Structural time series: computational efficiency in estimating economic parameters in industry

A growing field is related to automatized Time Series analysis, through complicated due to the dependence of observed and hidden dimensions often presented in these data types. In this report the problem is motivated by a Brazilian financial company interested in unraveling relation structure explanation of the Japanese' CPI ex-fresh Food \& Energy across 157 economical exogenous variables, with very limiting data. The problem becomes more complex when considering that each variable can enter the model with lags of 0 to 8 periods, as well as an additional restriction of admitting only a positive relationship. This report discusses three possible treatments involving models for structured time series, the most relevant approach found in this study is a Dynamic Regression Model combined with a Stepwise algorithm, which allows the most relevant variables, as well as their respective lags, to be found and inserted in the model with low computational cost.

The evolution of happiness pre and peri-COVID-19: A Markov Switching Dynamic Regression Model

Happiness levels often fluctuate from one day to the next, and an exogenous shock such as a pandemic can likely disrupt pre-existing happiness dynamics. This paper fits a Marko Switching Dynamic Regression Model (MSDR) to better understand the dynamic patterns of happiness levels before and during a pandemic. The estimated parameters from the MSDR model include each state’s mean and duration, volatility and transition probabilities. Once these parameters have been estimated, we use the one-step method to predict the unobserved states’ evolution over time. This gives us unique insights into the evolution of happiness. Furthermore, as maximising happiness is a policy priority, we determine the factors that can contribute to the probability of increasing happiness levels. We empirically test these models using New Zealand’s daily happiness data for May 2019 –November 2020. The results show that New Zealand seems to have two regimes, an unhappy and happy regime. In 2019 the happy regime dominated; thus, the probability of being unhappy in the next time period (day) occurred less frequently, whereas the opposite is true for 2020. The higher frequency of time periods with a probability of being unhappy in 2020 mostly correspond to pandemic events. Lastly, we find the factors positively and significantly related to the probability of being happy after lockdown to be jobseeker support payments and international travel. On the other hand, lack of mobility is significantly and negatively related to the probability of being happy.

Second-Order Least Squares Estimation in Nonlinear Time Series Models with ARCH Errors

Many financial and economic time series exhibit nonlinear patterns or relationships. However, most statistical methods for time series analysis are developed for mean-stationary processes that require transformation, such as differencing of the data. In this paper, we study a dynamic regression model with nonlinear, time-varying mean function, and autoregressive conditionally heteroscedastic errors. We propose an estimation approach based on the first two conditional moments of the response variable, which does not require specification of error distribution. Strong consistency and asymptotic normality of the proposed estimator is established under strong-mixing condition, so that the results apply to both stationary and mean-nonstationary processes. Moreover, the proposed approach is shown to be superior to the commonly used quasi-likelihood approach and the efficiency gain is significant when the (conditional) error distribution is asymmetric. We demonstrate through a real data example that the proposed method can identify a more accurate model than the quasi-likelihood method.

Core Elements Towards Circularity: Evidence From the European Countries

In this paper, the authors identified key elements important for circularity: (1) Background: The primary goal of circularity is to eliminate waste and to prove the constant use of resources. In the paper, we classify studies according to circular approaches. The authors identified main elements and classified them into categories important for circularity, starting with the managing and reducing waste and the recovery of resources; and ending with the circularity of material, and general circularity-related topics and presented scientific works dedicated to each of the above-mentioned categories. The authors analyzed several core elements from the first category aiming to investigate and connect different waste streams and provided a regression model; (2) Methods: The authors used a dynamic regression model to identify relationships among variables and selected the ones, which has an impact on the increase of biowaste. The research was delivered for the 27 European Union countries during the period between 2020 and 2019; (3) Conclusions: The authors indicated that the recycling rate of wasted electrical equipment in the previous year has an impact on the increase of recycling biowaste next year. This is explained as non-metallic spare parts of electronic equipment are used as biowaste for fuel production. And the separation process of the composites of electric equipment takes some time, on average the effect is evident in one year period.

An E-Commerce Economic Dynamic Data Evaluation Model Based on Multiuser Demand Constraints

Forecasting the future earnings of listed companies based on multiuser constraints is the focus of investors, securities dealers, creditors, and management. Some empirical studies at home and abroad indicate that the financial reports issued by listed companies regularly contain information about future changes in earnings. On this basis, this article uses the Bayesian dynamic regression model to predict the changes in the future earnings of listed companies and compares the results with traditional analysis models. Through case analysis, it can be seen that the prediction effect of the Bayesian dynamic regression model is generally better than that of the traditional regression model. The Bayesian model can better predict the results, and through the prediction results, it can also establish an evolutionary game model of the industrial innovation replication dynamic system, which can assist enterprises in making profit decisions.

Construção de cenários futuros da temperatura máxima do ar: Capitais do Nordeste Brasileiro

This research aims to analyze and estimate future scenarios of maximum air temperature in the capitals of northeastern Brazil, in order to highlight the importance of climate change today and in the future. For this, rainfall, wind speed, relative humidity and maximum air temperature data were used by the database meteorological activities of the National Institute of Meteorology, of the nine capitals of the northeastern region of Brazil from 1980 to 2019, and the dynamic regression technique that combines the dynamics of time series and the effect of explanatory variables.The main results showed that the dynamic regression model satisfactorily adjusted the association between meteorological variables.Trend (without lag) and seasonality (lag) functions were considered in all capitals, presenting the occurrence of different lags according to the capital and the variable. Thus, the highest temperatures among the capitals analyzed occurred in Teresina/PI and the least high, in Salvador/BA. In general terms, the optimistic scenarios (C1) presented temperature between 32.5 and 35 ºC, the pessimists (C2) between 37.5 ºC and extremes (C3) 35 and 39 ºC, evidencing that all future scenarios present danger to the population. It is expected that the results obtained can help public policies.

Region-wide comprehensive implementation of roguing infected trees, tree replacement, and insecticide applications successfully controls citrus HLB

Huanglongbing (HLB) is a devastating citrus disease worldwide. A three-pronged approach to controlling HLB has been suggested, namely, removal of HLB-symptomatic trees, psyllid control, and replacement with HLB-free trees. However, such a strategy did not lead to successful HLB control in many citrus producing regions. We hypothesize this is because of the small-scale or incomprehensive implementation of the program, conversely, a comprehensive implementation of such a strategy at regional level can successfully control HLB. Here we investigated the effects of region-wide comprehensive implementation of this scheme to control HLB in Gannan region, China, with a total planted citrus acreage of over 110,000 ha from 2013–2019. With the region-wide implementation of comprehensive HLB management, overall HLB incidence in Gannan decreased from 19.71% in 2014 to 3.86% in 2019. A partial implementation of such a program (without a comprehensive inoculum removal) at the regional level in Brazil resulted in HLB incidence increasing from 1.89% in 2010 to 19.02% in 2019. A dynamic regression model analyses predicated that in a region-wide comprehensive implementation of such a program, HLB incidence would be controlled to a level of less than 1%. Economic feasibility analyses showed that average net profits were positive for groves that implemented the comprehensive strategy, but negative for groves without such a program over a ten-year period. Overall, the key for the three-pronged program to successfully control HLB control is the large scale (region-wide) and comprehensiveness in implementation. This study provides valuable information to control HLB and other endemic diseases worldwide.

Identifying the Dynamic Pattern and Influencing Factors of Influenza in Northwest China from 2013 to 2020, Based on Dynamic Regression Model and Wavelet Analysis

Background: Influenza remains a serious global public health problem and a substantial economic burden. The dynamic pattern of influenza differs considerably among geographic and climatological areas, however, the factors underlying these differences are still uncertain. The aim of this paper is to characterize the dynamic pattern of influenza and its potential influencing factors in Northwest China. Methods: Influenza cases in Ningxia China from Nov. 2013 to Jun. 2020 were served as influenza proxy. Firstly, the baseline seasonal ARIMA model of influenza cases and seasonal pattern were analyzed. Then, the dynamic regression model was used to identifying the potential influencing factors of influenza. In addition, the wavelet analysis was further used to explore the coherence between influenza cases and these significant influencing factors.Results: The high risk periods of influenza in Ningxia presented a winter cycle outbreaks pattern and the fastigium came in January. The seasonal ARIMA(0,0,1)(1,1,0)12 was the optimal baseline forecast model. The dynamic regression models and wavelet analysis indicated that PM2.5 and public awareness are significantly positively associated with influenza, as well as minimum temperature is negatively associated. Conclusion: Meteorological (minimum temperature), pollution (PM2.5) and social (public awareness) factors may significantly associated with influenza in Northwest China. Decreasing PM2.5 concentration or increasing the public awareness prior to the fastigium of influenza may be the serviceable methods to reduce the disease risk of influenza, which have an important implication for policy-makers to choose an optimal time for influenza prevention campaign.

Power Flow Prediction for a Steel Plant with a Dynamic Regression Model

A power flow forecast it was shown for an industrial complex consisting of more than 20 different companies. The predominant consumer of electricity in the industrial complex is a steelworks company with an electric arc furnace. A steelworks with an electric arc furnace is a very specific example of an energy consumer. Other companies in the industrial complex are not connected to the steel plant technologically, but they are on the same energy connection. They have a weekly power flow profile significantly different from the steel plant. To calculate the forecast model and perform the forecast of power flows we need only two inputs of data: Historical measurements of power flows and the number of loads of the electric arc furnace in the following days. The first showed a prediction with linear regression. The next model to predict was the seasonal ARIMA model with a regressor, also called a dynamic regression model. The dynamic regression model improved the prediction by 15% compared to linear regression, according to the RMSE measure. This was followed by an improvement in the dynamic regression forecasting model by considering the seasonality 7/5 in the time series. We did this with a model with superimposed noise. With this model, we improved the forecasting by 30% to linear regression. Logically, the filter model of the prediction model also improved, gaining more Lag coefficients and losing a constant. Qualitatively, the result is a forecast of power flow for one month with prediction error MAPE 8% and measure R2 is 0.9.