Pub Date : 2020-05-29DOI: 10.1093/acrefore/9780190625979.013.541
Marco H. D. van Leeuwen
Occupations are a key characteristic for analyzing momentous changes in economy and society. Classical economists rooted their analyses in occupational divisions, emphasizing the division of work and its continuous evolution. Modern economists and economic historians also debate the wealth of nations by looking at the global changes in the labor force, at changing labor force participation rates, at winners and losers in the class structure, and in variations in this across the globe—stressing the importance of human capital for work and of changes therein for economic growth. To study such momentous changes over past centuries, historical occupational data are needed as well as measures and procedures to work with these data systematically and comparatively. The Historical International Standard Classification of Occupations (HISCO) maps occupational titles into a common coding scheme across the globe. HISCO-based measures of economic sector and economic specialization have been derived. To answer a number of interesting questions, the HISCO family has been extended to include HISCO-based measures of social status (HISCAM) and social classes (HISCLASS). Armed with his toolbox, scholars are able to study the development of the economy and society over past centuries.
{"title":"Studying Long-Term Changes in the Economy and Society Using the HISCO Family of Occupational Measures","authors":"Marco H. D. van Leeuwen","doi":"10.1093/acrefore/9780190625979.013.541","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.541","url":null,"abstract":"Occupations are a key characteristic for analyzing momentous changes in economy and society. Classical economists rooted their analyses in occupational divisions, emphasizing the division of work and its continuous evolution. Modern economists and economic historians also debate the wealth of nations by looking at the global changes in the labor force, at changing labor force participation rates, at winners and losers in the class structure, and in variations in this across the globe—stressing the importance of human capital for work and of changes therein for economic growth. To study such momentous changes over past centuries, historical occupational data are needed as well as measures and procedures to work with these data systematically and comparatively. The Historical International Standard Classification of Occupations (HISCO) maps occupational titles into a common coding scheme across the globe. HISCO-based measures of economic sector and economic specialization have been derived. To answer a number of interesting questions, the HISCO family has been extended to include HISCO-based measures of social status (HISCAM) and social classes (HISCLASS). Armed with his toolbox, scholars are able to study the development of the economy and society over past centuries.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125353896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-29DOI: 10.1093/acrefore/9780190625979.013.444
Kamal Saggi, Olena Ivus
Longstanding international frictions over uneven levels of protection granted to intellectual property rights (IPR) in different parts of the world culminated in 1995 in the form of the Agreement on Trade Related Aspects of Intellectual Property Rights (TRIPS)—a multilateral trade agreement that all member countries of the World Trade Organization (WTO) are obligated to follow. This landmark agreement was controversial from the start since it required countries with dramatically different economic and technological capabilities to abide by essentially the same rules and regulations with respect to IPRs, with some temporary leeway granted to developing and least developed countries. As one might expect, developing countries objected to the agreement on philosophical and practical grounds while developed countries, especially the United States, championed it strongly. Over the years, a vast and rich economics literature has emerged that helps understand this international divide. More specifically, several fundamental issues related to the protection of IPRs in the global economy have been addressed: are IPRs trade-related? Do the incentives for patent protection of an open economy differ from those of a closed one and, if so, why? What is the rationale for international coordination over national patent policies? Why do developed and developing countries have such radically different views regarding the protection of IPRs? What is the level of empirical support underlying the major arguments for and against the TRIPS-mandated strengthening of IPRs in the world economy? Can the core obligations of the TRIPS Agreement as well as the flexibilities it contains be justified on the basis of economic logic? We discuss the key conclusions that can be drawn from decades of rigorous theoretical and empirical research and also offer some suggestions for future work.
{"title":"The Protection of Intellectual Property in the Global Economy","authors":"Kamal Saggi, Olena Ivus","doi":"10.1093/acrefore/9780190625979.013.444","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.444","url":null,"abstract":"Longstanding international frictions over uneven levels of protection granted to intellectual property rights (IPR) in different parts of the world culminated in 1995 in the form of the Agreement on Trade Related Aspects of Intellectual Property Rights (TRIPS)—a multilateral trade agreement that all member countries of the World Trade Organization (WTO) are obligated to follow. This landmark agreement was controversial from the start since it required countries with dramatically different economic and technological capabilities to abide by essentially the same rules and regulations with respect to IPRs, with some temporary leeway granted to developing and least developed countries. As one might expect, developing countries objected to the agreement on philosophical and practical grounds while developed countries, especially the United States, championed it strongly.\u0000 Over the years, a vast and rich economics literature has emerged that helps understand this international divide. More specifically, several fundamental issues related to the protection of IPRs in the global economy have been addressed: are IPRs trade-related? Do the incentives for patent protection of an open economy differ from those of a closed one and, if so, why? What is the rationale for international coordination over national patent policies? Why do developed and developing countries have such radically different views regarding the protection of IPRs? What is the level of empirical support underlying the major arguments for and against the TRIPS-mandated strengthening of IPRs in the world economy? Can the core obligations of the TRIPS Agreement as well as the flexibilities it contains be justified on the basis of economic logic? We discuss the key conclusions that can be drawn from decades of rigorous theoretical and empirical research and also offer some suggestions for future work.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130604865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-29DOI: 10.1093/acrefore/9780190625979.013.538
Sheilagh Ogilvie
Guilds ruled many European crafts and trades from the Middle Ages to the Industrial Revolution. Each guild regulated entry to its occupation, requiring any practitioner to become a guild member and then limiting admission to the guild. Guilds intervened in the markets for their members’ products, striving to keep prices high, limit output, suppress competition, and block innovations that might disrupt the status quo. Guilds also acted in input markets, seeking to control access to raw materials, keep wages low, hinder employers from competing for workers, and prevent workers from agitating for better conditions. Guilds treated women particularly severely, usually excluding them from apprenticeship and forbidding any female other than a guild member’s widow from running a workshop. Guilds invested large sums in lobbying governments and political elites to grant, maintain, and extend these privileges. Guilds had the potential to compensate for their cartelistic activities by creating countervailing benefits. Guild quality certification was one possible solution to information asymmetries between producers and consumers, which could have made markets work better. Guild apprenticeship had the potential to solve imperfections in markets for skilled training, and thus to encourage human capital investment. The cartel profits generated by guilds could in theory have encouraged technological innovation by enabling guild masters to appropriate more of the social benefits of their innovations, while guild journeymanship and spatial clustering could diffuse new technical knowledge. A rich scholarship on European guilds makes it possible to assess the degree to which guilds created such benefits, outweighing the harm they caused. After about 1500, guild strength diverged across Europe, declining gradually in Flanders, the Netherlands, and England, surviving in France and Italy, and intensifying across large tracts of Iberia, Scandinavia, and the German-speaking lands. The activities of guilds contributed to variations across Europe in economic performance, urban growth, and inequality. Guilds interacted significantly with both markets and states, which helps explain why European economies diverged in the crucial centuries before industrialization.
{"title":"Guilds and the Economy","authors":"Sheilagh Ogilvie","doi":"10.1093/acrefore/9780190625979.013.538","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.538","url":null,"abstract":"Guilds ruled many European crafts and trades from the Middle Ages to the Industrial Revolution. Each guild regulated entry to its occupation, requiring any practitioner to become a guild member and then limiting admission to the guild. Guilds intervened in the markets for their members’ products, striving to keep prices high, limit output, suppress competition, and block innovations that might disrupt the status quo. Guilds also acted in input markets, seeking to control access to raw materials, keep wages low, hinder employers from competing for workers, and prevent workers from agitating for better conditions. Guilds treated women particularly severely, usually excluding them from apprenticeship and forbidding any female other than a guild member’s widow from running a workshop. Guilds invested large sums in lobbying governments and political elites to grant, maintain, and extend these privileges.\u0000 Guilds had the potential to compensate for their cartelistic activities by creating countervailing benefits. Guild quality certification was one possible solution to information asymmetries between producers and consumers, which could have made markets work better. Guild apprenticeship had the potential to solve imperfections in markets for skilled training, and thus to encourage human capital investment. The cartel profits generated by guilds could in theory have encouraged technological innovation by enabling guild masters to appropriate more of the social benefits of their innovations, while guild journeymanship and spatial clustering could diffuse new technical knowledge. A rich scholarship on European guilds makes it possible to assess the degree to which guilds created such benefits, outweighing the harm they caused.\u0000 After about 1500, guild strength diverged across Europe, declining gradually in Flanders, the Netherlands, and England, surviving in France and Italy, and intensifying across large tracts of Iberia, Scandinavia, and the German-speaking lands. The activities of guilds contributed to variations across Europe in economic performance, urban growth, and inequality. Guilds interacted significantly with both markets and states, which helps explain why European economies diverged in the crucial centuries before industrialization.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116678798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-30DOI: 10.1093/acrefore/9780190625979.013.403
P. Levine
Dynamic stochastic general equilibrium (DSGE) modeling can be structured around six key criticisms leveled at the approach. The first is fundamental and common to macroeconomics and microeconomics alike—namely, problems with rationality and expected utility maximization (EUM). The second is that DSGE models examine fluctuations about an exogenous balanced growth path and there is no role for endogenous growth. The third consists of a number of concerns associated with estimation. The fourth is another fundamental problem with any micro-founded macro-model—that of heterogeneity and aggregation. The fifth and sixth concern focus on the rudimentary nature of earlier models that lacked unemployment and a banking sector. A widely used and referenced example of DSGE modeling is the Smets-Wouters (SW) medium-sized NK model. The model features rational expectations and, in an environment of uncertainty, EUM by households and firms. Preferences are consistent with a nonstochastic exogenous balanced growth path about which the model is solved. The model can be estimated by a Bayesian systems estimation method that involves four types of representative agents (households, final goods producers, trade unions, and intermediate good producers). The latter two produce differentiated labor and goods, respectively, and, in each period of time, consist of a proportion locked into existing contracts and the rest that can reoptimize. There is underemployment but no unemployment. Finally, an arbitrage condition imposed on the return on capital and bonds rules out financial frictions. Thus the model, which has become the gold standard for DSGE macro-modeling, features all six areas of concern. The model can be used as a platform to examine how the current generation of DSGE models has developed in these six dimensions. This modeling framework has also used for macro-economic policy design.
{"title":"The State of DSGE Modeling","authors":"P. Levine","doi":"10.1093/acrefore/9780190625979.013.403","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.403","url":null,"abstract":"Dynamic stochastic general equilibrium (DSGE) modeling can be structured around six key criticisms leveled at the approach. The first is fundamental and common to macroeconomics and microeconomics alike—namely, problems with rationality and expected utility maximization (EUM). The second is that DSGE models examine fluctuations about an exogenous balanced growth path and there is no role for endogenous growth. The third consists of a number of concerns associated with estimation. The fourth is another fundamental problem with any micro-founded macro-model—that of heterogeneity and aggregation. The fifth and sixth concern focus on the rudimentary nature of earlier models that lacked unemployment and a banking sector.\u0000 A widely used and referenced example of DSGE modeling is the Smets-Wouters (SW) medium-sized NK model. The model features rational expectations and, in an environment of uncertainty, EUM by households and firms. Preferences are consistent with a nonstochastic exogenous balanced growth path about which the model is solved. The model can be estimated by a Bayesian systems estimation method that involves four types of representative agents (households, final goods producers, trade unions, and intermediate good producers). The latter two produce differentiated labor and goods, respectively, and, in each period of time, consist of a proportion locked into existing contracts and the rest that can reoptimize. There is underemployment but no unemployment. Finally, an arbitrage condition imposed on the return on capital and bonds rules out financial frictions. Thus the model, which has become the gold standard for DSGE macro-modeling, features all six areas of concern. The model can be used as a platform to examine how the current generation of DSGE models has developed in these six dimensions. This modeling framework has also used for macro-economic policy design.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133944112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-03-31DOI: 10.1093/acrefore/9780190625979.013.354
María Soledad Martínez Pería, M. Shin
The link between financial inclusion and human development is examined here. Using cross-country data, the behavior of variables that try to capture these concepts is examined and preliminary evidence of a positive association is offered. However, because establishing a causal relationship with macro-data is difficult, a thorough review of the literature on the impact of financial inclusion, focusing on micro-studies that can better address identification is conducted. The literature generally distinguishes between different dimensions of financial inclusion: access to credit, access to bank branches, and access to saving instruments (i.e., accounts). Despite promising results from a first wave of studies, the impact of expanding access to credit seems limited at best, with little evidence of transformative effects on human development outcomes. While there is more promising evidence on the impact of expanding access to bank branches and formal saving instruments, studies show that some interventions such as one-time account opening subsidies are unlikely to have a sizable impact on social and economic outcomes. Instead well-designed interventions catering to individuals’ specific needs in different contexts seem to be required to realize the full potential of formal financial services to enrich human lives.
{"title":"Financial Inclusion and Human Development","authors":"María Soledad Martínez Pería, M. Shin","doi":"10.1093/acrefore/9780190625979.013.354","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.354","url":null,"abstract":"The link between financial inclusion and human development is examined here. Using cross-country data, the behavior of variables that try to capture these concepts is examined and preliminary evidence of a positive association is offered. However, because establishing a causal relationship with macro-data is difficult, a thorough review of the literature on the impact of financial inclusion, focusing on micro-studies that can better address identification is conducted. The literature generally distinguishes between different dimensions of financial inclusion: access to credit, access to bank branches, and access to saving instruments (i.e., accounts). Despite promising results from a first wave of studies, the impact of expanding access to credit seems limited at best, with little evidence of transformative effects on human development outcomes. While there is more promising evidence on the impact of expanding access to bank branches and formal saving instruments, studies show that some interventions such as one-time account opening subsidies are unlikely to have a sizable impact on social and economic outcomes. Instead well-designed interventions catering to individuals’ specific needs in different contexts seem to be required to realize the full potential of formal financial services to enrich human lives.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121839038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-10DOI: 10.1093/acrefore/9780190625979.013.174
Yong Song, T. Wo'zniak
Markov switching models are a family of models that introduces time variation in the parameters in the form of their state, or regime-specific values. This time variation is governed by a latent discrete-valued stochastic process with limited memory. More specifically, the current value of the state indicator is determined by the value of the state indicator from the previous period only implying the Markov property. A transition matrix characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period conditionally on the state in the current period. This setup decides on the two main advantages of the Markov switching models: the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities. The most commonly applied models from this family are those that presume a finite number of regimes and the exogeneity of the Markov process, which is defined as its independence from the model’s unpredictable innovations. In many such applications, the desired properties of the Markov switching model have been obtained either by imposing appropriate restrictions on transition probabilities or by introducing the time dependence of these probabilities determined by explanatory variables or functions of the state indicator. One of the extensions of this basic specification includes infinite hidden Markov models that provide great flexibility and excellent forecasting performance by allowing the number of states to go to infinity. Another extension, the endogenous Markov switching model, explicitly relates the state indicator to the model’s innovations, making it more interpretable and offering promising avenues for development.
{"title":"Markov Switching","authors":"Yong Song, T. Wo'zniak","doi":"10.1093/acrefore/9780190625979.013.174","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.174","url":null,"abstract":"Markov switching models are a family of models that introduces time variation in the parameters in the form of their state, or regime-specific values. This time variation is governed by a latent discrete-valued stochastic process with limited memory. More specifically, the current value of the state indicator is determined by the value of the state indicator from the previous period only implying the Markov property. A transition matrix characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period conditionally on the state in the current period. This setup decides on the two main advantages of the Markov switching models: the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities.\u0000 The most commonly applied models from this family are those that presume a finite number of regimes and the exogeneity of the Markov process, which is defined as its independence from the model’s unpredictable innovations. In many such applications, the desired properties of the Markov switching model have been obtained either by imposing appropriate restrictions on transition probabilities or by introducing the time dependence of these probabilities determined by explanatory variables or functions of the state indicator. One of the extensions of this basic specification includes infinite hidden Markov models that provide great flexibility and excellent forecasting performance by allowing the number of states to go to infinity. Another extension, the endogenous Markov switching model, explicitly relates the state indicator to the model’s innovations, making it more interpretable and offering promising avenues for development.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126623426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-30DOI: 10.1093/ACREFORE/9780190625979.013.296
P. Chang, Wen-Tai Hsu
This article reviews interrelated power-law phenomena in geography and trade. Given the empirical evidence on the gravity equation in trade flows across countries and regions, its theoretical underpinnings are reviewed. The gravity equation amounts to saying that trade flows follow a power law in distance (or geographic barriers). It is concluded that in the environment with firm heterogeneity, the power law in firm size is the key condition for the gravity equation to arise. A distribution is said to follow a power law if its tail probability follows a power function in the distribution’s right tail. The second part of this article reviews the literature that provides the microfoundation for the power law in firm size and reviews how this power law (in firm size) may be related to the power laws in other distributions (in incomes, firm productivity and city size).
{"title":"Geography, Trade, and Power-Law Phenomena","authors":"P. Chang, Wen-Tai Hsu","doi":"10.1093/ACREFORE/9780190625979.013.296","DOIUrl":"https://doi.org/10.1093/ACREFORE/9780190625979.013.296","url":null,"abstract":"This article reviews interrelated power-law phenomena in geography and trade. Given the empirical evidence on the gravity equation in trade flows across countries and regions, its theoretical underpinnings are reviewed. The gravity equation amounts to saying that trade flows follow a power law in distance (or geographic barriers). It is concluded that in the environment with firm heterogeneity, the power law in firm size is the key condition for the gravity equation to arise. A distribution is said to follow a power law if its tail probability follows a power function in the distribution’s right tail. The second part of this article reviews the literature that provides the microfoundation for the power law in firm size and reviews how this power law (in firm size) may be related to the power laws in other distributions (in incomes, firm productivity and city size).","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124944580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-23DOI: 10.1093/ACREFORE/9780190625979.013.495
B. Nielsen
Outcomes of interest often depend on the age, period, or cohort of the individual observed, where cohort and age add up to period. An example is consumption: consumption patterns change over the lifecycle (age) but are also affected by the availability of products at different times (period) and by birth-cohort-specific habits and preferences (cohort). Age-period-cohort (APC) models are additive models where the predictor is a sum of three time effects, which are functions of age, period, and cohort, respectively. Variations of these models are available for data aggregated over age, period, and cohort, and for data drawn from repeated cross-sections, where the time effects can be combined with individual covariates. The age, period, and cohort time effects are intertwined. Inclusion of an indicator variable for each level of age, period, and cohort results in perfect collinearity, which is referred to as “the age-period-cohort identification problem.” Estimation can be done by dropping some indicator variables. However, dropping indicators has adverse consequences such as the time effects are not individually interpretable and inference becomes complicated. These consequences are avoided by instead decomposing the time effects into linear and non-linear components and noting that the identification problem relates to the linear components, whereas the non-linear components are identifiable. Thus, confusion is avoided by keeping the identifiable non-linear components of the time effects and the unidentifiable linear components apart. A variety of hypotheses of practical interest can be expressed in terms of the non-linear components.
{"title":"Age-Period-Cohort Models","authors":"B. Nielsen","doi":"10.1093/ACREFORE/9780190625979.013.495","DOIUrl":"https://doi.org/10.1093/ACREFORE/9780190625979.013.495","url":null,"abstract":"Outcomes of interest often depend on the age, period, or cohort of the individual observed, where cohort and age add up to period. An example is consumption: consumption patterns change over the lifecycle (age) but are also affected by the availability of products at different times (period) and by birth-cohort-specific habits and preferences (cohort). Age-period-cohort (APC) models are additive models where the predictor is a sum of three time effects, which are functions of age, period, and cohort, respectively. Variations of these models are available for data aggregated over age, period, and cohort, and for data drawn from repeated cross-sections, where the time effects can be combined with individual covariates.\u0000 The age, period, and cohort time effects are intertwined. Inclusion of an indicator variable for each level of age, period, and cohort results in perfect collinearity, which is referred to as “the age-period-cohort identification problem.” Estimation can be done by dropping some indicator variables. However, dropping indicators has adverse consequences such as the time effects are not individually interpretable and inference becomes complicated. These consequences are avoided by instead decomposing the time effects into linear and non-linear components and noting that the identification problem relates to the linear components, whereas the non-linear components are identifiable. Thus, confusion is avoided by keeping the identifiable non-linear components of the time effects and the unidentifiable linear components apart. A variety of hypotheses of practical interest can be expressed in terms of the non-linear components.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116920273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-22DOI: 10.1093/acrefore/9780190625979.013.494
J. Gonzalo, Jean-Yves Pitarakis
Predictive regressions are a widely used econometric environment for assessing the predictability of economic and financial variables using past values of one or more predictors. The nature of the applications considered by practitioners often involve the use of predictors that have highly persistent, smoothly varying dynamics as opposed to the much noisier nature of the variable being predicted. This imbalance tends to affect the accuracy of the estimates of the model parameters and the validity of inferences about them when one uses standard methods that do not explicitly recognize this and related complications. A growing literature aimed at introducing novel techniques specifically designed to produce accurate inferences in such environments ensued. The frequent use of these predictive regressions in applied work has also led practitioners to question the validity of viewing predictability within a linear setting that ignores the possibility that predictability may occasionally be switched off. This in turn has generated a new stream of research aiming at introducing regime-specific behavior within predictive regressions in order to explicitly capture phenomena such as episodic predictability.
{"title":"Predictive Regressions","authors":"J. Gonzalo, Jean-Yves Pitarakis","doi":"10.1093/acrefore/9780190625979.013.494","DOIUrl":"https://doi.org/10.1093/acrefore/9780190625979.013.494","url":null,"abstract":"Predictive regressions are a widely used econometric environment for assessing the predictability of economic and financial variables using past values of one or more predictors. The nature of the applications considered by practitioners often involve the use of predictors that have highly persistent, smoothly varying dynamics as opposed to the much noisier nature of the variable being predicted. This imbalance tends to affect the accuracy of the estimates of the model parameters and the validity of inferences about them when one uses standard methods that do not explicitly recognize this and related complications. A growing literature aimed at introducing novel techniques specifically designed to produce accurate inferences in such environments ensued. The frequent use of these predictive regressions in applied work has also led practitioners to question the validity of viewing predictability within a linear setting that ignores the possibility that predictability may occasionally be switched off. This in turn has generated a new stream of research aiming at introducing regime-specific behavior within predictive regressions in order to explicitly capture phenomena such as episodic predictability.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134522643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-28DOI: 10.1093/ACREFORE/9780190625979.013.344
Keith N. Hylton
Criminal law consists of substantive and procedural parts. Substantive law is the set of rules defining conduct that violates the law. Procedural criminal law is the set of rules regulating the process of punishment. Substantive rules apply mostly to individual actors, and procedural rules apply to public enforcement agencies and adjudicators. Economic theory of criminal law consists of normative and positive parts. Normative economic theory, which began with writings by Beccaria and Bentham, aims to recommend an ideal criminal punishment scheme. Positive economic theory, which appeared later in writings by Holmes and Posner, aims to justify and to better understand the criminal law rules that exist. Since the purpose of criminal law is to deter socially undesirable conduct, economic theory, which emphasizes incentives, would appear to be an important perspective from which to examine criminal law. Positive economic theory, applied to substantive criminal law, seeks to explain and to justify criminal law doctrine in economic terms—that is, in terms that emphasize the incentive effects created by the law. The positive economic theory of criminal law literature can be divided into three phases: Classical deterrence theory, neoclassical deterrence, and modern synthesis. The modern synthesis provides a rationale for fundamental criminal law doctrines and also more puzzling portions of the law such as the doctrines of intent and necessity. Positive economic theory also provides a rationale for the allocation of enforcement responsibilities.
{"title":"Economic Theory of Criminal Law","authors":"Keith N. Hylton","doi":"10.1093/ACREFORE/9780190625979.013.344","DOIUrl":"https://doi.org/10.1093/ACREFORE/9780190625979.013.344","url":null,"abstract":"Criminal law consists of substantive and procedural parts. Substantive law is the set of rules defining conduct that violates the law. Procedural criminal law is the set of rules regulating the process of punishment. Substantive rules apply mostly to individual actors, and procedural rules apply to public enforcement agencies and adjudicators.\u0000 Economic theory of criminal law consists of normative and positive parts. Normative economic theory, which began with writings by Beccaria and Bentham, aims to recommend an ideal criminal punishment scheme. Positive economic theory, which appeared later in writings by Holmes and Posner, aims to justify and to better understand the criminal law rules that exist. Since the purpose of criminal law is to deter socially undesirable conduct, economic theory, which emphasizes incentives, would appear to be an important perspective from which to examine criminal law.\u0000 Positive economic theory, applied to substantive criminal law, seeks to explain and to justify criminal law doctrine in economic terms—that is, in terms that emphasize the incentive effects created by the law. The positive economic theory of criminal law literature can be divided into three phases: Classical deterrence theory, neoclassical deterrence, and modern synthesis. The modern synthesis provides a rationale for fundamental criminal law doctrines and also more puzzling portions of the law such as the doctrines of intent and necessity. Positive economic theory also provides a rationale for the allocation of enforcement responsibilities.","PeriodicalId":211658,"journal":{"name":"Oxford Research Encyclopedia of Economics and Finance","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124868578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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