Conceptualizing and measuring “industry resilience”: Composite indicators for postshock industrial policy decision-making

Can resilience be a relevant concept for industrial policy? Resilience is usually described as the ability of a socioeconomic system to recover from unexpected shocks. While this concept has caught the attention of regional economics researchers seeking to understand the different patterns behind regional recovery after a disruption, it is increasingly recognized that resilience can have policy-relevant conceptual applications in many other regards. In this paper, we apply it to industries and define the “industry resilience” concept and measurements. Our contribution is twofold. Theoretically, we frame industry resilience as a useful conceptual framework for policy-making to support the selection of industrial policy targets that are more capable of recovering after unexpected shocks. In addition, industry resilience can mitigate government failures by supporting decision-makers in promoting both economically and socially sustainable structural change. Methodologically, building on post-2008 U.S. data, we develop two composite indicators (CIs) to separately analyze quantitative and qualitative postshock variations in sectoral employment. Such CIs support policy-makers in visualizing sectoral performances dynamically and multidimensionally and can be used to compare each sector both to other sectors and to its counterfactual. Our results highlight that sectors react heterogeneously to shocks. This points to the relevance of tailoring vertical industrial policies according to sector features and the aims of industrial policy initiatives.     

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well-performing deep learning model, extending its capabilities by including exogenous variables and allowing it to integrate multiple sources of useful information. To showcase the utility of the NBEATSx model, we conduct a comprehensive study of its application to electricity price forecasting tasks across a broad range of years and markets. We observe state-of-the-art performance, significantly improving the forecast accuracy by nearly 20% over the original NBEATS model, and by up to 5% over other well-established statistical and machine learning methods specialized for these tasks. Additionally, the proposed neural network has an interpretable configuration that can structurally decompose time series, visualizing the relative impact of trend and seasonal components and revealing the modeled processes’ interactions with exogenous factors. To assist related work, we made the code available in a dedicated repository.     

Distributed ARIMA models for ultra-long time series

Providing forecasts for ultra-long time series plays a vital role in various activities, such as investment decisions, industrial production arrangements, and farm management. This paper develops a novel distributed forecasting framework to tackle the challenges of forecasting ultra-long time series using the industry-standard MapReduce framework. The proposed model combination approach retains the local time dependency. It utilizes a straightforward splitting across samples to facilitate distributed forecasting by combining the local estimators of time series models delivered from worker nodes and minimizing a global loss function. Instead of unrealistically assuming the data generating process (DGP) of an ultra-long time series stays invariant, we only make assumptions on the DGP of subseries spanning shorter time periods. We investigate the performance of the proposed approach with AutoRegressive Integrated Moving Average (ARIMA) models using the real data application as well as numerical simulations. Our approach improves forecasting accuracy and computational efficiency in point forecasts and prediction intervals, especially for longer forecast horizons, compared to directly fitting the whole data with ARIMA models. Moreover, we explore some potential factors that may affect the forecasting performance of our approach.     

Constrained Kalman Filtering: Additional Results

This paper deals with linear state space modelling subject to general linear constraints on the state vector. The discussion concentrates on four topics: the constrained Kalman filtering versus the recursive restricted least squares estimator; a new proof of the constrained Kalman filtering under a conditional expectation framework; linear constraints under a reduced state space modelling; and state vector prediction under linear constraints. The techniques proposed are illustrated in two real problems. The first problem is related to investment analysis under a dynamic factor model, whereas the second is about making constrained predictions within a GDP benchmarking estimation.     

Bayesian semiparametric stochastic volatility modeling

This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models.     

Durable goods produced by state owned enterprises

A two-period durable-goods monopoly model is analyzed where the durable good is provided by a state owned enterprise (SOE). First, we suppose that the SOE is under pressure to provide employment, and therefore has an employment goal, as well as the traditional profit and consumer surplus objectives. Assuming that the SOE has difficulty committing to current buyers with respect to its profit and employment motives, we find that as the employment burden increases, the SOE tends to move further away from the efficient durability and provides a lower durability level than a pure profit maximizer. Additionally, we show that a durable-goods SOE without commitment power, will wish to partially privatize to help mitigate its commitment problem with buyers and increase social welfare. Both of these findings provide economic rationale for the partial privatization of SOEs in transitioning economies that have not been identified in the literature prior to this.     

On tariff and quality innovation in a market with discrete preferences

In a market for a quality-differentiated good with heterogeneous set of consumers and a local firm facing competitive imports from abroad, we examine private and social incentives for quality innovation. For differential tariff regime, we show that both the private and social gains increase with the tariff protection for the low-quality segment of the domestic market for any given tariff on high-quality imports. But for some very high costs of innovation, the local firm may not undertake a socially desirable innovation. The pro-competitive effect, on the other hand, ensures that quality-distortion-at-the-bottom occurs only for very high levels of tariffs.     

Prices vs. Quantities with fiscal cushioning

Enforcement of international environmental quantity regulations (e.g. tradable pollution permits) is often expected to be stricter than that of price regulations (e.g. emission taxes). While both instruments provide opportunities for misrepresenting actual pollution, enforcement of international price-based instruments is additionally hampered by potential fiscal cushioning, i.e. an adjustment of domestic fiscal policies offsetting the tax incentive to reduce emission reductions. The present paper extends the formal debate on Prices vs. Quantities by including the risk of fiscal cushioning. We find the level of the marginal benefit curve and the variance of costs become key elements for instrument choice and can render quantity regulations strictly preferable. Remarkably, in the latter case, the slopes of the marginal curves do not affect optimal instrument choice. Numerical calculations, using data taken from the international climate policy context, support the potential dominance of quantity regulations found in our formal analysis.     

Undesirable competition

We show that the entry of private profit-maximising firms makes the consumers worse off compared to having a nationalised monopoly. Such entry increases the nationalised firm’s profit, industry profit, and social welfare, at the expense of the consumers. Our result is important for competition policy.     

我国支线航空发展:痛并快乐着

我国现在规划从民航大国向民航强国迈进。要实现这个宏伟蓝图,不仅需要从航空制造业方面讲我国能够实现具有自我知识产权的核心机型,而且从航空运输业来说需要实现支线航空的长足可持续性发展。发现支线航空也是实现民航强国的一个重要保障点。我国支线航空目前正在干线航空和高铁的夹缝中艰难前行,痛并快乐着。