Arnab Bhattacharjee | Heriot-Watt University (original) (raw)

Uploads

Papers by Arnab Bhattacharjee

Spatial Economic Analysis, 2017

Spatial Economic Analysis, 2020

Spatial Economic Analysis, 2019

Theories of firm profitability make different predictions about the relative importance of firm, ... 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.

Current Trends in Bayesian Methodology with Applications, 2015

China Economic Review, 2014

SSRN Electronic Journal, 2007

Statistics & Probability Letters, 1996

The coefficient of variation of a life distribution is no more than 1 if it belongs to the L-clas... 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.

Journal of Statistical Planning and Inference, 2011

National Institute Economic Review, 2020

We provide a way of representing spatial and temporal equilibria in terms of a Engle-Granger repr... 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 fo... 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 wi... 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.

Econometrics, 2005

This paper considers empirical work relating to models of firm dynamics. We show that a hazard re... 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,...

Over the last two decades central bank independence has become the default for the conduct of mon... 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...

We compare three methods of motivating money in New Keynesian DSGE Models: Money-in-the-utility f... 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

It is important for demographic analyses and policy-making to obtain accurate models of spatial d... 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...