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1
Maziarz, Mariusz. "‘Emerging contrary result’ phenomenon and scientific realism." Panoeconomicus, no. 00 (2020): 24. http://dx.doi.org/10.2298/pan171218024m.
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The article is aimed at reconsidering the question if the project of econometrics can be read in line with scientific realism. Previously, the methodological literature focused on the philosophy of econometrics, voices criticizing realist interpretations of econometrics were raised. The criticism was aimed at showing that econometric models lack robustness. The use of slightly different methods leads to obtaining different and often contrary models what supposedly undermine the project of econometrics. In this article, I aim at offering a new argument in defence of the current practice of the economists devoted to the empirical branch of macroeconomics. To do so, I apply M?ki?s (2009) model of representation to three case studies of contradictory pairs of econometric models and argue that contrary results are not necessarily a drawback of econometrics. Instead, the seemingly contradictory pairs of models are useful in various contexts constituted by their purpose and audience.
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Koop, Gary. "Bayesian Methods for Empirical Macroeconomics with Big Data." Review of Economic Analysis 9, no. 1 (April 9, 2017): 33–56. http://dx.doi.org/10.15353/rea.v9i1.1434.
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Bayesian econometric methods are increasingly popular in empirical macroeconomics. They have been particularly popular among macroeconomists working with Big Data (where the number of variables under study is large relative to the number of observations). This paper, which is based on a keynote address at the Rimini Centre for Economic Analysis' 2016 Money-Macro-Finance Workshop, explains why this is so. It discusses the problems that arise with conventional econometric methods and how Bayesian methods can successfully overcome them either through use of prior shrinkage or through model averaging. The discussion is kept at a relatively non-technical level, providing the main ideas underlying and motivation for the models and methods used. It begins with single-equation models (such as regression) with many explanatory variables, then moves on to multiple equation models (such as Vector Autoregressive, VAR, models) before tacking the challenge caused by parameter change (e.g. changes in VAR coefficients or volatility). It concludes with an example of how the Bayesian can address all these challenges in a large multi-country VAR involving 133 variables: 7 variables for each of 19 countries.
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Phillips, Peter C. B. "Trending Multiple Time Series: Editor's Introduction." Econometric Theory 11, no. 5 (October 1995): 811–17. http://dx.doi.org/10.1017/s0266466600009890.
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One of the more obvious empirical characteristics of macroeconomic time series is their tendency to grow, or trend, over time. Dealing with this trendnonstationarity in models of multiple time series has been a major agenda of econometric research for much of the last decade and has produced an enormous literature. Equally, the goal of developing a general asymptotic theory of inference for stochastic processes has been a long-standing concern of probabilists and statisticians. Finally, understanding and modeling trend processes and cyclical activity lie at the nerve center of much of modern macroeconomics. As a consequence, research on nonstationary time series has brought statisticians, econometricians, and macroeconomists close together in productive ways that simply could not have been anticipated 10 years ago.The focus of this symposium issue of Econometric Theory is inference from multiple time series data with trends, and the symposium brings together researchers with these diverse interests. The papers included in the issue were, with two exceptions, presented at a conference called “Trending Multiple Time Series,” held at Yale University in the fall of 1993 under the financial sponsorship of the National Science Foundation. All of the papers were written by conference participants. The conference was the fourth in a series of small conferences at Yale on the general theme of “Applications of Functional Limit Theory to Econometrics and Statistics.”
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Nymoen, Ragnar. "On the Low Degree of Entropy Implied by the Solutions of Modern Macroeconomic Models." Entropy 24, no. 12 (November 25, 2022): 1728. http://dx.doi.org/10.3390/e24121728.
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The non-causal (“forward-looking”) solution used routinely in academic macroeconomics may represent a violation of a law of entropy, namely that the direction of time is one way (from the past and towards the present), and that the variance of economic processes increases with time. In order to re-establish a degree of compatibility with the law of entropy, so called hybrid forms are required add-ins to DSGE (Dynamic Stochastic General Equilibrium) models. However, the solution that uses hybrid forms is a particular special case of a causal solutions of autoregressive distributed lags, VARs and recursive and simultaneous equations models well known from empirical macro econometrics. Hence, hybrid forms of small scale DSGE models can be analysed and tested against competing model equations, using an econometric encompassing framework.
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Kazmi, Aqdas Ali. "An Econometric Estimation of Tax-discounting in Pakistan." Pakistan Development Review 34, no. 4III (December 1, 1995): 1067–77. http://dx.doi.org/10.30541/v34i4iiipp.1067-1077.
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The debt neutrality hypothesis which has been a source of major controversies in the theory of public finance, and macroeconomics has at the same time generated a vast literature on the implications of budgetary deficits and public debt on various subsectors/ variables of the economy, such as inflation, interest rates, current account deficit, etc. Tax discounting has been one of the fields of research associated with debt neutrality. The econometric estimation of some of the standard models of taxdiscounting has shown that consumer response to fiscal policy in Pakistan reflects neither the extreme Barro-like rational anticipation of future tax liabilities nor the Buchanan-type extreme fiscal myopia. It broadly follows a middle path between these extremes. The controversy relating to debt neutrality is quite old in economic theory. However, due to its serious and far-reaching implications for the formulation of fiscal policy and macroeconomic management, the issues of debt neutrality have assumed a foremost position in economic theoretisation and empirical testing. This controversy is based on two important questions: (a) Who bears the burden of the debt? (b) Should debt be used to finance public expenditure? The first question centres on whether the debt can be shifted forward in time, while the second question explores whether taxation is equivalent to debt in its effects on the national economy.
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Artamonov, N. V., D. V. Artamonov, and V. A. Artamonov. "Credit Cycles: Econometric Analysis and Evidence for Russia." MGIMO Review of International Relations, no. 2(35) (April 28, 2014): 113–22. http://dx.doi.org/10.24833/2071-8160-2014-2-35-113-122.
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One of the principal problem in contemporary macroeconomics is concerned with factors increasing or decreasing economic dynamics. The mainstream approach is based on neoclassical assumptions, but recently new approaches appear mostly based on new Keynesian concepts. In present time the influence of monetary market and credit instruments become more and more significant. Credit resources of banking and financial structures can affect and distort to reallocation of resources for national and even for global economic. In present paper an empiric and econometric analysis for some macroeconometric and monetary indices for Russian Federation is done. An econometrical models describing the influence of credit variables onto real GDP is estimated. It is shown that in short-term periods changes in credit variables do influence significantly onto GDP. It is shown that on short-term periods changes in money aggregate M2 brings influence (through credit variables) onto national output. As well it is shown that changes in short-term interest rate brings significant negative influence onto real output. Impulse response functions for GDP on shocks of credit variables, monetary base and short-term interest rate are evaluated. For the present study of credit cycles and their impact to real business cycles statistical data (quarterly time series) on the following factors for Russian Federation are collected: nominal and real GDP, monetary base M2, short-term interest rate, long-term interest rate (10-year treasuries bill rate), total debt outstanding. All time series are seasonally adjusted and collected for the period 2004 Q1 - 2013 Q2. All interest rates are adjusted for inflation (i.e. we deal with real interest rates). The investigation of long-term relationship for the factors under consideration are based on integration. It is important to note that in the present paper all econometric models are estimated on "pure" statistical data, while in many research papers on business and credit cycles all evaluations and inferences are based on "filtered" time series (mostly filtered by Hodrick-Prescott's method). In present paper "causality" always means "Granger causality". All estimations are made in gretl, an open-source multiplatform econometric software.
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Cherevatskyi, Danilo, and Roman Smirnov. "On the correlation between GDP and energy consumption in macroeconomic development." Economy of Industry 2, no. 94 (June 25, 2021): 59–70. http://dx.doi.org/10.15407/econindustry2021.02.059.
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There is substantial literature devoted to the study of the dependence between energy production and economic development. At the same time, the long-standing discussion of the relationship between the gross domestic product and consumption of primary energy resources, numbering thousands of publications, eventually degenerated into a dispute about econometric methods but did not give final results, which caused the need to resort to other approaches. This paper is an attempt to find a solution to this problem by the methods of theoretical mechanics and regression analysis of the relationship between GDP and energy production in macroeconomic development. Our case studies include the economies of Germany, France, Italy, Japan, Russia, Turkey, and Ukraine. In each case, we characterize the gross domestic product, recalculated at purchasing power parity in 2017 prices, and the consumption of primary energy resources (coal, oil, natural gas, hydro and nuclear energy, energy from renewable sources). Within the framework of the study, it was assumed that the development of any national economy over time is its path in the economic space, and the consumption of primary energy resources is due to dynamic characteristics inherent in macroeconomics, in particular, "mass", which serves as a measure of the inertia of the country's economic complex, the presence of an informal sector, etc. The path, traversed by macroeconomics in the economic space, is the gross domestic product accumulated over time. The observation period is from 1990 to 2019, that is – 30 years. The use of the theory of classical mechanics, in particular – kinematics and dynamics, is justified by the fact that macroeconomics in its development requires the expenditure of energy resources, and this likens it to a machine that moves in a certain space that models a given economyсs. The article introduces methodological approaches to defining the conventional mass of macroeconomics, accelerating its movement, expenditure of energy resources for the functioning of the formal sector of the national economy, the efficiency of energy use in the formal sector.
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Campos, Octávio Valente, Wagner Moura Lamounier, and Rafael Morais de Souza. "The composition of firms' indebtedness and the macroeconomy of capital." Revista Catarinense da Ciência Contábil 21 (September 9, 2022): e3296. http://dx.doi.org/10.16930/2237-7662202232962.
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The objective of this research is to analyze the influence that monetary policies exert on the composition of the indebtedness of Brazilian corporations. From this objective, 2 hypotheses derive. The first analyzes the sample aggregate and the second directs the tests to the productive sectors. The study sample is composed of 220 companies: 84 of consumer goods, 89 of capital goods and 47 of public utility. The data collected refer to the years 2009 to 2019. The methodology used for data analysis is through panel data models, using the GMM approach. According to the results, it can be concluded - in the light of the macroeconomics of capital - that the composition of the firms' indebtedness can be determined by the market moments defined by the monetary policies, so that such influence is different depending on the sector to which the companies are located in the production chain. These results complement the literature that studies the impacts of monetary policies and macroeconomic variables on corporate finance, mainly through econometric modeling based on accounting data.
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Hacioglu, Umit, Hasan Dincer, and Ismail Erkan Celik. "Conflict Risk and Its Implication on Economy and Financial System." International Journal of Finance & Banking Studies (2147-4486) 2, no. 2 (November 16, 2016): 109. http://dx.doi.org/10.20525/ijfbs.v2i2.638.
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<p>Considering the impacts of the conflict on the economic parameters in terms of macroeconomics, the following factors might affect the profitability of the company: foreign capital outflows, decrease in exports, increase in the interest rates, disruption of the investment climate, increase in the exchange rates, increase in the costs of import entry etc. Due to the expectable decrease in profit shares as to the investors, the contraction in the risk appetite will cause volatility in the prices of equity securities markets based on the impacts of the conflict, and the equity securities will depreciate. In this study, the main contributions on conflict risk and related econometric models have been discussed.</p>
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Foley, D. "Mathematical Formalism and Political-Economic Content." Voprosy Ekonomiki, no. 7 (July 20, 2012): 82–95. http://dx.doi.org/10.32609/0042-8736-2012-7-82-95.
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Mathematical methods are only one moment in a layered process of theory generation in political economy, which starts from Schumpeterian vision, progresses to the identification of relevant abstractions, the development of mathematical and quantitative models, and the confrontation of theories with empirical data through statistical methods. But today the relevant abstract problems of political economy are modified to fit available mathematical tools. The role of empirical research in disciplining theoretical speculation, on which the scientific traditions integrity rests, was undermined by specific limitations of nascent econometric methods, and usurped by ex cathedra methodological fiats of theorists. These developmentssystematically favored certain ideological predispositions of economicsas a discipline. There is abundant room for New Thinking in political economy starting from the vision of the capitalist economy as a complex, adaptive system far from equilibrium, including the development of the theory of statistical fluctuations for economic interactions, redirection of macroeconomics and financial economics from path prediction toward an understanding of the qualitative properties of the system, introduction of constructive and computable methods into economic modeling, and the critical reconstruction of econometric statistical methods.
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Zamulin, O., and K. Styrin. "How to Distinguish Cause and Effect? (2011 Nobel Memorial Prize in Economics)." Voprosy Ekonomiki, no. 1 (January 20, 2012): 4–20. http://dx.doi.org/10.32609/0042-8736-2012-1-4-20.
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The paper discusses the contribution of 2011 Nobel laureates, Thomas Sargent and Christopher Sims, to economics. It is shown how these two economists proposed to analyze causal relationships in macroeconomics. The common idea of the two laureates is that one should not look for isolated empirical relationships; rather, one should seek theories, which are capable of explaining the observed regularities, and then subject the whole theories to empirical tests. However, the specific methods proposed by the two economists differ. Thus, Sargent proposed to test a specific model; Sims, on the other hand, introduced the method called structural autoregression, in which an econometric model is subjected to a minimal set of theoretical restrictions encompassing a large set of theoretical models. Then the paper discusses applications of this logic to different situations.
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Hall, S. G., and R. Herbert. "Consistent Simulations and the National Institute Model 8." National Institute Economic Review 115 (February 1986): 64–73. http://dx.doi.org/10.1177/002795018611500107.
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This paper will present a set of simulation exercises, and discuss some of the methodological issues arising from the new National Institute Model 8. This model has been developed from earlier models used at the National Institute and has much in common with those models. In particular, it adheres to a traditional income-expenditure framework and is, in broad outline, a Keynesian model. The new feature of Model 8 which distinguishes it from its predecessors is the explicit widespread treatment of expectations. Recognition of the importance of expectations in macroeconomics is widely disseminated and much attention has been given recently in the econometric literature to the modelling of expectations. Model 8 now includes explicit expectations terms in the following sectors: employment, stockbuilding, investment, wage formation, the exchange rate and the demand for money. The details of individual equation estimation and specification may be found in Hall and Henry (1985a) and will not be given here. In the estimation process much use has been made of the rational expectations hypothesis, while in the full model implementation expectations may be formed either consistently or on the basis of explicit expectations models.
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Bares López, Lydia, Ana Mª Fernández Pérez, Esther Ferrándiz León, Mª Esther Flores Varo, and Mª Dolores León Rodríguez. "Using Interactive Response Systems in Economics: utility and factors influencing students’ attitudes." Multidisciplinary Journal for Education, Social and Technological Sciences 4, no. 1 (April 10, 2017): 16. http://dx.doi.org/10.4995/muse.2017.5476.
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<p class="Textoindependiente21"><span lang="EN-US">The European Higher Education Area (EHEA) involves changing traditional methods to promote innovative teaching experiences. This paper has two main aims: a) to show evidence of the use of Interactive Response Systems (IRS) to identify gaps in the understanding of the course contents and b) to investigate factors influencing students’ attitudes towards the use of IRS. The experience was developed through a collective tutoring session in the subject of Economics using IRS. Economics is a first-year subject in the Degree of Business Administration and Management offered by the University of Cadiz, which includes contents of Microeconomics and Macroeconomics and uses economic models to explain the function of the economy and the behaviour of economic agents. Results show that IRS technique allows detecting gaps in learning and comprehension. From our econometric estimations, we also identify two strongly significant variables affecting students’ attitudes towards IRS: gender and received explanations regarding the use of IRS. Variables such as first enrolment in the subject and the number of hours devoted to studying have a positive and significant effect on the attitude to IRS, but at a lower level of significance (from 5% to 10%).</span></p>
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Rana Shahid Imdad Akash, Muhammad Mudasar Ghafoor, and Navid Ahmed. "Testing the Validity of Purchasing Power Parity Theory and Dynamics of Exchange Rate Behavior (Pakistan, China, Iran and Turkey)." Journal of Accounting and Finance in Emerging Economies 6, no. 1 (March 31, 2020): 127–44. http://dx.doi.org/10.26710/jafee.v6i1.1059.
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Purpose: This study is aimed at to observe the purchasing power parity (PPP) Theory. The purchasing power parity (PPP) is the most enduring debate of literature in international macroeconomics. It is most controversial due to various puzzles and tested with different econometric models for certain group of countries. Therefore, the PPP is valid assumption while international comparison due to use of common exchange rate and the prevalence of Law of One price.
Design/Methodology/Approach: The validity of PPP for relative countries (Pakistan, China, Iran and Turkey) was tested and analyzed for the sample period 2001 to 2018.
Findings: It is observed that exchange rates of Pakistan, China, Iran and Turkey are not consistent and constant. The deviations of PPP through structural changes identified and are not persistence over long period. Overall results reflected that there is an existence of long run equilibrium relation in between Pakistan and China as well as in between Iran and Turkey. The error correction model has confirmed the adjustment speed of short run disequilibrium to long term disequilibrium level. Implications/Originality/Value: The expected differential level of inflation has significant positive impact to exchange rate shift to Pakistan and trading activity patterns. The changes in foreign exchange market and commodity market due to economic integration are important implications for economic globalization.
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Formánek, Tomáš, and Roman Hušek. "Spatial Aspects Of Unemployment In The Visegrad-Group Economies." Creative and Knowledge Society 6, no. 2 (December 1, 2016): 1–12. http://dx.doi.org/10.1515/cks-2016-0007.
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Abstract Purpose of the article: Most regional macroeconomic processes may not be adequately analyzed without accounting for their spatial nature: regional distances, interactions between neighbors, spill-over effects and interdependencies. This contribution focuses on various factors ruling unemployment dynamics in the Visegrad Group countries and their major economic partners: Germany and Austria. The analysis is performed at the NUTS2 level. Methodology/methods: Spatial econometrics is a unique tool for a broad range of quantitative analyses and evaluations. Spatial econometric models are based on geo-coded (spatially defined) data. Spatial econometrics and regional competitiveness paradigms are combined into different types of regression model specifications describing unemployment dynamics. Alternative spatial structures (i.e. neighbor definitions) are used for verification of stability in estimated model properties. Scientific aim: We aim to provide a detailed empirical evaluation of spatially determined factors of regional unemployment dynamics, along with insight into the robustness of such approach. Both conceptually and parametrically varying neighbor definitions are used to provide evidence for model evaluation. Findings: We find strong positive spatial dependence patterns in the estimated models, robust against varying neighborhood definitions. Our results strongly support the importance of regional and potentially cross-border (international) cooperation in macroeconomic policies addressing unemployment. The estimated models also underline the importance of using spatial models, by pointing out the bias in OLS-estimated models. Conclusions and limits: Spatial approach to econometric analysis provides important insight and robustness to a broad range of unemployment analyses that may be carried out using regional (spatial) data. At the same time, it should be noted that this article focuses mostly on the spatial and stability aspects of model estimation, while leaving out other interesting topics such as spill-over effects calculations as based on estimated models. Also, estimations provided in this article might benefit from spatial panel data-based methods - once data availability issues are sorted.
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Lieberman, Offer. "ASYMPTOTIC THEORY OF STATISTICAL INFERENCE FOR TIME SERIES." Econometric Theory 18, no. 4 (May 17, 2002): 993–99. http://dx.doi.org/10.1017/s0266466602004103.
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Modern time series econometrics involves a diversity of models. In addition to the more traditional vector autoregressive (VAR) and autoregressive moving average (ARMA) systems, cointegration and unit root models are in widespread use for macroeconomic data, nonlinear and non-Gaussian models are popular for financial data, and long memory models are becoming more common in both macroeconomic and financial applications. Much econometric thought relates to issues of estimation and hypothesis testing, and so, in the absence of a usable finite sample theory (as is the case for the models just mentioned), an enormous amount of effort has been given to developing adequate asymptotics for statistical inference. There is often a lag between the introduction of a new model and the development of an asymptotic theory. In consequence, applied econometricians sometimes have to estimate time series models for which no asymptotic theory is available. For instance, multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models have been in use in empirical research for a while, and practitioners have been using asymptotic normality of estimators in this model even though a theoretical justification is not available.
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Mallick, Sushanta Kumar, and T. Krishna Kumar. "A Non-econometric Review of Indian Macroeconomic Models." Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics 37, no. 1 (March 1, 1995): 1. http://dx.doi.org/10.21648/arthavij/1995/v37/i1/115998.
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Oxley, Les, and Michael McAleer. "ECONOMETRIC ISSUES IN MACROECONOMIC MODELS WITH GENERATED REGRESSORS." Journal of Economic Surveys 7, no. 1 (March 1993): 1–40. http://dx.doi.org/10.1111/j.1467-6419.1993.tb00158.x.
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Fisher, P. G., S. K. Tanna, D. S. Turner, K. F. Wallis, and J. D. Whitley. "Comparative Properties of Models of the Uk Economy." National Institute Economic Review 125 (August 1988): 69–87. http://dx.doi.org/10.1177/002795018812500107.
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This paper describes the overall properties of six major macroeconomic models, through dynamic multiplier analysis of a number of standard simulation exercises. The models are those of the London Business School (LBS), the National Institute of Economic and Social Research (NIESR), HM Treasury (HMT), the Bank of England (BE), the City University Business School (CUBS) and the Liverpool University Research Group in Macroeconomics (LPL), as deposited with the ESRC Macroeconomic Modelling Bureau in late 1987. The simulations demonstrate the response of the models to changes in key policy instruments and to exogenous shocks; they are conducted, as far as possible, in a consistent manner across the models.
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Nikolaychuk, Sergiy, and Yurii Sholomytskyi. "Using Macroeconomic Models for Monetary Policy in Ukraine." Visnyk of the National Bank of Ukraine, no. 233 (September 29, 2015): 54–64. http://dx.doi.org/10.26531/vnbu2015.233.054.
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An important precondition for successful implementation of inflation targeting is the ability of the central bank to forecast inflation given the fact that the inflation forecast has become an intermediate target. Certainly, this means there should be clear understanding of the monetary policy transmission mechanism functioning within the bank, because it is precisely through transmission channels that a central bank has to ensure convergence of its inflation forecast to the target. And it is almost impossible to pursue inflation targeting without a set of macroeconomic models that describes the monetary policy transmission mechanism and helps to analyse the current state of the economy as well as forecast (simulate) short- and medium-term macroeconomic scenarios. This article provides a review of the current state of macroeconomic modelling at central banks and describes the history of development and actual stance of the National Bank of Ukraine’s system of macroeconomic models. The existing system provides quite reliable support for the current monetary policy decision-making process, but it has to be improved by implementing a more sophisticated model (such as a dynamic stochastic general equilibrium model) and enhancing the set of econometric models for shortterm forecast purposes in the future.
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Jawadi, Fredj. "INTRODUCTION TO TIME-VARYING MODELING WITH MACROECONOMIC AND FINANCIAL DATA." Macroeconomic Dynamics 16, S2 (April 12, 2012): 167–75. http://dx.doi.org/10.1017/s136510051100071x.
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The dynamics of macroeconomic and financial series has evolved swiftly and asymmetrically since the end of the 1970s, and their statistical properties have also changed over time, suggesting complex relationships between economic and financial variables. The transformations can be explained by considerable changes in householder's behavior, market structures, and economic systems and by the alternation of exogenous shocks and financial crises that have affected the economic cycle, with significant evidence of time variation in the major economic variables. Hence, there is a need for new econometric protocols to take such changes into consideration. The introduction of ARMA (autoregressive moving average models) by Box and Jenkins (1970) led to the development of time-series econometrics, which had a major impact on the conceptual analysis of economic and financial data. This type of modeling offered a transition from a static setup to a new modeling process that reproduces the time-varying features of macroeconomic and financial series. However, the ARMA modeling system retains the constancy of the first and second moments, limits the phases of a cycle to symmetrical instances, and only reproduces the dynamics of stationary variables. It thus fails to adequately reproduce the nonstationary relationships between major economic and financial variables. Abrupt changes in economies and financial systems have given evidence of nonstationary series whose statistical properties are also time-varying, making it necessary to develop new econometric tools to capture the time variation of economic and financial series in the mean and in the variance, and to apprehend their dynamics in the short and long term. Among the most important and influential studies in the 1980s' econometrics literature were therefore those that dealt with the introduction of the ARCH (autoregressive conditional heteroskedasticity) model by Engle (1982) and the cointegration theory by Engle and Granger (1987). The ARCH model, which focuses on the time-varying features of volatility structure, was a major breakthrough, as it highlighted the importance of the second moment of time series, while the cointegration framework enabled the short- and long-term dynamics of nonstationary variables to be modeled.
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Kaboudan, Mahmoud A. "Oil Revenue and Kuwait's Economy: An Econometric Approach." International Journal of Middle East Studies 20, no. 1 (February 1988): 45–66. http://dx.doi.org/10.1017/s0020743800057500.
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This paper presents a macroeconomic model for a small developing oil-exporting economy: Kuwait. The model is a simultaneous system of difference equations. Historic effects of changes in revenues from oil exports on the country's economic conditions are simulated. The model is then used to forecast these conditions through 1990, and to test two fiscal policy alternatives under the assumption that revenues from Kuwait's oil exports will remain constant from 1986 to 1990. The following are key words: developing economies; oil-exporting economies; Middle East economies; Kuwait; Kuwait's economy; policy models; macroeconomic models; econometric models; macroeconometric models; forecasting models; and policy models.
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Habibullah, Muzafar Shah. "The Rationality Of Economic Forecasts: The Cases Of Rubber, Oil Palm, Forestry And Mining Sector." Agro Ekonomi 10, no. 1 (November 29, 2016): 67. http://dx.doi.org/10.22146/agroekonomi.16788.
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Forecasts of economic variables is very important for planning and policy making purposes. Forecasts is an important input in decision making processes because obtaining reliable forecasts of some relevant macroeconomic variables is necessary for efficient management of funds, time and resources.Business has always recognised the need for a view of the future and has used explicit forecasts in the design and execution of their economic andJor business policies. For example, a firm trying to decide upon its investment programme will have to take into account not only the current known set of circumstances but also the unknown economic and business conditions in the future. The firm has to form a view about the future, such as the likely sales, costs, prices, competitors' reactions, labour requirements, government regulations and so on. These views about the future values of economic variables are frequently referred to as 'expectations', that is, what the firm expects to happen in the future.In recent years the performances of many microeconomics and macroeconomics series have been erratic. For example, rate of inflation, price of crude oil, prices of primary commodities, rate of interest and other pertinent economic variables have been fluctuating widely and have caused concern among the public, politicians, economists and also the businessmen. According to Mayes (l 981), with such non-uniformity of economic variables observed in the last two decades, the role of expectations has become more relevant in the economic agents' decision making process. Mayes (1981) further states that under the present conditions it has become more important to consider what expectations actually are and how they are formed.The value of economic forecasts of certain macroeconomic variables can be derived from several methods. The three main methods for deriving economic forecasts are (i) time series, (ii) econometric models, and (iii) survey of intentions of concerned agents and organizations. Time seriesanalysis and econometric modeling are the two most widely used methods in economic forecasting, but Holden and Peel (1983) had noted their drawbacks. Recently, economists have turned their direction of interest in evaluating the rationality of economic forecasts from surveys of market participants. The empirical literature on the direct tests of the rational expectations hypothesis is vast and growing. Holden et al. (1985), Lovell (1986), Wallis (1989), Maddala (1991) and Pesaran (1991) had reviewed some of these studies. The aim was to determine whether survey data on economic forecasts are accurate in the Muth's (1961) sense, that is, whether participating economic agents used all available information at the time forecasts are made. in other words, the rational expectations hypothesis of the economic forecast was put to test. In general, the empirical studies do not support the rational expectations hypothesis.Most of the studies carried out to evaluate the rationality of business firms' forecasts of economic variables were conducted on developed nations. Madsen (1993) studies the formation of output expectations in manufacturing industry in Japan, Denmark, Finland, France, Germany, Netherlands, Norway, Sweden and the United Kingdom. He found that the rational expectations hypothesis was weakly rejected. Williams (1988) and Chazelas (1988) found investment forecasts biased predictors of the actual investment value for firms in the United Kingdom and France. Meganck et a!. (1988) have concluded that investment forecasts of the manufacturing firm in Belgium were unbiased predictors of the actual values. However. Daub (1982) failed to find any rationality of the Canadian capital investment intention survey data. On the other hand. a study by Leonard (1982) on employment forecasts by the United States services sectors found that the forecasts were biased and the rationality of these employment forecasts rejected.The purpose of this paper is to present some empirical evidence on the rationality of agricultural firm managers' expectations using survey data. This study is important because it adds to the current literature on the testing of rationality of survey data, in particular, it provides empirical evidence from the perspective of a developing country. As for the country under study, the finding of the study could establish whether the forecasts documented by such survey are accurate or not; and if not, ways to produce more accurate forecasts must be found. 'Rationality' in this paper means that managers in agricultural firms have unbiased expectations and efficiently utilised available information at the time the forecasts are made.
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Stavytskyy, Andriy, and Daria Martynovych. "THE ECONOMETRIC MODELING OF UKRAINIAN MACROECONOMIC TENDENCIES." Ekonomika 91, no. 1 (January 1, 2012): 79–92. http://dx.doi.org/10.15388/ekon.2012.0.906.
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Econometric models are widely used in economic policies of many states. They help to build a great variety of econometric systems for every country and take into account the specifics of each economy.In this article, the structural macroeconomic models that describe the main aspects of the economic policy were applied. The interdependence between the level of inflation, the value of investment, savings, consumption, export and import transactions, taxes on the foreign trade were defined based on the analysis of the key macroeconomic parameters of Ukraine. After investigating all economic indicators, they were transformed into stationary time series for a correct use in the model. In addition, heteroscedasticity and autocorrelation of residuals were excluded in all econometric equations.As a result, the research shows that a large share of black economy leads to a rather high level of inflation in the state, because its value is primarily determined by expectations of the population under such circumstances. The paper indicates that the further export growth leads to a lower consumption growth and also to a lower growth of savings. Such a situation indicates an insufficient development of the domestic market. Investment growth has been fund not to be directly linked to consumption increase and economic development in general. Unfortunately, the main sources of investment in Ukraine are the funds of enterprises and foreign sources. The analysis shows a need to encourage public involvement into investment processes. For example, the creation of public–private partnerships is especially useful while implementing infrastructural projects.
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Rudzkis, Rimantas, and Roma Valkavičienė. "ECONOMETRIC MODELS OF THE IMPACT OF MACROECONOMIC PROCESSES ON THE STOCK MARKET IN THE BALTIC COUNTRIES." Technological and Economic Development of Economy 20, no. 4 (December 16, 2014): 783–800. http://dx.doi.org/10.3846/20294913.2014.949901.
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The article examines the dependencies of individual sectoral stock price indices of OMX Baltic security market on macroeconomic indicators, using econometric methods. Regression models are constructed using quarterly time series of 2000–2011 years while the methodology is backed with the findings of Lithuanian and foreign scientists from an extensive overview of specific literature. Regression equations, obtained in the paper, allows us to identify the key macroeconomic and global indicators that statistically significantly affect the Baltic securities market and to quantify their impact on the stock price indices of individual sectors in the Baltic countries. Econometric analysis of OMX Baltic security market proves the hypothesis that the set of macroeconomic regressors may vary considerably depending on the individual sector's price indices, especially in the case of small open economy with immature stock markets. The paper provides investors who are shaping their portfolios taking into account the macroeconomic forecasts with additional opportunities on the basis of sectoral stock price indices regression equations.
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Roljic, Lazo. "An expert system for national economy model simulations." Yugoslav Journal of Operations Research 12, no. 2 (2002): 247–69. http://dx.doi.org/10.2298/yjor0202247r.
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There are some fundamental economic uncertainties. We cannot forecast economic events with a very high scientific precision. It is very clear that there does not exist a unique 'general' model, which can yield all answers to a wide range of macroeconomic issues. Therefore, we use several different kinds of models on segments of the macroeconomic problem. Different models can distinguish/solve economy desegregation, time series analysis and other subfactors involved in macroeconomic problem solving. A major issue becomes finding a meaningful method to link these econometric models. Macroeconomic models were linked through development of an Expert System for National Economy Model Simulations (ESNEMS). ESNEMS consists of five parts: (1) small-scale short-term national econometric model, (2) Methodology of Interactive Nonlinear Goal Programming (MINGP), (3) data-base of historical macro-economic aggregates, (4) software interface for interactive communications between a model and a decision maker, and (5) software for solving problems. ESNEMS was developed to model the optimum macro-economic policy of a developing country (SFRY-formerly Yugoslavia). Most econometric models are very complex. Optimizing of the economic policy is typically defined as a nonlinear goal programming problem. To solve/optimize these models, a new methodology, MINGP, was developed as a part of ESNEMS. MINGP is methodologically based on linear goal programming and feasible directions method. Using Euler's Homogeneous Function Theorem, MINGP linearizes nonlinear homogeneous functions. The highest priorities in minimizing the objective function are the growth of gross domestic product and the decrease of inflation. In the core of the optimization model, MINGP, there is a small-scale econometric model. This model was designed through analysis of the causal relations in the SFRY's social reproduction process of the past 20 years. The objective of the econometric model is to simulate potential short term (one-year) national economic policies. Ex-ante simulation and optimization of economic policy for 1986 showed that, in SFRY, non-consistent macro-economic policy was resolute and led to both slower economic development and more rapid growth of inflation.
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Kozinova, A. T. "An econometric analysis of retail turnover in Russia." Economic Analysis: Theory and Practice 19, no. 6 (June 29, 2020): 1133–53. http://dx.doi.org/10.24891/ea.19.6.1133.
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Subject. The article deals with econometric analysis of retail turnover in Russia and its relationship with macroeconomic indicators, like real disposable household income, consumer prices, etc. Objectives. The purpose is to create effective models to analyze the retail turnover in Russia and its relationship with other macroeconomic indicators, taking into account the existence of periods of economic instability. Methods. I apply correlation and regression methods to analyze statistics. To quantify changes in the retail turnover of Russia during the periods of economic instability, I use dummy variables. Results. The Russia’s retail trade turnover index had a reverse and moderate relationship with the consumer price index, direct and strong relationship with the indices of real disposable household income and imports, direct relationship with the manufacturing index. I offer statistically significant regression models of Russia’s retail turnover with the said macroeconomic indicators. Conclusions. The main advantage of models of retail turnover that are built using a large number of observations is a greater number of simultaneously considered factors. The quantitative assessment of retail turnover elasticity by consumer prices confirms the need for inflation targeting by the Central Bank of the Russian Federation. The higher elasticity of retail turnover in manufacturing as compared with the imports denotes the importance of import substitution policy.
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Kwilosz, Tadeusz, and Bogdan Filar. "Ekonometryczny model krótkoterminowego prognozowania zużycia gazu." Nafta-Gaz 77, no. 7 (July 2021): 454–62. http://dx.doi.org/10.18668/ng.2021.07.04.
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In order to develop a mathematical model of short-term gas demand, it is necessary to analyze the latest mathematical forecasting methods in order to select and adapt the right one (meeting the condition of efficiency and effectiveness). It is necessary to recognize and analyze factors (mainly environmental) affecting the result of short-term forecasts and sources of data that can be used. The result of the work is a numerical model of short-term gas demand for a selected territorial unit of the country. The developed model was calibrated and tested on historical data describing environmental conditions and real gas consumption. A heterogeneous linear econometric model was designed and calibrated on the basis of a selected set of attributes (explanatory variables). The estimated parameters of the model were statistically verified. It is worth noting that in the short term of the forecast (7 days) there are no significant changes in the gas market environment (launching new investments, connecting new users to the system, or changes in demand resulting from changing macroeconomic conditions). Other technical factors, such as production line failures at customers or industrial downtime, are difficult to predict, or knowledge about their occurrence is rarely available. For this reason, the only factors that may have an impact on changes in gas demand in the short term are weather factors, which were selected as explanatory variables for the developed model. Historical weather data was retrieved from the OpenWeatherMapHistoryBulk web service. Daily values of gas consumption for one of the voivodships of southern Poland were used as the response variable. The data was downloaded from the information exchange system of the transmission pipeline operator. The data covers a three-year period, as only such data has been made public. The explanatory variables include the daily values of weather data such as: average temperature, chilled temperature, minimum temperature, maximum temperature, atmospheric pressure, relative humidity, wind speed and wind direction.
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Fung, Manson, and Christopher A. Kennedy. "An Integrated Macroeconomic Model for Assessing Urban Sustainability." Environment and Planning B: Planning and Design 32, no. 5 (October 2005): 639–56. http://dx.doi.org/10.1068/b31113.
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A macrolevel approach for integrating regional economic models with urban metabolism models is developed. Challenges with consistency and aggregation persist in the integration of urban models between socioeconomic and environmental systems, and between the micro and macro scale. Using an econometric model as a foundation offers a flexible structure, with low data requirements, that might potentially be integrated with microlevel process models. Such an econometric model is developed and verified for the economy of the Toronto region. The economic model is integrated with a greenhouse gas emission model that simulates emissions from the residential, transportation, and solid waste sectors. Emissions are simulated to 2010 for optimistic and pessimistic exogenous economic climates. In the absence of technological change, emissions will increase in the order of 30%, largely as a result of population growth (22%–23%), which is relatively insensitive to economic growth. The potential to decrease emissions through changing land-use development and increased recycling of solid waste is examined.
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Franses, Philip Hans, and Timo Teräsvirta. "INTRODUCTION TO THE SPECIAL ISSUE: NONLINEAR MODELING OF MULTIVARIATE MACROECONOMIC RELATIONS." Macroeconomic Dynamics 5, no. 4 (September 2001): 461–65. http://dx.doi.org/10.1017/s136510050102301x.
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During the past decade, the popularity of nonlinear models in econometrics has been increasing quite rapidly. Nonlinear models are now widely used for modeling macroeconomic relationships, and they also are used frequently in financial econometrics. The most popular nonlinear models have been univariate. Threshold autoregressive, Markov switching autoregressive, and smooth-transition autoregressive models, just to name a few popular families of models, have been widely applied to modeling of macroeconomic series. Even nonlinear multivariate single-equation models have found application in areas where linear single-equation models traditionally have been used, such as modeling the demand for money, real exchange rates, consumption–income relationship, and house prices. Interest in nonlinearities in the Phillips curve also has grown recently.
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FRANCO, Ray John Gabriel, and Dennis S. MAPA. "ANALYZING THE DYNAMICS OF GROSS DOMESTIC PRODUCT GROWTH. A MIXED FREQUENCY MODEL APPROACH." Theoretical and Practical Research in the Economic Fields 5, no. 2 (December 31, 2014): 117. http://dx.doi.org/10.14505/tpref.v5.2(10).01.
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Frequency mismatch has been a problem in time series econometrics. Many monthly economic and financial indicators are normally aggregated to match quarterly macroeconomic series such as Gross Domestic Product when performing econometric analysis. However, temporal aggregation, although widely accepted, is prone to information loss. To address this issue, mixed frequency modelling is employed by using state space models with time-varying parameters. Quarter-on-quarter growth rate of GDP estimates are treated as monthly series with missing observation. Using Kalman filter algorithm, state space models are estimated with eleven monthly economic indicators as explanatory variables. A one-step-ahead forecast for GDP growth rates is generated and as more indicators are included in the model, the predicted values became closer to the actual data. Further evaluation revealed that among the group competing models, using Consumer Price Index (CPI), growth rates of Philippine Stock Exchange Index (PSEi), Exchange Rate, Real Money Supply, Wholesale Price Index (WPI) and Merchandise Exports are the more important determinants of GDP growth and generated the most desirable forecasts (lower forecast errors).
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RICHARDS, GORDON R. "FRACTALITY IN A MACROECONOMIC MODEL: NONLINEAR OSCILLATION AROUND A LONG-TERM EQUILIBRIUM." Fractals 10, no. 02 (June 2002): 235–51. http://dx.doi.org/10.1142/s0218348x02001063.
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Recent studies have established that macroeconomic time series exhibit fractal properties. Empirical tests here demonstrate that interest rates, exchange rates, output and prices all show evidence of a non-integer fractal dimension. Several classes of volatility models widely used in econometrics can give rise to fractality. In the paradigm proposed here, fractality results from multiplicative relationships between residual noise terms in simultaneous equation systems. The emergence of fractality in a large-scale econometric model is analyzed. The model uses well-established structural equations, so that all variables converge toward their equilibrium paths in the long run. The forecasted paths are then embedded in noise, and the model is re-simulated at a higher frequency. The simultaneity of the model equations causes the embedding noise to take on fractal properties. Multi-scaling demonstrates that the model simulations reproduce the fractal properties of the real-world time series reasonably well. Finally, it is possible to forecast at short horizons using an algorithm that exploits two aspects of fractality, scaling symmetries and intermittency. Ratios of rates of change capture proximate symmetries. A logit regression is used to predict the conditional probability of extreme events.
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Simionescu, Bratu Mihaela. "Predicting Macroeconomic Indicators in the Czech Republic Using Econometric Models and Exponential Smoothing Techniques." South East European Journal of Economics and Business 7, no. 2 (November 1, 2012): 89–99. http://dx.doi.org/10.2478/v10033-012-0017-3.
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Abstract Econometric modeling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in the Czech Republic various accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For the Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2) models, ARMA procedure and models with lagged variables). One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions were more influenced by the recent evolution of the indicators.
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CHRISTODOULAKIS, NICOS M., JESSICA GAINES, and PAUL LEVINE. "MACROECONOMIC POLICY USING LARGE ECONOMETRIC RATIONAL EXPECTATIONS MODELS: METHODOLOGY AND APPLICATION ‡." Oxford Economic Papers 43, no. 1 (January 1991): 25–58. http://dx.doi.org/10.1093/oxfordjournals.oep.a041986.
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LEAL, CARLOS IVAN SIMONSEN, and SÉRGIO RIBEIRO DA COSTA WERLANG. "Macroeconomia com M4." Brazilian Journal of Political Economy 15, no. 1 (January 1995): 70–83. http://dx.doi.org/10.1590/0101-31572000-0741.
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RESUMO Este artigo é um remake de modelos macroeconômicos comuns, como o IS-LM. Utilizamos como exógeno (sob o controle do governo) o amplo agregado monetário M4. Começamos por uma análise cuidadosa da identidade dos Walras, estendendo Simonsen (1983). Como resultado, é possível ver que, com M4, é necessário abandonar a curva IS (isto é, equilíbrio no mercado de bens e serviços) ou a curva LM (equilíbrio no mercado monetário). Em seguida, analisamos a demanda por M4 no Brasil. Finalmente, estudamos um modelo macroeconômico LM4 - LM. A utilidade do modelo é clara: é possível verificar diretamente o impacto das variações no M4 nas variáveis macro.
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Fair, Ray C. "Trade models and macroeconomics." Economic Modelling 94 (January 2021): 296–302. http://dx.doi.org/10.1016/j.econmod.2020.10.007.
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Nixon, James, and Ray C. Fair. "Testing Macroeconomic Models." Economica 63, no. 252 (November 1996): 704. http://dx.doi.org/10.2307/2555006.
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Misztal, Piotr. "Foreign Direct Investment, Production Factors Productivity and Income Inequalities in Selected CEE Countries." TalTech Journal of European Studies 10, no. 1 (June 1, 2020): 146–72. http://dx.doi.org/10.1515/bjes-2020-0008.
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Abstract The issue of global economic inequality has inspired researchers to explore the potential connection between income inequalities and foreign direct investment (FDI), as it is one of the driving forces of globalization. Although there is a large body of theoretical as well as empirical studies linking these variables, the empirical literature on the relationship between FDI, production factors productivity and income inequalities is not conclusive because most scientists treat FDI as uniform. Therefore there is a lack of reliable empirical evidence on the distributional effects of FDI, especially in emerging countries, such as in Central and Eastern Europe (CEE). The research presented in the article fills this gap. The aim of the study is to analyze the impact of the inflow of foreign direct investment on the productivity of production factors (labor, capital and total factor productivity) and income inequality of households in four Central and Eastern European countries (Poland, the Czech Republic, Slovakia and Hungary) in the period 1990–2016. The four countries were selected for analysis as a classic example of European countries transforming their economic structures and similar in terms of the level of economic development. In turn, the choice of the analysis period was related to the availability of necessary statistical data. According to the theory of economics, the inflow of foreign direct investment should have a positive impact on production factors productivity as well as on income inequalities of households in investment receiving countries. In the study, a research method based on the study of economic literature in macroeconomics and international finance and econometric methods (vector autoregression models—VAR) was used. Results of the research suggest a significant and positive impact of greenfield investment inflow on labor productivity and total factor productivity, as well as a positive impact of brownfield investment inflow (mergers and acquisitions) on capital productivity in countries receiving investments. Moreover, the results also revealed the lack of a statistically significant impact of greenfield and brownfield investment on income inequalities in all of the examined countries. The statistical data used in the study came from the statistical databases of the Organization for Economic Cooperation and Development (OECD), the World Bank (World Development Indicators), World Income Inequality Database (United Nations University World Institute for Development Economics Research) and Total Economy Database (The Conference Board of Canada).
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Pretorius, Marinda, and Ilse Botha. "A macroeconomic model for South Africa: A non-linear econometric modelling approach." Journal of Economic and Financial Sciences 1, no. 1 (April 29, 2007): 51–66. http://dx.doi.org/10.4102/jef.v1i1.379.
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Econometric models are often made up of assumptions that never truly match reality. One of the most challenged requirements is that the coefficients of econometric models remain constant over time, in the sense that it is assumed that the future will be similar to the past. If the assumption of constant coefficients is not satisfied, any conclusions reached from normal (constant coefficient) models will be biased. Another, very closely related, contested assumption is that the functional form (usually linear) of a model remains unchanged over time. The theory of linearity has long been the centre of all econometric model-building. According to Teräsvirta (1994), if linear estimates were not successful in practice, they would have been forsaken long ago, and this has certainly not been the case. Quite the opposite has been experienced: some very influential ideas based on the linear relationships between variables, like cointegration analysis, have been established. Nonetheless, there are definite situations in which linear models are unable to grasp the underlying economic theory of the data accurately. This article addresses the problem of non-linearity by applying smooth transition autoregressive (STAR) specifications to an existing simultaneous macroeconomic model of the South African economy. The results support the view that non-linear models provide better forecasts than linear specifications of equations.
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Nymoen, Ragnar. "Economic Covid-19 effects analysed by macro econometric models—the case of Norway." National Accounting Review 5, no. 1 (2023): 1–22. http://dx.doi.org/10.3934/nar.2023001.
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<abstract><p>Counterfactual analysis of the impact of Covid-19 can be based on a solution of a macroeconomic model for a scenario without the coronavirus interfering with the macroeconomic system. Two measures of impact are introduced with the aid of a simple theoretical model, and then used in the empirical analysis: (Ⅰ) The difference between the counterfactual without Covid-19 and a baseline model solution. (Ⅱ) The difference between the counterfactual and the actual development of the economy. In order to analyze the impact on GDP we use two model categories. First, empirical final form model equations, which were purpose-built with the aid of a machine learning algorithm. Second, an operational multiple-equation model of the Norwegian macroeconomic system. Empirically, we find a significant impact of Covid-19 on the GDP Mainland Norway in 2020. For some of the estimator/model combinations, the impacts are also significant in the two first quarters of 2021. Using the multiple-equation model, the assessment is extended to the impact of Covid-19 on value added in four Mainland Norway industries, on imports and exports, and on final consumption expenditure and gross capital formation.</p></abstract>
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CAPRIATA, WILLIAM, and LEONARDO FLAUZINO DE SOUZA. "The exchange rate in Orthodox, Keynesian and New Developmentalism theoretical models: a literature review." Brazilian Journal of Political Economy 41, no. 2 (April 2021): 220–35. http://dx.doi.org/10.1590/0101-31572021-3126.
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ABSTRACT The main purpose of this paper is to present the differences in the exchange rates in macroeconomic models from the three current theoretical views: Orthodox, Post-Keynesian and New Developmentalism. To achieve this objective, it is proposed to make a bibliographic survey of the literature on open macroeconomics and exchange rate. The main differences among these views concerns to exchange rate determination, causes of exchange rate variations and balance of payments equilibrium determination.
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Coeurdacier, Nicolas, and Hélène Rey. "Home Bias in Open Economy Financial Macroeconomics." Journal of Economic Literature 51, no. 1 (March 1, 2013): 63–115. http://dx.doi.org/10.1257/jel.51.1.63.
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Home bias is a perennial feature of international capital markets. We review various explanations of this puzzling phenomenon highlighting recent developments in macroeconomic modeling that incorporate international portfolio choices in standard two-country general equilibrium models. We refer to this new literature as Open Economy Financial Macroeconomics. We focus on three broad classes of explanations: (i) hedging motives in frictionless financial markets (real exchange rate and nontradable income risk), (ii) asset trade costs in international financial markets (such as transaction costs or differences in tax treatments between national and foreign assets), and (iii) informational frictions and behavioral biases. Recent theories call for new portfolio facts beyond equity home bias. We present new evidence on cross-border asset holdings across different types of assets: equities, bonds and bank lending and new micro data on institutional holdings of equity at the fund level. These data should inform macroeconomic modeling of the open economy and a growing literature of models of delegated investment. (JEL E13, F41, G11, G12, G15)
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Capps, Oral. "Dynamics of macroeconomic factor effects on food assistance program participation in the United States." PLOS ONE 17, no. 6 (June 13, 2022): e0269442. http://dx.doi.org/10.1371/journal.pone.0269442.
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Using polynomial distributed lag (PDL) models, the impacts of macroeconomic factors relating to economic, financial, and sociological stress and designed to be short-run predictors of U.S. economic performance are identified and assessed concerning participation in key food assistance programs (SNAP, WIC, and NSLP). The econometric analysis covers the period October 1999 to September 2020. The impact of COVID-19 on participation in these programs also is quantified. Based on the parameter estimates obtained from the econometric PDL models, ex-ante forecasts of participation in the SNAP, WIC, and NSLP subsequently are made and evaluated over the period October 2020 to August 2021. The empirical results show that different sets of macroeconomic drivers affect participation levels across the respective food assistance programs. No macroeconomic factor is common across SNAP, WIC, and NSLP participation. Changes in macroeconomic conditions which influence SNAP, WIC and NSLP participation are not just contemporaneous but also affect participation levels anywhere from 1 month to 12 months later. Importantly, this research allows not only the determination of the macroeconomic factors which affect program participation but also allows the determination of the ability of the respective models to forecast program participation. As such, the Food and Nutrition Service will be in better position to assess program needs as well as to forecast program participation levels to minimize errors in the budgetary process.
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Chipman, John S. "HAAVELMO’S CONTRIBUTIONS TO SIMULTANEOUS-EQUATIONS ESTIMATION." Econometric Theory 31, no. 2 (July 31, 2014): 233–48. http://dx.doi.org/10.1017/s0266466614000280.
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This paper surveys Trygve Haavelmo’s contributions to econometrics. A brief summary of his 1944 monograph is followed by an analysis of the six important papers he contributed during 1943–47. Four of them were devoted to macroeconomic models including estimation of the Keynesian marginal propensity to consume; the first two introduced the methodology of a system of structural equations; the third marked a milestone in econometric method by deriving the reduced form of such a system; and the fourth analyzed the contrast between time-series and cross-section analysis. The fifth (joint with M.A. Girshick) on the demand for food provided a definitive treatment of estimation of demand and supply functions; it carried out the execution of a five-equation structural model of the U.S. economy. The sixth was an interesting policy model of the interrelationship between the agricultural and the rural sector of the economy, which was fitted to U.S. data and addressed to questions of policy.
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Wan Yaacob, Wan Fairos, and Wan Zakiyatussariroh Wan Husin. "Modelling Malaysian Road Accident Deaths : An Econometric Approach." Social and Management Research Journal 3, no. 1 (June 1, 2006): 99. http://dx.doi.org/10.24191/smrj.v3i1.5105.
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A number of methods have been proposed for dealing with road accident death model. This paper uses econometric regression models to develop the road accident death model. By using this approach, this paper attempts to establish a statistical model to describe the relationship between the total road accident deaths and a range ofexplanatory macroeconomic variables. The macroeconomic factors used in the model include population, the number of registered vehicles, road length, technique of data coverage, system of data recording and Gross Domestic Product. The results suggest that the POp, ROADL, VEH and DR do not have any impact on road accident deaths. In contrast, the GDP and Technique of data Coverage were found to be highly significant (P < 0.05) in explaining the road accident deaths.
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Geweke, John, and Gianni Amisano. "Prediction with Misspecified Models." American Economic Review 102, no. 3 (May 1, 2012): 482–86. http://dx.doi.org/10.1257/aer.102.3.482.
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The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard US postwar quarterly macroeconomic time series.
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Shpak, Nestor, Solomiya Ohinok, Ihor Kulyniak, Włodzimierz Sroka, Yuriy Fedun, Romualdas Ginevičius, and Joanna Cygler. "CO2 Emissions and Macroeconomic Indicators: Analysis of the Most Polluted Regions in the World." Energies 15, no. 8 (April 15, 2022): 2928. http://dx.doi.org/10.3390/en15082928.
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There is no sector of the economy that is not dependent on the state of development of the energy sector. This sector produces a significant share of global CO2 emissions. Harmful CO2 emissions and greenhouse gas emissions accelerate global warming. Therefore, more and more countries are adopting a strategy for the transition to carbon-neutral energy. However, energy independence and economic competitiveness are closely linked. One cannot analyze them separately. Given these facts, we focused on conducting an econometric study of the impact of key macroeconomic indicators on the level of CO2 emissions into the air in the United States and the Asia-Pacific region as the regions with the largest CO2 emissions. The modeling was carried out using the method of a correlation–regression analysis with the subsequent construction of econometric models. The quality of the built econometric models was checked using the coefficient of determination and Fisher’s criterion. The sample of statistics was formed from all the available values of the World Bank’s annual indicators for the period 1970–2020. The findings achieved showed that: (i) The results of our study confirmed the dependence of CO2 emissions on macroeconomic factors such as GDP, exports and imports, the rate of inflation, and unemployment. It allows the governments of many countries to use research findings to diagnose, monitor, and forecast macroeconomic outcomes to reduce or maintain allowable CO2 emissions. (ii) Identifying and assessing economic losses from environmental pollution by CO2 emissions using econometric models will allow to ensure effective public environmental and economic policies aimed at reducing harmful CO2 emissions into the air. It may be regarded as the practical importance of our study.
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Simionescu, Mihaela. "The Accuracy Assessment of Macroeconomic Forecasts based on Econometric Models for Romania." Procedia Economics and Finance 8 (2014): 671–77. http://dx.doi.org/10.1016/s2212-5671(14)00143-9.
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Morley, James, Irina B. Panovska, and Tara M. Sinclair. "TESTING STATIONARITY WITH UNOBSERVED-COMPONENTS MODELS." Macroeconomic Dynamics 21, no. 1 (March 8, 2016): 160–82. http://dx.doi.org/10.1017/s1365100515000437.
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In the aftermath of the global financial crisis, competing measures of the trend in macroeconomic variables such as U.S. real GDP have featured prominently in policy debates. A key question is whether large shocks to macroeconomic variables will have permanent effects—i.e., in econometric terms, do the data contain stochastic trends? Unobserved-components models provide a convenient way to estimate stochastic trends for time series data, with their existence typically motivated by stationarity tests that allow at most a deterministic trend under the null hypothesis. However, given the small sample sizes available for most macroeconomic variables, standard Lagrange multiplier tests of stationarity will perform poorly when the data are highly persistent. To address this problem, we propose the use of a likelihood ratio test of stationarity based directly on the unobserved-components models used in estimation of stochastic trends. We demonstrate that a bootstrap version of this test has far better small-sample properties for empirically relevant data-generating processes than bootstrap versions of the standard Lagrange multiplier tests. An application to U.S. real GDP produces stronger support for the presence of large permanent shocks using the likelihood ratio test than using the standard tests.
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Holden, Kenneth, and Andrew Britton. "Policymaking with Macroeconomic Models." Economic Journal 99, no. 397 (September 1989): 861. http://dx.doi.org/10.2307/2233780.
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