Arnab Bhattacharjee
Heriot-Watt University, Economics, Faculty Member
- University of Dundee, Economics, Faculty Memberadd
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Theories of firm profitability make different predictions about the relative importance of firm, industry and time specific factors. We assess, empirically, the relevance of these effects over a sixteen year period in India, as a regime... more
Theories of firm profitability make different predictions about the relative importance of firm, industry and time specific factors. We assess, empirically, the relevance of these effects over a sixteen year period in India, as a regime of control and regulation, pre 1985, gave way to partial liberalisation between 1985 and 1991 and to more decisive liberalisation after 1991. We find that firm effects are important throughout, when rent seeking opportunities proliferated, as well as when competitive forces were enhanced by institutional change. In contrast, industry effects significantly increased after liberalisation, suggesting that industry structure matters more within competitive markets. These findings help understand the relevance of different models over different stages of liberalisation, and have important implications for both theory and policy.
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The coefficient of variation of a life distribution is no more than 1 if it belongs to the L-class and no less than 1 if it belongs to the L-class. However, there are nonexponential distributions in each of these classes that have... more
The coefficient of variation of a life distribution is no more than 1 if it belongs to the L-class and no less than 1 if it belongs to the L-class. However, there are nonexponential distributions in each of these classes that have coefficient of variation equal to 1.
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We provide a way of representing spatial and temporal equilibria in terms of a Engle-Granger representation theorem in a panel setting. We use the mean group, common correlated effects estimator plus multiple testing to provide a set of... more
We provide a way of representing spatial and temporal equilibria in terms of a Engle-Granger representation theorem in a panel setting. We use the mean group, common correlated effects estimator plus multiple testing to provide a set of weakly cross corre
This paper investigates the added benefit of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of the mixed frequency augmented Bayesian Structural Time Series model (BSTS)... more
This paper investigates the added benefit of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of the mixed frequency augmented Bayesian Structural Time Series model (BSTS) of Scott and Varian (2014). We show that a large dimensional set of search terms are able to improve nowcasts before other macro data becomes available early on the quarter. Search terms with high inclusion probability have negative correlation with GDP growth, which we reason to stem from them signalling special attention likely due to expected large troughs. We further offer several improvements on the priors: we allow to shrink state variances to zero to avoid overfitting states, extend the SSVS prior to the more flexible normal-inverse-gamma prior of Ishwaran et al. (2005) which stays agnostic about the underlying model size, as well as adapt the horseshoe prior of Carvalho et al. (2010) to the BSTS. The application to nowcasting GDP growth as ...
Until recently, much effort has been devoted to the estimation of panel data regression models without adequate attention being paid to the drivers of diffusion and interaction across cross section and spatial units. We discuss some new... more
Until recently, much effort has been devoted to the estimation of panel data regression models without adequate attention being paid to the drivers of diffusion and interaction across cross section and spatial units. We discuss some new methodologies in this emerging area and demonstrate their use in measurement and inferences on cross section and spatial interactions. Specifically, we highlight the important distinction between spatial dependence driven by unobserved common factors and those based on a spatial weights matrix. We argue that, purely factor driven models of spatial dependence may be somewhat inadequate because of their connection with the exchangeability assumption. Limitations and potential enhancements of the existing methods are discussed, and several directions for new research are highlighted.
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This paper considers empirical work relating to models of firm dynamics. We show that a hazard regression model for firm exits, with a modification to accommodate age-varying covariate effects, provides an empirical framework... more
This paper considers empirical work relating to models of firm dynamics. We show that a hazard regression model for firm exits, with a modification to accommodate age-varying covariate effects, provides an empirical framework accommodating many of the features of interest in studies on firm dynamics. Modelling implications of some of the popular theoretical models are considered and a set of empirical procedures for verifying testable implications of the theoretical models are proposed. The proposed hazard regression models can accommodate negative effects of initial size that go to zero with age (active learning model), negative initial size effects that fall with age but stay permanently negative (passive learning model), conditional and unconditional hazard rates that decrease with age at higher ages, and adverse effects of macroeconomic shocks that decrease with age of the firm. The methods are illustrated using data on quoted UK firms. Consistent with the active learning model,...
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Over the last two decades central bank independence has become the default for the conduct of monetary policy. In turn the decisionmaking process, within a central bank, has become by design much more transparent. The governance of this... more
Over the last two decades central bank independence has become the default for the conduct of monetary policy. In turn the decisionmaking process, within a central bank, has become by design much more transparent. The governance of this process is generally embedded in some type of committee. In turn, the use of committees to make decisions about interest rates, and other aspects of monetary policy, has increased the amount of information again deliberately made available about this decision-making itself. This in turn has generated a large literature on how committees make decisions, how they interact among themselves, and whether or not the outcome reects the consensus, a majority decision, or perhaps the domination of one or more members of the committee in the decision-making process. This paper o¤ers further insight into how decisions are made within a committee and and proposes a method by which we can detect hidden interactions among members of a committee, once weve con...
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We compare three methods of motivating money in New Keynesian DSGE Models: Money-in-the-utility function, shopping time and cash-in-advance constraint, as well as two ways of modelling monetary policy, interest rate feedback rule and... more
We compare three methods of motivating money in New Keynesian DSGE Models: Money-in-the-utility function, shopping time and cash-in-advance constraint, as well as two ways of modelling monetary policy, interest rate feedback rule and money growth rules. We use impulse response analysis, and a set of econometric distance measures based on comparing model and data variance-covariance matrices to compare the different models. We find all models closed by an estimated interest rate feedback rule imply counter-cyclical policy and inflation rates, which is at odds with the data. This problem is robust to the introduction of demand side shocks, but is not a feature of models closed by an estimated money growth rule. Drawing on our econometric analysis, we argue that the cash-in-advance model, closed by a money growth rule, comes closest to the data
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It is important for demographic analyses and policy-making to obtain accurate models of spatial diffusion, so that policy experiments can reflect endogenous spatial spillovers appropriately. Likewise, it is important to obtain accurate... more
It is important for demographic analyses and policy-making to obtain accurate models of spatial diffusion, so that policy experiments can reflect endogenous spatial spillovers appropriately. Likewise, it is important to obtain accurate estimates and forecasts of demographic variables such as age-specific fertility rates, by regions and over time, as well as the uncertainty associated with such estimation. Here, we consider Bayesian hierarchical models with separable spatio-temporal dependence structure that can be estimated by borrowing strength from neighbouring regions and all years. Further, we do not consider the adjacency structure as a given, but rather as an object of inference. For this purpose, we use the local similarity of temporal patterns by developing a spatial clustering model based on Bayesian nonparametric smoothing techniques. The Bayesian inference provides the uncertainty associated with the clustering configurations which is typically lacking in classical analys...
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We develop a method for estimating the functional surface of a regression coefficient that varies over a complex spatial domain with irregular boundaries, peninsulas and interior holes. The method is motivated by, and applied to, data on... more
We develop a method for estimating the functional surface of a regression coefficient that varies over a complex spatial domain with irregular boundaries, peninsulas and interior holes. The method is motivated by, and applied to, data on housing markets, where the central object of inference is estimation of spatially varying effects of living space on house prices. For this purpose, we extend a method of spline smoothing over an irregular domain to the functional regression model. Spatially varying coefficients for a specific regressor are estimated by a combination of three smoothing problems, allowing for additional regressors with spatially fixed coefficients. The estimates adapt well to the irregular and complex spatial domain. Implicit prices for living space vary spatially, being high in the city centre and other desirable locations, and declining towards the periphery along gradients determined by major roads.
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Socioeconomic characteristics, health behaviours, and the utilisation and quality of healthcare are prime examples of socioeconomic, cultural and demographic phenomena that are inherently spatial in nature. Understanding the spatial... more
Socioeconomic characteristics, health behaviours, and the utilisation and quality of healthcare are prime examples of socioeconomic, cultural and demographic phenomena that are inherently spatial in nature. Understanding the spatial structure of these factors is particularly relevant in order to efficiently allocate resources. This paper explores the general equilibrium spatial structure of health outcomes and health behaviours across Scottish health boards using a variant of the spatial Durbin model which allows for an a priori unknown spatial weights matrix. The results suggest that there is substantial spatial dynamics in behaviours across Health Boards and that these spillovers are, as expected, asymmetric. We then demonstrate how the model can be used to estimate the behavioural and health impact of a targeted education policy within each health board taking into account both the direct effect on the particular health board itself and the indirect effect in terms of spillovers....
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Operating leverage describes the extent to which a firm’s operating costs are fixed in the short run. The effect of operating leverage is to amplify the impact on profit of a change in revenues; an effect which is further amplified by... more
Operating leverage describes the extent to which a firm’s operating costs are fixed in the short run. The effect of operating leverage is to amplify the impact on profit of a change in revenues; an effect which is further amplified by financial leverage and by asymmetry in the tax system. In this paper we provide empirical estimates of operating leverage at the firm level, using a long panel of data on UK quoted firms. We report sectoral differences in operating leverage around the business cycle, and show that these can be partly explained in terms of costly labour adjustment and asymmetric price adjustment.
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An evaluation of the impact of an entrepreneur's human capital on her/his entrepre-neurial ability is likely to suffer from a sample selection bias if performed on a sample of new entrepreneurs alone. Our theoretical model of... more
An evaluation of the impact of an entrepreneur's human capital on her/his entrepre-neurial ability is likely to suffer from a sample selection bias if performed on a sample of new entrepreneurs alone. Our theoretical model of entrepreneurial choice allows us to characterize this bias. It is shown to be positive (respectively negative) for individuals who were in a favorable (respectively adverse) situation in the labor market at the time at which they decided to become self-employed. Our empirical application measures the impact of the entrepreneur's education on the newly created firm's survival. It is found to be strong and significant for individuals who were previously employed in the new firm's branch of activity, whereas it is at best weakly significant for individuals who were previously unemployed or employed in a branch different from that of the new firm, so that they are more likely to have been poorly matched. These results suggest a very substantial samp...
Definition of housing submarkets is important at both conceptual and empirical levels. In the housing studies literature, submarkets have been defined according to three different criteria: i) similarity in hedonic housing characters, ii)... more
Definition of housing submarkets is important at both conceptual and empirical levels. In the housing studies literature, submarkets have been defined according to three different criteria: i) similarity in hedonic housing characters, ii) similarity in hedonic prices; iii) substitutability of housing units. We argue that the simultaneous fulfilment of criteria i) and ii) is a sufficient condition for criteria iii) to be fulfilled. Criterion i) is directly observable, while criterion ii) can be checked by a model able to detect and analyse spatial heterogeneity in the shadow prices. Here, we propose a new framework, based on a synthesis of spatial econometrics, functional data analysis (FDA) and geographically weighted regression (GWR). The framework is applied to a hedonic regression model where the dependent variable is logarithm of house prices per square meter and housing features are regressors. Thus, we delineate submarkets by clustering (jointly) on the surfaces of the estimat...
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Agents may consider information and other signals from their peers (especially close peers) when making their spatial site choices. However, the presence of other agents in a spatial location may generate congestion or agglomeration... more
Agents may consider information and other signals from their peers (especially close peers) when making their spatial site choices. However, the presence of other agents in a spatial location may generate congestion or agglomeration effects. Disentangling the potential peer effects with issues of congestion is difficult since it is hard to ascertain whether the observed congestion effects are a result of observing others behavior or the influence of peer effects within the same network encouraging a fisherman to visit a site even in the presence of congestion. The research develops an empirical framework to decompose both motivations in a spatial discrete choice model in an effort to synthesize the congestion/agglomeration literature with the peer effects literature. Using Monte Carlo analysis we investigate the robustness of our proposed estimation routine to the conventional random utility model (RUM) that ignores both peer and congestion/agglomeration effects as well as the spati...
Este artigo realca tres aspetos fundamentais da abordagem quantitativa do espaco na analise do mercado da habitacao: i) heterogeneidade espacial; ii) interacao espacial; e iii) escala espacial. A dificuldade de identificar mercados... more
Este artigo realca tres aspetos fundamentais da abordagem quantitativa do espaco na analise do mercado da habitacao: i) heterogeneidade espacial; ii) interacao espacial; e iii) escala espacial. A dificuldade de identificar mercados habitacionais e compreender as suas interacoes e amplamente referida na literatura, bem como a diversidade de metodos adequados para os analisar. No entanto, nao ha consenso sobre as metodologias a serem aplicadas. De modo a contribuir para a compreensao da estrutura espacial da habitacao, com especial enfoque na dependencia espacial, serao apresentados uma metodologia e os resultados da sua aplicacao empirica. Contrariamente a abordagem tradicional, que considera a definicao a priori de uma matriz de pesos espaciais (W), e apresentada uma abordagem nao parametrica que permite estimar essa mesma matriz (W)
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We provide a way of representing spatial and temporal equilibria in terms of a Engle-Granger representation theorem in a panel setting. We use the mean group, common correlated effects estimator plus multiple testing to provide a set of... more
We provide a way of representing spatial and temporal equilibria in terms of a Engle-Granger representation theorem in a panel setting. We use the mean group, common correlated effects estimator plus multiple testing to provide a set of weakly cross correlated correlations that we treat as spatial weights. We apply this model to the 324 local authorities of England, and show that our approach successfully mops up weak cross section correlations as well as strong cross sectional correlations.
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It is important for demographic analyses and policy-making to obtain accurate models of spatial diffusion, so that policy experiments can reflect endogenous spatial spillovers appropriately. Likewise, it is important to obtain accurate... more
It is important for demographic analyses and policy-making to obtain accurate models of spatial diffusion, so that policy experiments can reflect endogenous spatial spillovers appropriately. Likewise, it is important to obtain accurate estimates and forecasts of demographic variables such as age-specific fertility rates, by regions and over time, as well as the uncertainty associated with such estimation. Here, we consider Bayesian hierarchical models with separable spatio-temporal dependence structure that can be estimated by borrowing strength from neighbouring regions and all years. Further, we do not consider the adjacency structure as a given, but rather as an object of inference. For this purpose, we use the local similarity of temporal patterns by developing a spatial clustering model based on Bayesian nonparametric smoothing techniques. The Bayesian inference provides the uncertainty associated with the clustering configurations that is typically lacking in classical analyse...
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We use microsimulation combined with a model of the COVID-19 impacts on individuals and households to obtain projections of households in destitution in the United Kingdom. The projections are estimated at two levels: aggregate quarterly... more
We use microsimulation combined with a model of the COVID-19 impacts on individuals and households to obtain projections of households in destitution in the United Kingdom. The projections are estimated at two levels: aggregate quarterly for the UK, for all quarters of 2020; and annual for 2020 differentiated by region, sector and household demographics. At the aggregate level, destitution is projected to be about three times higher than the non-COVID counterfactual level in 2020Q2, as well as substantially higher than the non-COVID case for the remainder of the year. This increased destitution is initially largely due to the effect on the self-employed, and as the Furlough scheme is drawn down, also on the unemployed. Impacts upon different regions and sectors vary widely, and so do variations across different household types. The sectors particularly affected are construction and manufacturing, while London and its closely connected regions (South East and the Midlands) are most s...
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There is substantial interest in the current literature – spanning finance, economics, engineering and medical imaging – on the relationship structure between several nodes in a complex system. In this paper, we extend the literature by... more
There is substantial interest in the current literature – spanning finance, economics, engineering and medical imaging – on the relationship structure between several nodes in a complex system. In this paper, we extend the literature by developing model and inference for complex networks in terms of latent factors, by extracting the hidden factors that plays significant role in the configuration of inter node relationships. Together, we extend inferences to applications where the underlying network structure is also latent; that is, the adjacency matrix is unobserved. Here, we consider a Bayesian variant of the matrix factorization technique to develop a structural model of the latent adjacency matrix. There are many potential applications. For illustration, we consider a latent network of firms in the in the US automotive sector, where the central object is to understand the impact of an economic shock on firms (or nodes of the network). An important question centers around the factors that affect the stability and resilience of inter node relationships in a network. In the automotive sector application, we would like to know whether these relationship structures are driven by collaborative or competitive environments? What are the effects of a collaborative or competitive role played by a specific firm on the configuration of the relationship network? Using accounting data on firm sales and costs, we use our proposed object oriented factorization methodology to provide explanation of the estimated network links between 3 major US auto manufacturers and their intermediate suppliers.
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Recent event study literature has highlighted abnormal stock returns, particularly in short event windows. A common explanation is the cross-correlation of stock returns that are often enhanced during periods of sharp market movements.... more
Recent event study literature has highlighted abnormal stock returns, particularly in short event windows. A common explanation is the cross-correlation of stock returns that are often enhanced during periods of sharp market movements. This suggests the misspecification of the underlying factor model, typically the Fama-French model. By drawing upon recent panel data literature with cross-section dependence, we argue that the Fame-French factor model can be enriched by allowing explicitly for network effects between stock returns. We show that recent empirical work is consistent with the above interpretation, and we advance some hypotheses along which new structural models for stock returns may be developed. Applied to data on stock returns for the 30 Dow Jones Industrial Average (DJIA) stocks, our framework provides exciting new insights.
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Sustainable procurement is steering today's supply chains towards responsible business practices. This research aims to examine the trend in the sustainability performance of large enterprises for supplier selection across supply... more
Sustainable procurement is steering today's supply chains towards responsible business practices. This research aims to examine the trend in the sustainability performance of large enterprises for supplier selection across supply chain tiers and geographic locations. Secondary data on 83 global, large enterprises discussing sustainable procurement practices are analysed using hierarchical multiple regression analysis. Dynamic capabilities view and stakeholder theory are utilised to develop the hypotheses. The results show that sustainable procurement performance for large enterprises varies across supply chain tiers and increases in the direction of the end-customer. Due to standardisation of regulations and dynamic capabilities of global, large enterprises, no significant difference is observed across geographic regions.
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Spatial homogeneity is a strong assumption in the hedonic housing price context; if not analyzed conveniently it can be a potential source of specification errors. Spatial heterogeneity occurs when a territorial segmentation exists in the... more
Spatial homogeneity is a strong assumption in the hedonic housing price context; if not analyzed conveniently it can be a potential source of specification errors. Spatial heterogeneity occurs when a territorial segmentation exists in the housing market and, therefore, either the hedonic prices associated with different attributes or the characteristics of the houses are not constant over space. The evidence of recognition of housing submarkets and the argument that caution should be exercised when interpreting the results of standard hedonic models has been identified early in the literature. Despite the argument that housing submarkets should be adopted as a working framework, some ambiguity remains about how to deal with this issue. The early empirical works on submarkets tended to be segmented into two major perspectives: those studies that adopt a supply side determinant focusing on the structural characteristics of dwellings and on neighbourhood characteristics; and those that focus on demand side determinants, such as, on household incomes or other demographic and socioeconomic characteristics. In this case the existence of distinct subgroups of demand is reflected on the territorial segmentation of the hedonic price vector. Thus, the objective of this communication is to a methodology to define housing submarkets applied at an urban area, specifically, the analysis will be applied to the urban area of Aveiro-A lhavo in Portugal. Demand and supply side views will be adopted and results compared to see how demand and supply interact to shape the segmentation landscape of housing markets
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We compare two methods of motivating money in New Keynesian DSGE Models: Money-in-the-utility function and cash-in-advance constraint, as well as two ways of modelling monetary policy: interest rate feedback rule and money growth rules.... more
We compare two methods of motivating money in New Keynesian DSGE Models: Money-in-the-utility function and cash-in-advance constraint, as well as two ways of modelling monetary policy: interest rate feedback rule and money growth rules. As an aid to model selection, we use a new econometric measure of the distance between model and data variance-covariance matrices. The proposed measure is useful in distinguishing between alternative general equilibrium models. We find that the models closed by an estimated interest rate feedback rule imply counter-cyclical policy and inflation rates, which is at odds with the data. This problem is not a feature of models closed by an estimated money growth rule. Drawing on our econometric analysis, we argue that the cash-in-advance model, closed by a money growth rule, comes closest to the data.