Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives

Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to the selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality (“partially-egalitarian LASSO”). Ex post analysis reveals that the optimal solution has a very simple form: the vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures that are motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require the choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts; indeed, they perform approximately as well as the ex post best forecaster.     

Optimal and robust combination of forecasts via constrained optimization and shrinkage

We introduce various methods that combine forecasts using constrained optimization with penalty. A non-negativity constraint is imposed on the weights, and several penalties are considered, taking the form of a divergence from a reference combination scheme. In contrast with most of the existing approaches, our framework performs forecast selection and combination in one step, allowing for potentially sparse combining schemes. Moreover, by exploiting the analogy between forecasts combination and portfolio optimization, we provide the analytical expression of the optimal penalty strength when penalizing with the L2-divergence from the equally-weighted scheme. An extensive simulation study and two empirical applications allow us to investigate the impact of the divergence function, the reference scheme, and the non-negativity constraint on the predictive performance. Our results suggest that the proposed models outperform those considered in previous studies.     

Forecast combination for U.S. recessions with real-time data

This paper proposes the use of forecast combination to improve predictive accuracy in forecasting the U.S. business cycle index, as published by the Business Cycle Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly forecast utilising the well-established coincident indicators and yield curve models, allowing for dynamics and real-time data revisions. Forecast combinations use log-score and quadratic-score based weights, which change over time. This paper finds that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's own forecasting performance.     

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

Data visualization and forecast combination for probabilistic load forecasting in GEFCom2017 final match

This paper describes the methods used by Team Cassandra, a joint effort between IBM Research Australia and the University of Melbourne, in the GEFCom2017 load forecasting competition. An important first phase in the forecasting effort involved a deep exploration of the underlying dataset. Several data visualisation techniques were applied to help us better understand the nature and size of gaps, outliers, the relationships between different entities in the dataset, and the relevance of custom date ranges. Improved, cleaned data were then used to train multiple probabilistic forecasting models. These included a number of standard and well-known approaches, as well as a neural-network based quantile forecast model that was developed specifically for this dataset. Finally, model selection and forecast combination were used to choose a custom forecasting model for every entity in the dataset.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

经济适用住房挤出效应研究——基于2006-2010年29个大中城市的面板数据分析

国内学者在经济适用房对商品住房的挤出效应问题上一直存有争议。基于2006-2010年29个大中城市面板数据回归,对经济适用房的挤出效应进行实证检验,结果表明：（1）经济适用房销售量的系数显著不为0,说明经济适用房对商品住房的挤出效应存在;（2）挤出效应的值为-0．86,即经济适用房销售量每增加1个单位量,商品住房销售量将会减少0．86个单位量。结果表明,经济适用房政策存在较大的挤出效应。     

Disentangling the Role of Management Control Systems for Product and Process Innovation in Different Contexts

ABSTRACT

This paper studies the relationship between the use of management control systems and different types of innovation (product and process), taking into account innovation contexts (high-tech versus low-tech sectors). We develop and test our hypotheses based on a large sample of Spanish manufacturing firms and find that input controls have a positive association only with process innovation in both innovation contexts. Behavior controls have similar effects on both types of innovation outcomes for high-tech firms, while they have stronger positive associations with process than with product innovation for low-tech firms. Output controls are equally relevant for product and process innovation in both contexts.     

Testing for heteroskedasticity and serial correlation in a random effects panel data model

This paper considers a panel data regression model with heteroskedastic as well as serially correlated disturbances, and derives a joint LM test for homoskedasticity and no first order serial correlation. The restricted model is the standard random individual error component model. It also derives a conditional LM test for homoskedasticity given serial correlation, as well as, a conditional LM test for no first order serial correlation given heteroskedasticity, all in the context of a random effects panel data model. Monte Carlo results show that these tests along with their likelihood ratio alternatives have good size and power under various forms of heteroskedasticity including exponential and quadratic functional forms.     

Distributional implications of informal economy in the EU countries: Accounting for the spread of tax evasion benefits and cultural characteristics

This study provides evidence about the impact of informal economy on income inequality by using annual cross-country panel data from 28 European Union countries observed during the period 2005–2017. Particular attention is dedicated to the cultural setting which is expected to impact taxpayers’ behaviour and thereby income distribution. The study reveals a relationship between the size of the underground economy and income inequality, which is negative when approaching domestic informality and positive when international tax evasion by individuals is related to the top-bottom income disparity. Moreover, a delay of one-year is needed for these effects to occur, sustaining the hypothesis of secondary consequences of the informality. The study also shows that a set of cultural peculiarities are relevant for the nexus of international tax evasion by individuals and income polarisation. The inequality enhancing effect of the offshore activities is larger in countries with high degree of uncertainty avoidance and power distance, and low level of indulgence and long-term orientation respectively.