Academic literature on the topic 'Macroeconomics – Econometric models'

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.

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.

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.”

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.

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.

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.

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.

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.

<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>

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.

Thesis (PhD)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: In the first essay of this thesis, a medium scale DSGE model is developed and estimated for the South African economy. When used for forecasting, the model is found to outperform private sector economists when forecasting CPI inflation, GDP growth and the policy rate over certain horizons. In the second essay, the benchmark DSGE model is extended to include the yield on South African 10-year government bonds. The model is then used to decompose the 10-year yield spread into (1) the structural shocks that contributed to its evolution during the inflation targeting regime of the South African Reserve Bank, as well as (2) an expected yield and a term premium. In addition, it is found that changes in the South African term premium may predict future real economic activity. Finally, the need for DSGE models to take account of financial frictions became apparent during the recent global financial crisis. As a result, the final essay incorporates a stylised banking sector into the benchmark DSGE model described above. The optimal response of the South African Reserve Bank to financial shocks is then analysed within the context of this structural model.

This thesis examines empirical and theoretical issues related to the role of uninsurable individual risk and heterogeneity in macroeconomics. The thesis includes four chapters. The first chapter uses data from the Panel Study of Income Dynamics (PSID) to test full risk-sharing among North American households. The second chapter is a short essay where I use simulated data to show how the method applied in the previous chapter can be used to distinguish between partial risk sharing and imperfect credit markets. The third chapter develops a heterogeneous agent dynamic general equilibrium model which jointly models aggregate saving and employment. Finally, the fourth chapter investigates empirically the ability of financial market incompleteness to help explaining the equity premium puzzle. The central motivation throughout this dissertation is the recognition that the interaction between cross-sectional volatility and aggregate volatility is of fundamental importance to understand the way we should model macroeconomic aggregates such as aggregate consumption, asset prices and business cycle fluctuations.

Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

This Thesis is composed by three independent papers that investigate

central debates in empirical macroeconomic modeling.

Chapter 1, entitled “A Model for Real-Time Data Assessment with an Application to GDP Growth Rates”, provides a model for the data

revisions of macroeconomic variables that distinguishes between rational expectation updates and noise corrections. Thus, the model encompasses the two polar views regarding the publication process of statistical agencies: noise versus news. Most of the studies previous studies that analyze data revisions are based

on the classical noise and news regression approach introduced by Mankiew, Runkle and Shapiro (1984). The problem is that the statistical tests available do not formulate both extreme hypotheses as collectively exhaustive, as recognized by Aruoba (2008). That is, it would be possible to reject or accept both of them simultaneously. In turn, the model for the

DPP presented here allows for the simultaneous presence of both noise and news. While the “regression approach” followed by Faust et al. (2005), along the lines of Mankiew et al. (1984), identifies noise in the preliminary

figures, it is not possible for them to quantify it, as done by our model.

The second and third chapters acknowledge the possibility that macroeconomic data is measured with errors, but the approach followed to model the missmeasurement is extremely stylized and does not capture the complexity of the revision process that we describe in the first chapter.

Chapter 2, entitled “Revisiting the Success of the RBC model”, proposes the use of dynamic factor models as an alternative to the VAR based tools for the empirical validation of dynamic stochastic general equilibrium (DSGE) theories. Along the lines of Giannone et al. (2006), we use the state-space parameterisation of the factor models proposed by Forni et al. (2007) as a competitive benchmark that is able to capture weak statistical restrictions that DSGE models impose on the data. Our empirical illustration compares the out-of-sample forecasting performance of a simple RBC model augmented with a serially correlated noise component against several specifications belonging to classes of dynamic factor and VAR models. Although the performance of the RBC model is comparable

to that of the reduced form models, a formal test of predictive accuracy reveals that the weak restrictions are more useful at forecasting than the strong behavioral assumptions imposed by the microfoundations in the model economy.

The last chapter, “What are Shocks Capturing in DSGE modeling”, contributes to current debates on the use and interpretation of larger DSGE

models. Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies point out that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin and

Sala, 2005, Uhlig, 2004). In this Chapter, we propose to reconcile both views by considering an alternative DSGE estimation approach which

models explicitly the statistical agency along the lines of Sargent (1989). This enables us to distinguish whether the exogenous shocks in DSGE

modeling are structural or instead serve the purpose of fitting the data in presence of misspecification and measurement problems. When applied to the original Smets and Wouters (2007) model, we find that the explanatory power of the structural shocks decreases at high frequencies. This allows us to back out a smoother measure of the natural output gap than that

resulting from the original specification.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

This study argues that governments with sustained GDP growth, open markets, low country risk, high levels and low standard deviation of government performance, and few or no occurrences of war, will see larger levels of foreign direct investment (FDI) over time. Scholarship on the determinants of FDI variously argues the influence of GDP growth, the openness of a country's economy, a government's level of political capacity, the level of country risk, and the negative effects of inter-, intra- and extrastate conflict. These studies on the various effects on FDI, while providing insightful and substantial statistical results, fail to capture the simultaneous effects of macroeconomic, government performance, country risk, and war variables. The present study attempts to resolve this gap in the literature on FDI by proposing a multi-dimensional model of the combined effects of un-weighted macroeconomic, political, country risk, and war variables on FDI flows over time. The empirical results confirm the expected multi-dimensional nature of FDI flows over time and provide insight into the macroeconomic and political effects on regional and country-level yearly flows of FDI, as well as yielding some unexpected and counter-intuitive results of the role war plays on FDI flows over time.

China has been increasingly sending more contracts to work on projects in Africa. This study tests the effects of Chinese contracts on economic growth in 50 African countries as well as the correlation between Chinese contracts and other economic indicators. The paper uses data from the World Bank and National Bureau of Statistics of China starting from 2000-2015. This study finds that from 2000 to 2015, Chinese contracts have not been significant in economic growth of all African countries. The analysis does find that Chinese contracts are significant to economic growth when considering only the top five countries who have received the most contracts on average.

This dissertation contains a series of essays that provide empirical evidence for Australia on some fundamental predictions of real business cycle models and on the convergence and persistence of real interest rates. Chapter 1 provides a brief introduction to the issues examined in each chapter and provides an overview of the methodologies that are used. Tests of various basic predictions of standard real business cycle models for Australia are presented in Chapters 2, 3 and 4. Chapter 2 considers the question of great ratios for Australia. These are ratios of macroeconomic variables that are predicted by standard models to be stationary in the steady state. Using time series econometric techniques (unit root tests and cointegration tests) Australia great ratios are examined. In Chapter 3 a more restrictive implication of real business cycle models than the existence of great ratios is considered. Following the methodology proposed by Canova, Finn and Pagan (1994) the equilibrium decision rules for some standard real business cycle are tested on Australian data. The final essay on this topic is presented in Chapter 4. In this chapter a large-country, small-country is used to try and understand the reason for the sharp rise in Australia??s share of world output that began around 1990. Chapter 5 discusses real interest rate linkages in the Pacific Basin region. Vector autoregressive models and bootstrap methods are adopted to study financial linkages between East Asian markets, Japan and US. Given the apparent non-stationarity of real interest rates a related issue is examined in Chapter 6, viz. the persistence of international real interest rates and estimation of their half-life. Half-life is selected as a means of measuring persistence of real rates. Bootstrap methods are employed to overcome small sample issues in the estimation and a non-standard statistical inference methodology (Highest Density Regions) is adopted. Chapter 7 reapplies the High Density Regions methodology and bootstrap half-life estimation to the data used in Chapters 2 and 5. This provides a robustness check on the results of standard unit root tests that were applied to the data in those chapters. Main findings of the thesis are as follows. The long run implications of real business cycle models are largely rejected by the Australia data. This finding holds for both the existence of great ratios and when the explicit decision rules are employed. When the small open economy features of the Australian economy are incorporated in a two country RBC model, a country-specific productivity boom seems to provide a possible explanation for the rise in Australia??s share of world output. The essays that examine real interest rates suggest the following results. Following the East Asian financial crisis in 1997-98 there appears to have been a decline in the importance of Japan in influencing developments in the Pacific Basin region. In addition there is evidence that following the crisis Korea??s financial market became less insular and more integrated with the US. Finally results obtained from the half-life estimators suggest that despite the usual findings from unit root tests, real interest rates may in fact exhibit mean-reversion.

This thesis contains three essays covering different topics in the field of statistics

and econometrics of survey data. Chapters one and two analyse two aspects

of the Survey of Professional Forecasters (SPF hereafter) dataset. This survey

provides a large information on macroeconomic expectations done by the professional

forecasters and offers an opportunity to exploit a rich information set.

But it poses a challenge on how to extract the relevant information in a proper

way. The last chapter addresses the issue of analyzing the opinions on the euro

reported in the Flash Eurobaromenter dataset.

The first chapter Measuring Uncertainty and Disagreement in the European

Survey of Professional Forecasters proposes a density forecast methodology based

on the piecewise linear approximation of the individual’s forecasting histograms,

to measure uncertainty and disagreement of the professional forecasters. Since

1960 with the introduction of the SPF in the US, it has been clear that they were a

useful source of information to address the issue on how to measure disagreement

and uncertainty, without relying on macroeconomic or time series models. Direct

measures of uncertainty are seldom available, whereas many surveys report point

forecasts from a number of individual respondents. There has been a long tradition

of using measures of the dispersion of individual respondents’ point forecasts

(disagreement or consensus) as proxies for uncertainty. Unlike other surveys, the

SPF represents an exception. It directly asks for the point forecast, and for the

probability distribution, in the form of histogram, associated with the macro variables

of interest. An important issue that should be considered concerns how to

approximate individual probability densities and get accurate individual results

for disagreement and uncertainty before computing the aggregate measures. In

contrast to Zarnowitz and Lambros (1987), and Giordani and Soderlind (2003) we

overcome the problem associated with distributional assumptions of probability

density forecasts by using a non parametric approach that, instead of assuming

a functional form for the individual probability law, approximates the histogram

by a piecewise linear function. In addition, and unlike earlier works that focus on

US data, we employ European data, considering gross domestic product (GDP),

inflation and unemployment.

The second chapter Optimal Combination of Survey Forecasts is based on

a joint work with Christine De Mol and Domenico Giannone. It proposes an

approach to optimally combine survey forecasts, exploiting the whole covariance

structure among forecasters. There is a vast literature on forecast combination

methods, advocating their usefulness both from the theoretical and empirical

points of view (see e.g. the recent review by Timmermann (2006)). Surprisingly,

it appears that simple methods tend to outperform more sophisticated ones, as

shown for example by Genre et al. (2010) on the combination of the forecasts in

the SPF conducted by the European Central Bank (ECB). The main conclusion of

several studies is that the simple equal-weighted average constitutes a benchmark

that is hard to improve upon. In contrast to a great part of the literature which

does not exploit the correlation among forecasters, we take into account the full

covariance structure and we determine the optimal weights for the combination

of point forecasts as the minimizers of the mean squared forecast error (MSFE),

under the constraint that these weights are nonnegative and sum to one. We

compare our combination scheme with other methodologies in terms of forecasting

performance. Results show that the proposed optimal combination scheme is an

appropriate methodology to combine survey forecasts.

The literature on point forecast combination has been widely developed, however

there are fewer studies analyzing the issue for combination density forecast.

We extend our work considering the density forecasts combination. Moving from

the main results presented in Hall and Mitchell (2007), we propose an iterative

algorithm for computing the density weights which maximize the average logarithmic

score over the sample period. The empirical application is made for the

European GDP and inflation forecasts. Results suggest that optimal weights,

obtained via an iterative algorithm outperform the equal-weighted used by the

ECB density combinations.

The third chapter entitled Opinion surveys on the euro: a multilevel multinomial

logistic analysis outlines the multilevel aspects related to public attitudes

toward the euro. This work was motivated by the on-going debate whether the

perception of the euro among European citizenships after ten years from its introduction

was positive or negative. The aim of this work is, therefore, to disentangle

the issue of public attitudes considering either individual socio-demographic characteristics

and macroeconomic features of each country, counting each of them

as two separate levels in a single analysis. Considering a hierarchical structure

represents an advantage as it models within-country as well as between-country

relations using a single analysis. The multilevel analysis allows the consideration

of the existence of dependence between individuals within countries induced by

unobserved heterogeneity between countries, i.e. we include in the estimation

specific country characteristics not directly observable. In this chapter we empirically

investigate which individual characteristics and country specificities are

most important and affect the perception of the euro. The attitudes toward the

euro vary across individuals and countries, and are driven by personal considerations

based on the benefits and costs of using the single currency. Individual

features, such as a high level of education or living in a metropolitan area, have

a positive impact on the perception of the euro. Moreover, the country-specific

economic condition can influence individuals attitudes.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

AbstractWe present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggregate information derived from both approaches is broadly in line, Shapley values offer several advantages, such as the discovery of unknown functional forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.

AbstractThis Chapter presents the NEMESIS macro-econometric model. This model has been used for several ex-ante and ex-post evaluations of the macroeconomic impact of EU R&I policies. After a general overview of the model, a thorough description of the representation of innovation in the model is provided. As an example of its workings, an application to the interim evaluation of the Horizon 2020 programme is also provided.

The aim of this study is to determine whether there is a relationship between Capital flight and some macroeconomic variables by using anual data between 1980 and 2010 in Turkey. Capital flight measured by World Bank (1985) method, was used as dependent variable and external debt, foreign direct investment, uncertainty, real GDP growth, exchange rates, trade balance and consumer price index were used as independent variables. Ordinary Least squares estimation method, Johansen-Jeselius cointegration test, Granger causality test and variance decomposition results produced by VEC model were used in the study. After those econometrics and economics analysis, this paper put forward that there is a long run relationship between some macroeconomic variables and capital flight.The results show external debt, foreign direct investment inflows, and foreign reserves to be the major effector of capital flight.

Trade openness has been subject to an important issue many studies in literature. It allows us to analyze potential trade as a percentage of gross domestic product. Total value of international trade in goods and services shows the countries’ integration into the world economy. Generally, small countries are more integrated because of their dependency on imports. On the other hand, there many variables which effects trade integration. Our study focuses on to analyze the effects on trade openness and make inferences for OECD countries. In this paper we aim to examine the relationship between trade openness and macro-economic indicators in OECD countries. To analyze the relationship, we used panel data regression analysis. Data obtained from World Bank, The Heritage Foundation and United Nations Conference on Trade and Development (UNCTAD). The panel data covers 2000-2013 periods and 33 countries. The analysis made through the Stata econometric packet program. We predicted pooled, fixed effects and random effects panel data models and analyzed them. It has been found that gross domestic savings, investment freedom, and unemployment rate are statistically significant. The results found in this paper show that investment freedom and gross domestic savings have positive effect on trade openness as we expected. On the other hand, unemployment rate has positive effect on trade openness. These findings have important policy implications for OECD countries. Our interpretation of these findings is that, integration to world economy has generally positive effects for macroeconomic factors in OECD countries, but it should be limited.

The Saudi agricultural sector relies on diesel for irrigation, which is provided to farmers at a much lower price than the average global price, implying significant opportunity costs. With the aid of soft-coupled power and macro-econometric models, we assess the cost and macroeconomic implications of electrifying irrigation activities in the Saudi agricultural sector. Three electrification scenarios are considered: electrifying each individual farm with a dedicated hybrid renewable micro-grid, electrifying the entire farm cluster with central generation and connecting the entire cluster via transmission to the national grid. Compared with the base-case, connecting the farm cluster to the national grid is found to be the most economical but the least environmentally friendly. The renewable and central generation scenarios are costlier (compared with the transmission scenario) due, respectively, to the high battery costs and gas infrastructure needed.