Oxford Bulletin of Economics and Statistics最新文献

We investigate the effect of international opium price shocks on the per capita dispensation of prescription opioids in the USA. Using quarterly county-level data for 2002q4–2016q4, three main results emerge. First, reductions in opium prices significantly increase the quantity of opioids prescribed, and more so in counties with a larger pre-existing market for pain relief, as captured by the incidence of mining sites. Second, the increase involves only natural and semi-synthetic, but not fully-synthetic, opioids, suggesting that the effect is moderated by the amount of raw material contained in the products. The impact is larger prior to 2010, when overdose deaths were more related to the use of legally prescribed opioids. Third, advertising expenses, stock prices and the profits of opioid producers increase following negative opium price shocks, suggesting an important role of supply-side economic incentives.

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 illustrated by identifying the marginal treatment effects of food stamps on health.

We use a DSGE model with financial frictions and with macroprudential limits on both banks and mortgage borrowers, in the form of capital requirements and maximum debt-service ratios. We then examine: (i) the impact of different combinations of macroprudential limits on key macroeconomic aggregates; (ii) their interaction with each other and with monetary policy; and (iii) their effects on the volatility of key macroeconomic variables and on welfare. We find that capital requirements on banks are the optimal tool when faced with a financial shock, as they nullify the effects of financial frictions and reduce the effects of the shock on the real economy. Instead, limits on mortgage debt-service ratios are optimal following a housing demand shock, as they disconnect the housing market from the real economy, reducing the volatility of inflation. Hence, no policy on its own is sufficient to deal with a wide range of shocks.

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 donations by the firm, having members of managerial boards who donated to a political party, and having members of boards who ran for political office. The results indicate that over 85% of firms with political connections can be accurately identified by the proposed algorithms. The model obtains this high accuracy by using only firm-level financial and industry indicators that are widely available in most countries. These findings suggest that machine learning algorithms could be used by public institutions to improve the identification of politically connected firms with potentially large conflicts of interest.

Applied economists often transform a dependent variable that is non-negative and skewed with the natural log transformation, the inverse hyperbolic sine transformation, or power function. We show that these transformations separate the zeros from the positives such that the estimated parameters are related to those from a scaled linear probability model. The retransformed marginal effects and elasticities are sensitive to changes in a shape parameter, ranging in magnitude between those of an untransformed least squares regression and those of a scaled linear probability model. Instead of transforming the dependent variable with non-negative outcomes that includes zeros, we recommend using a non-transformed dependent variable, such as a two-part model, untransformed linear regression, or Poisson.

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 longer pejorative—with other criticisms based on misunderstandings of the methodology, all now rebutted. A prior theoretical formulation can be retained unaltered while searching over more variables than the available sample size from non-stationary data to select congruent, encompassing relations with invariant parameters on valid conditioning variables.

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 $��{�\text{PM}�}_{�2�.�5�}�$, resulting in significant negative effects on pupils' scores in Portuguese and Maths in the $��5�\text{th}�$ grade through prenatal exposure. Back-of-the-envelope calculations suggest that a 1% reduction in $��{�\text{PM}�}_{�2�.�5�}�$ from agricultural burning has the potential to increase later life wages by 2.6%.

I model a stochastic non-cooperative game between an independent central bank and a treasury and study optimal time-consistent policy in the context of demand-driven recessions and an occasionally binding zero lower bound constraint. Departing from coordination leads to contractionary fiscal policy in the liquidity trap. The persistent decline in short-term government debt improves price stability and welfare albeit at the expense of a deeper recession in the near term. Underlying this policy is the anticipated risk of monetary tightening during the economic recovery in response to fiscally induced inflation. The issuance of long-term government debt helps to buffer the yield to maturity against interest rate fluctuations, thereby reducing the relevance of central bank independence for macroeconomic outcomes.

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 modal regression utilizing prior information from initial studies or economic analysis, and then estimate the non-parametric modal function based on the additive correction by eliminating the parametric feature. We derive the asymptotic normal distribution of the proposed modal estimator for both fixed and estimated parametric feature cases, and demonstrate that there is substantial room for bias reduction under certain regularity conditions. We numerically estimate the suggested modal regression model with the use of a modified modal-expectation-maximization (MEM) algorithm. Monte Carlo simulations and one empirical analysis are presented to illustrate the finite sample performance of the developed modal estimator. Several extensions, including multiplicative correction, generalized guidance, modal-based robust regression and the incorporation of categorical covariates, are also discussed for the sake of completeness.

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 realized kernel volatility of the S&P500 index, this model dominates other simpler specifications, especially when monthly aggregated realized volatility is used. The same pattern is confirmed in the out-of-sample forecasting performance which suggests that adding an abrupt change in the average level of volatility better helps in tracking quick bursts of volatility and a relatively rapid absorption of the shocks.