, Domenico Giannone, Argia M. Sbordone, and Andrea Tambalotti. "Macroeconomic Nowcasting and Forecasting with Big Data." Annual Review of Economics 10, no. 1 (August 2, 2018): 615–43. http://dx.doi.org/10.1146/annurev-economics-080217-053214.

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Data, data, data…. Economists know their importance well, especially when it comes to monitoring macroeconomic conditions—the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before so-called big data became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available.

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19

Siviero, Stefano, and Daniele Terlizzese. "Macroeconomic Forecasting." Journal of Business Cycle Measurement and Analysis 2007, no. 3 (July 22, 2008): 287–316. http://dx.doi.org/10.1787/jbcma-v2007-art14-en.

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20

Henry, S. G. B., and K. Holden. "Macroeconomic forecasting." International Journal of Forecasting 6, no. 3 (October 1990): 283–84. http://dx.doi.org/10.1016/0169-2070(90)90054-f.

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21

Yasir, Muhammad, Sitara Afzal, Khalid Latif, Ghulam Mujtaba Chaudhary, Nazish Yameen Malik, Farhan Shahzad, and Oh-young Song. "An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment." Sustainability 12, no. 4 (February 22, 2020): 1660. http://dx.doi.org/10.3390/su12041660.

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In macroeconomics, decision making is highly sensitive and significantly influences the financial and business world, where the interest rate is a crucial factor. In addition, the interest rate is used by the governments to manage the monetary policy. There is a need to design an efficient algorithm for interest rate prediction. The analysis of the social media sentiment impact on financial decision making is also an open research area. In this study, we deploy a deep learning model for the accurate forecasting of the interest rate for the UK, Turkey, China, Hong Kong, and Mexico. For this purpose, daily data of the interest rate and exchange rate covering the period from Jan 2010 to Oct 2019 is used for all the mentioned countries. We also incorporate the input of the twitter sentiments of six mega-events, namely the US election 2012, Mexican election 2012, Gaza under attack 2014, Hong Kong protest 2014, Refugee Welcome 2015, and Brexit 2016. Our results provide evidence that the error of the deep learning model significantly decreases when event sentiment is incorporated. A notable improvement has been observed in the case of the Hong Kong interest rate, i.e., a 266% decline in the error after incorporating event sentiments as an input in the deep learning model.

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22

Toroptsev, E. L., A. S. Marakhovskii, and R. R. Duszynski. "The problem of digitalization of the dynamic input-output model." Economic Analysis: Theory and Practice 16, no. 5 (May 28, 2020): 946–72. http://dx.doi.org/10.24891/ea.19.5.946.

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Subject. The article addresses the digitalization of the dynamic model of inter industry balance. Objectives. We focus on developing our own statistical research base for input-output analysis, structural forecasting, sustainability, economic dynamics and economic growth. Mathematically, the study is formalized by the Cauchy problem for ordinary differential equations. Methods. The methodology components include theoretical and practical bases of the systems, statistical, input-output, and structural dynamic analysis. Results. Based on official statistics, we solved the problem of digitalization of the dynamic model of input-output balance, written in the form of a system of differential equations. For the first time, this model was transferred from a set of purely theoretical structures to a class of computable models. We developed a sequence of coordinated actions and calculations, which serve as a methodology for the said transfer. We also devised and presented the elements of our own statistical research base. Conclusions. The quantitative measurement of the dynamic inter-industry model in the form of a system of differential equations opens up broad perspectives on the sustainability of macroeconomics, its structural readiness for expanded reproduction, i.e. economic growth. The model can be used both independently and in combination with equilibrium and other agent-oriented models.

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23

Simionescu, Mihaela. "The Use of Varma Models in Forecasting Macroeconomic Indicators." ECONOMICS & SOCIOLOGY 6, no. 2 (November 20, 2013): 94–102. http://dx.doi.org/10.14254/2071-789x.2013/6-2/9.

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Semenenko, T., and V. Domrachev. "MACROECONOMIC DYNAMICS FORECASTING." Vìsnik Sumsʹkogo deržavnogo unìversitetu, no. 3 (2019): 110–16. http://dx.doi.org/10.21272/1817-9215.2019.3-14.

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Steady economy growth is possible only after allocating a clear objective and building macroeconomic development model. Acquiring of accurate prognoses of future development is the primary objective of applying macroeconomic models. Before VAR methods emerged prognosis were made based on timeline of economic indicators that were acquired through singular spectrum prognosis methods and extrapolation. Applying SSA methods implied that an indicator under research was formed under the influence of a multitude of factors that were impossible to separate. In this case, indicator changes were connected with the time flow rather than with the influencing factors, which led to the creation of singular time series. Authors prove that Ukrainian economy faced steady developing as well shocks. That is why using simple regressions for prognosis of macroeconomic indicators is not sufficient. VAR models not only enable the accurate forecasting of macroeconomic indicators but also are very useful when building models of stress-testing of the economy and banks in case of external and internal shocks. Preventing the negative effects can be effective using a model of macroeconomic risk management that enables managing exogenous macroeconomic factors in order to attain the well-defined objectives. In this paper, authors present the dynamics analysis of yearly changes of the gross domestic product, consumer price level, USD/UAH exchange rate, M2 money supply indicator, assets and liabilities of the Ukrainian banks dynamics, Ukrainian deposits, banks capital dynamics. Keywords: macroeconomic model, VAR model, macroeconomic risks, timeline, inflation rate, money supply, gross domestic product.

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25

Adams, Patrick A., Tobias Adrian, Nina Boyarchenko, and Domenico Giannone. "Forecasting macroeconomic risks." International Journal of Forecasting 37, no. 3 (July 2021): 1173–91. http://dx.doi.org/10.1016/j.ijforecast.2021.01.003.

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Heaton, Chris, Natalia Ponomareva, and Qin Zhang. "Forecasting models for the Chinese macroeconomy: the simpler the better?" Empirical Economics 58, no. 1 (November 7, 2019): 139–67. http://dx.doi.org/10.1007/s00181-019-01788-0.

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Abstract We consider the problem of macroeconomic forecasting for China. Our objective is to determine whether well-established forecasting models that are commonly used to compute forecasts for Western macroeconomies are also useful for China. Our study includes 19 different forecasting models, ranging from simple approaches such as the naive forecast to more sophisticated techniques such as ARMA, Bayesian VAR, and factor models. We use these models to forecast two different measures of price inflation and two different measures of real activity, with forecast horizons ranging from 1 to 12 months, over a period that stretches from March 2005 to December 2018. We test null hypotheses of equal mean squared forecasting error between each candidate model and a simple benchmark. We find evidence that AR, ARMA, VAR, and Bayesian VAR models provide superior 1-month-ahead forecasts of the producer price index when compared to simple benchmarks, but find no evidence of superiority over simple benchmarks at longer horizons, or for any of our other variables.

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27

Wallis, Kenneth F. "Macroeconomic Forecasting: A Survey." Economic Journal 99, no. 394 (March 1989): 28. http://dx.doi.org/10.2307/2234203.

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Teschner, Florian, and Christof Weinhardt. "A macroeconomic forecasting market." Journal of Business Economics 85, no. 3 (July 25, 2014): 293–317. http://dx.doi.org/10.1007/s11573-014-0741-5.

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29

Thury, Gerhard. "Macroeconomic forecasting in Austria." International Journal of Forecasting 1, no. 2 (January 1985): 111–21. http://dx.doi.org/10.1016/0169-2070(85)90016-0.

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Marcellino, Massimliano. "Forecasting EMU macroeconomic variables." International Journal of Forecasting 20, no. 2 (April 2004): 359–72. http://dx.doi.org/10.1016/j.ijforecast.2003.09.003.

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31

Pestova, A., and M. Mamonov. "A survey of methods for macroeconomic forecasting:looking for perspective directions in russia." Voprosy Ekonomiki, no. 6 (June 20, 2016): 45–75. http://dx.doi.org/10.32609/0042-8736-2016-6-45-75.

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The paper describes the evolution of macroeconomic theory in the XX century and the development of empirical models for applied macroeconomic forecasting. A comparison of modern structural and non-structural methods for macroeconomic forecasting is made. We consider the experience of macroeconomic forecasting in Russia and reveal its weaknesses and prospects for improvement.

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32

Fildes, Robert, and Herman Stekler. "The state of macroeconomic forecasting." Journal of Macroeconomics 24, no. 4 (December 2002): 435–68. http://dx.doi.org/10.1016/s0164-0704(02)00055-1.

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33

Arthur Smith, A. "Macroeconomic forecasting: Science or voodoo?" Planning Review 13, no. 3 (March 1985): 26–30. http://dx.doi.org/10.1108/eb054102.

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34

Porojan, Anca. "Macroeconomic Forecasting: A Sociological Appraisal." International Journal of Forecasting 16, no. 3 (July 2000): 423–25. http://dx.doi.org/10.1016/s0169-2070(00)00056-x.

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35

Ruth, Karsten. "Macroeconomic forecasting in the EMU." Journal of Policy Modeling 30, no. 3 (May 2008): 417–29. http://dx.doi.org/10.1016/j.jpolmod.2007.12.002.

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36

Hendry, David F. "The Econometrics of Macroeconomic Forecasting." Economic Journal 107, no. 444 (September 1, 1997): 1330–57. http://dx.doi.org/10.1111/j.1468-0297.1997.tb00051.x.

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Stock, James H., and Mark W. Watson. "Macroeconomic Forecasting Using Diffusion Indexes." Journal of Business & Economic Statistics 20, no. 2 (April 2002): 147–62. http://dx.doi.org/10.1198/073500102317351921.

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38

HAEFKE, CHRISTIAN, MARTIN NATTER, TARUN SONI, and HEINRICH OTRUBA. "Adaptive Methods in Macroeconomic Forecasting." International Journal of Intelligent Systems in Accounting, Finance & Management 6, no. 1 (March 1997): 1–10. http://dx.doi.org/10.1002/(sici)1099-1174(199703)6:1<1::aid-isaf118>3.0.co;2-2.

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39

Claveria, Oscar, Enric Monte, and Salvador Torra. "Evolutionary Computation for Macroeconomic Forecasting." Computational Economics 53, no. 2 (November 7, 2017): 833–49. http://dx.doi.org/10.1007/s10614-017-9767-4.

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40

Bachmeier, Lance. "The State of Macroeconomic Forecasting." International Journal of Forecasting 20, no. 4 (October 2004): 737–38. http://dx.doi.org/10.1016/j.ijforecast.2004.01.001.

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D'Agostino, Antonello, Luca Gambetti, and Domenico Giannone. "Macroeconomic forecasting and structural change." Journal of Applied Econometrics 28, no. 1 (July 14, 2011): 82–101. http://dx.doi.org/10.1002/jae.1257.

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Rybacki, Jakub. "Macroeconomic forecasting in Poland: The role of forecasting competitions." Central European Economic Journal 7, no. 54 (September 10, 2020): 1–11. http://dx.doi.org/10.2478/ceej-2020-0001.

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AbstractMacroeconomic forecasters are often believed to idealistically work on improving the accuracy of their estimates based on for example the Root Mean Squared Error (RMSE). Unfortunately, reality is far more complex. Forecasters are not awarded equally for each of their estimates. They have their targets of acquiring publicity or to earn prestige. This article aims to study the results of Parkiet's competitions of macroeconomic forecasting during 2015–2019. Based on a logit model, we analyse whether more accurate forecasting of some selected macroeconomic variables (e.g. inflation) increases the chances of winning the competition by a greater degree comparing to the others. Our research shows that among macroeconomic variables three groups have a significant impact on the final score: inflation (CPI and core inflation), the labour market (employment in the enterprise sector and unemployment rate) and financial market indicators (EUR/PLN and 10-year government bond yields). Each group is characterised by a low disagreement between forecasters. In the case of inflation, we found evidence that some forecasters put a greater effort to score the top place. There is no evidence that forecasters are trying to somehow exploit the contest.

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Stekler, H. O. "The future of macroeconomic forecasting: Understanding the forecasting process." International Journal of Forecasting 23, no. 2 (April 2007): 237–48. http://dx.doi.org/10.1016/j.ijforecast.2007.01.002.

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Diebold, Francis X. "The Past, Present, and Future of Macroeconomic Forecasting." Journal of Economic Perspectives 12, no. 2 (May 1, 1998): 175–92. http://dx.doi.org/10.1257/jep.12.2.175.

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Broadly defined, macroeconomic forecasting is alive and well. Nonstructural forecasting, which is based largely on reduced-form correlations, has always been well and continues to improve. Structural forecasting, which aligns itself with economic theory and hence rises and falls with theory, receded following the decline of Keynesian theory. In recent years, however, powerful new dynamic stochastic general equilibrium theory has been developed and structural macroeconomic forecasting is poised for resurgence.

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Schorfheide, Frank. "FORECASTING ECONOMIC TIME SERIES." Econometric Theory 16, no. 3 (June 2000): 441–50. http://dx.doi.org/10.1017/s0266466600003066.

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The prediction of future events and developments is an exciting and perhaps mysterious task, often associated with the aura of prophets and seers instead of probabilistic models and computer screens. The reality of macroeconomic forecasting, however, is quite mundane. Predictions of macroeconomic aggregates play an important role in the decision making of private enterprises, central banks, and governments. In general, forecasts become less popular if they turn out to be inaccurate ex post, and the postwar history of macroeconomic forecasting has had its share of disappointments. For instance, in the early 1980's, economists tested inflation forecasts taken over the previous 20 years and found that the forecasts were poor, partly as a result of the oil price shocks in the 1970's. A recent study (Croushore, 1998) with data up to 1996 provides a more favorable assessment of the quality of inflation forecasts.

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Garcia-Ferrer, A., R. A. Highfield, F. Palm, and A. Zellner. "Macroeconomic Forecasting Using Pooled International Data." Journal of Business & Economic Statistics 5, no. 1 (January 1987): 53. http://dx.doi.org/10.2307/1391215.

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Mittnik, Stefan. "Macroeconomic Forecasting Using Pooled International Data." Journal of Business & Economic Statistics 8, no. 2 (April 1990): 205. http://dx.doi.org/10.2307/1391982.

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Garcia-Ferrer, A., R. A. Highfield, F. Palm, and A. Zellner. "Macroeconomic Forecasting Using Pooled International Data." Journal of Business & Economic Statistics 5, no. 1 (January 1987): 53–67. http://dx.doi.org/10.1080/07350015.1987.10509560.

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Mittnik, Stefan. "Macroeconomic Forecasting Using Pooled International Data." Journal of Business & Economic Statistics 8, no. 2 (April 1990): 205–8. http://dx.doi.org/10.1080/07350015.1990.10509791.

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Pettenuzzo, Davide, and Allan Timmermann. "Forecasting Macroeconomic Variables Under Model Instability." Journal of Business & Economic Statistics 35, no. 2 (March 13, 2017): 183–201. http://dx.doi.org/10.1080/07350015.2015.1051183.

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