Tax Law (Books and Journals)
3039 results for Tax Law (Books and Journals)
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A Brief History of General‐to‐specific Modelling*
We review key stages in the development of general‐to‐specific modelling (Gets). Selecting a simplified model from a more general specification was initially implemented manually, then through computer programs to its present automated machine learning role to discover a viable empirical model. Throughout, Gets applications faced many criticisms, especially from accusations of ‘data mining’—no...
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Non‐parametric Estimator for Conditional Mode with Parametric Features*
We in this paper propose a new approach for estimating conditional mode non‐parametrically to capture the ‘most likely’ effect built on local linear approximation, in which a parametric pilot modal regression is locally adjusted through a kernel smoothing fit to potentially reduce the bias asymptotically without affecting the variance of the estimator. Specifically, we first estimate a parametric
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Smooth and Abrupt Dynamics in Financial Volatility: The MS‐MEM‐MIDAS*
In this paper, we maintain that the evolution of the realized volatility is characterized by a combination of high‐frequency dynamics and smoother, yet persistent, dynamics evolving at a lower frequency. We suggest a new Multiplicative Error Model which combines the mixed frequency features of a MIDAS at the monthly level with Markovian dynamics at the daily level. When estimated in‐sample on the
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Identifying Politically Connected Firms: A Machine Learning Approach*
This article introduces machine learning techniques to identify politically connected firms. By assembling information from publicly available sources and the Orbis company database, we constructed a novel firm population dataset from Czechia in which various forms of political connections can be determined. The data about firms' connections are unique and comprehensive. They include political...
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Are Economics Conferences Gender‐Neutral? Evidence from Ireland*
We study gender inequality in conference acceptance using data from the Irish Economic Association annual conference from 2016 to 2022, exploiting the introduction of anonymized submission in 2021 to study the effect of blinding. While no gender gap is observed in organizers' acceptance decisions, there is an indication of gender difference favouring the in‐group at the reviewer stage. In...
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Sequencing the COVID‐19 Recession in the USA: What Were the Macroeconomic Drivers?
We apply a structural vectorautoregressive analysis to decompose fluctuations in the growth rate of industrial production and inflation precipitated by the COVID‐19 pandemic in the USA into aggregate demand, aggregate supply, and uncertainty shocks. While all three types of shocks contributed to output and inflation dynamics, the surge in economic uncertainty contributed to the decline in output...
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Partial Identification of Marginal Treatment Effects with Discrete Instruments and Misreported Treatment*
This paper provides partial identification results for the marginal treatment effect (MTE) when the binary treatment variable is potentially misreported and the instrumental variable is discrete. Identification results are derived under smoothness assumptions. Bounds for both the case of misreported treatment and the case of no misreported treatment are derived. The identification results are...
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Foetal Exposure to Air Pollution and Students' Cognitive Performance: Evidence from Agricultural Fires in Brazil*
This paper examines the impact of foetal exposure to air pollution from agricultural fires on Brazilian students' cognitive performance later in life. We rely on comparisons across children who were upwind and downwind of the fires while in utero to address concerns around sorting and temporary income shocks. Our findings show that agricultural fires increase PM2.5$$ {\mathrm{PM}}_{2.5} $$,...
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Projection Estimators for Structural Impulse Responses*
In this paper we provide a general two‐step framework for linear projection estimators of impulse responses in structural vector autoregressions (SVARs). This framework is particularly useful for situations when structural shocks are identified from information outside the VAR (e.g. narrative shocks). We provide asymptotic results for statistical inference and discuss situations when standard...
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Early Years Multi‐grade Classes and Pupil Attainment*
We study the effect of exposure to older, more experienced, classroom peers resulting from the widespread use of multi‐grade classes in Scottish primary schools. For identification, we exploit that a class‐planning algorithm quasi‐randomly assigns groups of pupils to multi‐grade classes. We find that school‐starters benefit from exposure to second‐graders in measures of numeracy and literacy. We...
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Medium‐Run Impacts of Iron‐Fortified School Lunch on Anaemia, Cognition, and Learning Outcomes in India*
Using a phase‐in research design, we provide experimental evidence on the impacts of early versus late initiation of iron fortification in school lunch programmes on children's health and cognitive outcomes in India. We find higher haemoglobin levels and a lower likelihood of anaemia in the early treatment group that experienced 4 years of treatment, compared to the late treatment group that was...
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Job Protection and Mortgage Conditions: Evidence from Italian Administrative Data*
This paper combines administrative data from the Italian social security administration and proprietary data from a major Italian commercial bank to analyse the impact of job protection legislation on mortgage conditions. An exogenous change in the degree of job protection against individual dismissals of workers with open‐ended contracts is identified by exploiting the labour market reform of 201
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Information Equivalence among Transformations of Semi‐parametric Nonlinear Panel Data Models*
This paper considers transformations of nonlinear semi‐parametric mean functions that yield moment conditions for estimation. Such transformations are said to be information equivalent if they yield the same asymptotic efficiency bound. I derive a unified theory of algebraic equivalence for moment conditions created by a given linear transformation. The main equivalence result states that under...
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Physician Connectedness and Referral Choice*
This study examines the effects of social network structure of intermediaries in health care, namely referring physicians, upon the specialty treatment choices of patients in the United States. The social network of a referring physician is identified by the patient‐sharing pattern in Medicare claims data, and the following three measures are employed as key explanatory variables: (1) number of...
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Job Polarization and the Declining Wages of Young Female Workers in the United Kingdom*
We examine whether the decline of routine occupations contributed to rising wage inequality between young and prime‐age non‐college educated women in the UK over 2001‐2019. We estimate age, period, and cohort effects for the likelihood of employment in different occupations and the wages earned therein. For recent generations, cohort effects indicate a higher likelihood of employment in low‐paying
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Furlough and Household Financial Distress during the COVID‐19 Pandemic*
We study how being furloughed affects household financial distress during the COVID‐19 pandemic in the United Kingdom. Furlough increases the probability of late housing and bill payments by 30% and 19%, respectively. At the aggregate level, furlough increases the incidence of financial distress by 3.38 percentage points. To offset furlough‐induced income reductions, individuals significantly...
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Revisiting the Great Ratios Hypothesis*
Kaldor called the constancy of certain ratios stylized facts, Klein and Kosobud called them great ratios. While they often appear in theoretical models, the empirical literature finds little evidence for them, perhaps because the procedures used cannot deal with lack of co‐integration, two‐way causality, and cross‐country error dependence. We propose a new system pooled mean group estimator that...
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The UK's Great Demand and Supply Recession*
We revisit the weak productivity performance of the UK since the Great Recession by means of both a suitable theoretical framework and firm‐level price and quantity data for detailed products, allowing us to measure both demand and its changes over time and distinguish between quantity total factor productivity and revenue total factor productivity. This in turn allows us to measure how changes...
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A Mixed Frequency BVAR for the Euro Area Labour Market*
We introduce a Bayesian mixed frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results
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How Do People Respond When They Know That Robots Will Take Their Jobs?
In recent years, the USA observed a substantial increase in the adoption of robotic technology. The use of industrial robots in the US economy increased rapidly from about 1 robot per 1,000 workers in 2005 to 1.7 robots per 1,000 workers in 2017, a 70% increase. At the same time, there is a concern that the rapid adoption of robots will transform our society in a way that we have never seen...
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Heteroskedasticity‐Robust Standard Errors for Dynamic Panel Data Models with Fixed Effects*
For linear panel data models with fixed effects, cluster‐robust covariance estimation does not use variability over time. The extant heteroskedasticity‐robust methods available under strict exogeneity do not generalize to dynamic models. We propose novel robust covariance estimators under a strong version of serial uncorrelatedness, where serial uncorrelatedness is required to identify dynamic...
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Cross‐sectional Gravity Models, PPML Estimation, and the Bias Correction of the Two‐Way Cluster‐Robust Standard Errors*
In cross‐section gravity models the two‐way cluster‐robust standard errors of the Poisson pseudo maximum likelihood (PPML) estimates tend to be considerably downward biased. However, two‐way clustering can be avoided if intra‐cluster correlation is induced by country‐specific trade shocks with uniform pass through (equi‐correlation) and the gravity model includes exporter and importer country...
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Testing R&D‐Based Endogenous Growth Models*
This study examines US productivity growth through the lens of R&D‐based growth models. A general R&D‐based model, nesting different model varieties, is developed. These varieties are tested using a novel cointegrating relationship and US data for the period 1953–2018. The results provide evidence against the widely used fully endogenous variety and support for other varieties including the semi‐e
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Using Machine Learning to Create an Early Warning System for Welfare Recipients*
Using high‐quality nationwide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that machine learning algorithms can significantly improve predictive accuracy compared to simpler heuristic models or early warning systems...
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Selective Mortality and the Long‐Term Effects of Early‐Life Exposure to Natural Disasters
We analyze the effects of early‐life shocks in the Philippines and find that in utero exposure to severe typhoons is associated with adverse outcomes. We exploit variations in typhoon exposure and sharp increases in short‐term disaster relief efforts in the 1960s. Before the increase in disaster relief efforts, in utero exposure to severe typhoons was associated with higher mortality (a 9%...
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Seemingly Unrelated Regression Estimation for VAR Models with Explosive Roots*
For VAR models with common explosive root, the OLS estimator of the autoregressive coefficient matrix is inconsistent (refer to Nielsen, 2009 and Phillips and Magdalinos, 2013). Although Phillips & Magdalinos (2013) proposed using the future observations as the instrumental variable for removing the endogeneity from VAR models, type I error occurs when testing for a common explosive root from the
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Asymptotic Behavior of Temporal Aggregation in Mixed‐Frequency Datasets
Here, we present an unexplored issue regarding temporal aggregation. When a model contains frequency‐dependent coefficients, such as a distinct long‐ and short‐term coefficient, temporal aggregation leads to inconsistent least squares estimates. Because the sub‐sampled variable's spectrum is equal to its folded original spectrum, the low‐frequency variable may exhibit a mixture of distinct linear