Macro Dataset Research Articles

Search Complementarities, Aggregate Fluctuations, and Fiscal Policy

Abstract We document five novel facts about the role of search effort in forming trading relationships among firms by combining a variety of micro and macro datasets. These facts strongly suggest the presence of search complementarities. To study the implications of these facts for aggregate fluctuations, we build a dynamic general equilibrium model, disciplined by our new firm-level evidence on search effort. The model matches key aspects of the macro and micro data that have remained unaccounted for by standard models, including the time-varying bimodal distribution of output and the strong, nonlinear propagation of shocks. Also, changes to the volatility of shocks have nonlinear effects on macroeconomic fluctuations that advance a novel interpretation of the Great Moderation. Finally, we provide a new account of the state-dependent effects of fiscal policy.

The Effect of Innovation on Employment in Türkiye

This study examines the impact of innovation on employment in the Turkish labor market between the years 1991-2021, using monthly patent grants and annual R&D expenditure statistics. As for empirical technique, ARDL (autoregressive distributed lag) approach is used. The reason for choosing this approach is that it separates the long-term and short-term results and gives better results in analysis with a lower number of observations than other methods. The difference between the results of this study from the literature is that the analysis was performed in two different time periods, with two different proxy variables, and they gave the same result as proof of the robustness of the results. When the long-run model and the short-run model are investigated separately, it is found that while the effect of innovation on employment is negative in the short-run, it turns out to be positive in the long-run. Thus, during the period 1991-2021 in the Turkish labor market, while innovation might negatively affect employment levels to some extent in the short run, innovation could exert a more structural and sustainable positive impact on employment levels in the long run. In the short-run, the negative effect of innovation on employment can be seen as a kind of creative destruction, but in the long-run, the positive effect of innovation supports the hypothesis that the increase in the education and training levels of workers along with the profit and productivity provided by innovation increases employment by adapting workers to innovation. The aim of this study is to make an inference with macro data sets but, using micro-level, firm data may provide significant results on the effect of innovation on employment.

Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation.

Credit Constraints and Innovation Activities: Empirical Evidence on Russian Enterprises

This article studies the relationship between the credit constraints on Russian enterprises and their decision to introduce product innovation, process innovation, and to spend on research and development (R&D). The evidence regarding the relationship between R&D spending remains somewhat ambiguous and could differ between countries. A cross-sectional macro dataset of the World Bank Enterprises Survey in Russia in 2019 is used in a system of seemingly unrelated regressions. The results show that the existence of credit constraints is associated with a lower probability of introducing product innovations and spending on R&D activities. Nevertheless, there is no significant relationship between being credit constrained and the enterprise decision to introduce process innovations. The importance of this article stems from the fact that previous works showed that these relationships differ by country and that these relationships are considered simultaneously, while other works concentrate mainly on one of these relationships.

Analysis of the excess mortality and factors associated with deaths from COVID-19 versus other causes in Central Tuscany (Italy) in 2020.

to assess the extent of the excess mortality from all causes in 2020 compared to 2015-2019 in Central Tuscany (Italy) as a proxy to estimate COVID-19-related excess mortality and to identify demographic and clinical differences between subjects who died from COVID-19 and those who died from other causes in 2020. descriptive analysis of the temporal trend of general mortality. the study population is represented by the 1.6 million residents living in the territory of the Central Tuscany Healthcare Authority in Central Italy, i.e., little less than half of the population of Tuscany, in an area of just over one fifth of the entire region, where the provinces of Florence, Pistoia, and Prato are comprised.MAIN OUTCOME MEASURES: using the Italian National Resident Population Registry (ANPR) as a source of mortality data, standardized mortality ratios with 95% confidence intervals were calculated to compare the number of deaths in 2020 with the number of deaths expected on the basis of mortality data from 2015 to 2019. Furthermore, after record linkage with data from the integrated surveillance of cases of SARS-CoV-2 virus infection and with the MaCro dataset of comorbidities, the characteristics of subjects who died from COVID-19 were compared with those of patients who died from other causes using a multivariate logistic regression model; odds ratios with 95% confidence intervals were calculated. a statistically significant excess mortality was observed during the first pandemic wave in March and April, and during the second wave in the fall; it ranged between +9% in March and +51% in November. On the contrary, in January, February, and May, all-cause mortality was significantly lower than in previous years. The male gender, dyslipidaemia, and dementia were positively associated with death from COVID-19 rather than from all other causes. On the contrary, heart failure and recent tumours were more represented among deaths from other causes.CONCLUSIONS: much of the over-mortality observed in spring is attributable to the harvesting effect COVID-19 exerted on a segment of population with serious underlying chronic conditions and who in the previous months had survived a mild winter and a flu season of medium intensity. In the second pandemic wave, in autumn, the impact of both direct and indirect effects of COVID-19 was substantially higher. Consistently with the available evidence, death from COVID-19 was related to the male gender and to clinical conditions such as dyslipidaemia and dementia.

Energy transition at local level: Analyzing the role of peer effects and socio-economic factors on UK solar photovoltaic deployment

A growing literature highlights the presence of spatial differences in solar photovoltaic (PV) adoption patterns. Central to forward planning is an understanding of what affects PV growth, yet insights into the determinants of PV adoption in the literature are limited. What factors do drive the adoption at local level? Are the effects of these factors geographically uniform or are there nuances? What is the nature of these nuances? Existing studies so far use aggregate macro datasets with limited ability to capture the role of peer effects. This paper considers some established variables but also broadens the base of variables to try to identify new indicators relating to PV adoption. Specifically, it analyses domestic PV adoption in the UK at local level using data on the number of charities as a proxy to capture the opportunities to initiate social interactions and peer effects. A geographically weighted regression model that considers the spatially varying relationship between PV adoption and socio-economic explanatory variables reveals significantly more variability than the global regression. Our results show that charities and self-employment positively influence PV uptake while other socio-economic variables such as population density has bidirectional impacts.

ACTSMLT: Automatic Classification of Text Summarization using Machine Learning Technique

In today’s world, due to the steep rise in internet users, Community Question Answering (CQA) has attracted many research communities. In order to provide the correct and perfect answer to the user asked question from a given large collection of text data, understanding the question properly to suggest a precise answer is a challenging task. Therefore, Question Answering (QA) system is a challenging task than a common information retrieval task done by many search engines. In this paper, an automatic prediction of the quality of CQA answers is proposed. This is accomplished by using five well known machine learning algorithms. Usually, questions asked by the user are based on a topic or theme. We try to exploit this feature in our work by identifying the category of the question posted and further map with the corresponding question. Similarly, for the answers posted by the multiple user’s are processed as answer for category mapping. Here, the results show that for Question Classification (QA), Linear Support Vector Classification (LSVC) is found to be the best classifier and Multinomial Logistic Regression (MLR) is the most suitable for Answer Classification (AC). The MS Macro dataset is used as the underlying dataset for retrieving and testing the question and answer classifiers. The Yahoo Answers are used as a golden reference during the testing throughout our experiments. Experiments results show that the proposed technique is efficient and outperforms Metzler and Kanungo’s (MK++) [1] while providing the best answer summary satisfying the user’s queries.

Measuring Uncertainty for New Zealand Using Data‐Rich Approach

AbstractThis paper develops two state‐of‐the‐art uncertainty indices for New Zealand by exploiting two separate data‐rich environments. The first index follows the methodology outlined in Jurado, Ludvigson and Ng (2015) to construct an estimate of uncertainty based on a large New Zealand macro dataset. The second index is constructed based on freely accessible and real time Google Trends data to provide a real‐time and freely‐accessible measure of uncertainty as in Castelnuovo and Tran (2017) and Shields and Tran (2018). Both indices do a reasonable job measuring uncertainty in New Zealand. VAR evidence documents significant impacts of uncertainty shocks on GDP in New Zealand.

Resource scarcity drives lethal aggression among prehistoric hunter-gatherers in central California

The origin of human violence and warfare is controversial, and some scholars contend that intergroup conflict was rare until the emergence of sedentary foraging and complex sociopolitical organization, whereas others assert that violence was common and of considerable antiquity among small-scale societies. Here we consider two alternative explanations for the evolution of human violence: (i) individuals resort to violence when benefits outweigh potential costs, which is likely in resource poor environments, or (ii) participation in violence increases when there is coercion from leaders in complex societies leading to group level benefits. To test these hypotheses, we evaluate the relative importance of resource scarcity vs. sociopolitical complexity by evaluating spatial variation in three macro datasets from central California: (i) an extensive bioarchaeological record dating from 1,530 to 230 cal BP recording rates of blunt and sharp force skeletal trauma on thousands of burials, (ii) quantitative scores of sociopolitical complexity recorded ethnographically, and (iii) mean net primary productivity (NPP) from a remotely sensed global dataset. Results reveal that sharp force trauma, the most common form of violence in the record, is better predicted by resource scarcity than relative sociopolitical complexity. Blunt force cranial trauma shows no correlation with NPP or political complexity and may reflect a different form of close contact violence. This study provides no support for the position that violence originated with the development of more complex hunter-gatherer adaptations in the fairly recent past. Instead, findings show that individuals are prone to violence in times and places of resource scarcity.

Balance Sheet Analysis in Fund Surveillance - Reference Note

This note serves as a reference for balance sheet analysis, which should be read in conjunction with the IMF board paper on Balance Sheet Analysis in Fund Surveillance. It provides a: compendium of good examples of balance sheet analysis from both bilateral and multilateral surveillance, covering a variety of topics; full listing of available balance sheet related macro datasets, including their relevance for surveillance, remaining limitations, and remedial measures being undertaken; summary of data availability for each Fund member; compilation of all the tools for balance sheet analysis developed by the Fund over the last decade; and toolkit featuring some new empirical applications that could help deepen balance sheet analysis in surveillance. These include illustrations of how to construct and use BSA matrices, general equilibrium and reduced form approaches, as well as tools to analyze sectoral vulnerabilities using micro data.

Are wages equal across sectors of production?

AbstractThe assumption that national labour markets are homogeneous across tradable and non‐tradable goods is common in multisector (open‐economy) macro models and crucial for the prominent Balassa–Samuelson hypothesis. To test it, this study introduces a novel, theory‐based method of distinguishing the tradable and non‐tradable sectors to the Balassa–Samuelson literature and employs modern empirical methods and a large and detailed macro dataset. It finds that both the internal relationship between productivity and wages in the tradable and non‐tradable sectors postulated by the Balassa–Samuelson hypothesis and its external transmission mechanism are rejected.

Testing causality between human development and economic growth: a panel data approach

This study provides evidence on human development-economic growth causality by employing a large macro panel dataset. We followed the UNDP’s approach for human development. With the help of Hurlin (2004) fixed coefficient approach, we tested causality between human development and economic growth (EG). The results show homogeneous causality from HD to EG. This is not the same when we tested causality from EG to HD. A heterogeneous causality is found from EG to HD. Only Korea and Singapore have bidirectional causality between human development and economic growth. On the other hand, Bangladesh and Philippine have no causality from EG → HD (human development index, education-index, life-index, and PPP index). Result suggests that HD will lead EG but EG not always lead HD.

Household's Portfolio Structure in Germany - Analysis of Financial Accounts Data 1959-2009

Based on a Financial Almost Ideal Demand System (FAIDS), this paper investigates the wealth structure of German households. The long-run wealth elasticities and interestrate elasticities were calculated using a unique new quarterly financial accounts macrodata set which covers the period from 1959 to 2009 and contains a portfolio of eight different financial assets. Descriptive analysis shows that all financial assets were characterized by substantial volatility of their weight in the portfolio of households. We found that portfolio shifts in the long run are determined significantly by changes in interest rates. The estimated model provides evidence that currency (and transferable deposits) is mainly a substitute for other assets and time deposits are typically a complement. Wealth elasticity is for most assets around unity. JEL Classification: E21, G11, C32

Are Wages Equal Across Sectors of Production? A Panel Data Analysis for Tradable and Non-Tradable Goods

The assumption that national labor markets are homogenous across tradable and non-tradable goods is common in multisector (open-economy) macro models and crucial for the prominent Balassa-Samuelson hypothesis. This study tests it with a novel method to distinguish the tradable and non-tradable sectors grounded in economic theory, modern empirical methods and a large and detailed macro data set. It finds that both the internal relationship between productivity and wages in the tradable and non-tradable sectors postulated by the Balassa-Samuelson hypothesis and its external transmission mechanism are rejected.

Households’ Portfolio Structure in Germany - Analysis of Financial Accounts Data 1959-2009

Based on a Financial Almost Ideal Demand System (FAIDS), this paper investigates the wealth structure of German households. The long-run wealth elasticities and interest-rate elasticities were calculated using a unique new financial accounts macro-data set which covers the period from 1959 to 2009 and contains a portfolio of eight different financial assets. We found that portfolio shifts in the long run are mainly determined by changes in interest rates. Furthermore, the estimated model provides evidence that currency (and transferable deposits) is mainly a substitute for other assets and that time deposits are typically a complement.

Complementarity of Inputs across Countries in Services Trade

This article argues that trade in certain services has a specific feature that does not necessarily apply to goods. In those services, the production process ends in the country where they are eventually consumed (i.e. in the importing country). Hence, we propose that services trade must use interactively inputs from both exporting and importing countries and thus, should be affected in the same manner by their respective costs. We test our analytical framework using a macro dataset on bilateral trade in services and a specific industry dataset on Air passenger international transportation. We find results consistent with our theoretical framework: policy and non-policy factors affecting the use of inputs in both the exporting and the importing country have a symmetric impact on the bilateral flow of services between those countries.*

Non-Linearities and Unit Roots in G7 Macroeconomic Variables

We carry out a meta-analysis on the frequency of unit-roots in macroeconomic time series with a dataset covering 249 variables for the G7 countries. We use linear tests and the three popular non-linear tests (TAR, ESTAR and Markov Switching). In general, the evidence in favour of the random walk hypothesis is weaker than in previous studies. This evidence against unit roots is stronger for real and nominal asset prices. Our results show that rejection of the null of a unit root in the macro dataset is substantially higher for non-linear than linear models. Finally, the results from a Monte Carlo experiment show that rejection frequencies are very close to the nominal size of the test when the DGP is a linear unit root process. This leads us to reject the hypothesis that overfitting deterministic components explains the higher rejection frequencies of nonlinear tests.

Non-Linearities and Unit Roots in G7 Macroeconomic Variables

We carry out a meta-analysis on the frequency of unit-roots in macroeconomic time series with a dataset covering 249 variables for the G7 countries. We use linear tests and the three popular non-linear tests (TAR, ESTAR and Markov Switching). In general, the evidence in favour of the random walk hypothesis is weaker than in previous studies. This evidence against unit roots is stronger for real and nominal asset prices. Our results show that rejection of the null of a unit root in the macro dataset is substantially higher for non-linear than linear models. Finally, the results from a Monte Carlo experiment show that rejection frequencies are very close to the nominal size of the test when the DGP is a linear unit root process. This leads us to reject the hypothesis that overfitting deterministic components explains the higher rejection frequencies of nonlinear tests.

Structural Vector Autoregressions With Nonnormal Residuals

In structural vector autoregressive (SVAR) modeling, sometimes the identifying restrictions are insufficient for a unique specification of all shocks. In this paper it is pointed out that specific distributional assumptions can help in identifying the structural shocks. In particular, a mixture of normal distributions is considered as a possible model that can be used in this context. Our model setup enables us to test restrictions which are just-identifying in a standard SVAR framework. The results are illustrated using a U.S. macro data set and a system of U.S. and European interest rates.

The big problem of forecasting small change1

The United States Mint recently reviewed approaches to forecasting the demand for new coin. This paper reports on methods used to determine fundamental attributes of the data, and uses these to help better determine appropriate model specification in order to better plan coin production. In particular, the debate regarding trend versus difference stationarity in macroeconomic trending data is considered. The interest in the present paper is limited to applying a well known unit root test procedure to an untested macrodata set – changes in US Coin demand – to see whether the test is useful in guiding the specification to improved forecast performance. It is found that the forecast results are somewhat sensitive to the way in which the data are seasonally adjusted, and lessons learned from this ‘case study’ indicate that unit root tests are useful in guiding model specification.