Nonlinear Econometric Models Research Articles

Innovations in economic assessment of drought: an application to the US southwest

ABSTRACT While numerous studies have examined the economic repercussions of drought, there remains a gap in integrated analyses comprehensively assessing its economic effects, especially where there is no drought adaptation policy under debate calling for a standard cost-benefit analysis. This work's first contribution comes from the development of a non-linear econometric model predicting total county income for several counties and years, for which population growth in some regions showed increasing water use despite the presence of drought. Using the arid southwest US state of New Mexico as a case study for the drought years of 2017–2019, this work develops a second innovation to assess drought damages based on comparing changes in per capita water use from 2015 to those later years. Using those two innovations, total economic damages to water users summed over the three drought years amount to $1.983 billion, just under 1% of the state's total income for that period. Overall, the effects of drought in New Mexico were more modest than anticipated partly because water users in this state showed remarkable resilience in handling water shortages due to several unique structural features of the state's income generation processes.

A user-friendly method for estimating discrete choice land-use model in a panel data setting

Land-use modeling stands as a pivotal tool in shaping sustainable development policies. With the rapid advancement of remote-sensing technology and the widespread adoption of satellite imagery-based land cover products, these datasets have emerged as primary sources for understanding land-use dynamics due to their high spatial and temporal resolutions. Yet, it remains challenging to effectively integrate such rich panel data into nonlinear econometric land-use models. This paper introduces a method to seamlessly incorporate land cover panel data into econometric models, enabling comprehensive utilization of temporal information within a single framework.-By capturing dynamic land-use patterns, the method enhances prediction accuracy while mitigating issues such as autocorrelated error terms commonly encountered in panel data analysis.-The method is straightforward to implement and applicable to many nonlinear models, making it particularly suitable for datasets with large sample sizes.

Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models

Accurately modeling and forecasting electricity consumption remains a challenging task due to the large number of the statistical properties that characterize this time series such as seasonality, trend, sudden changes, slow decay of autocorrelation function, among many others. This study contributes to this literature by using and comparing four advanced time series econometrics models, and four machine learning and deep learning models11These models include the autoregressive model with seasonality, autoregressive models with exogenous variables, the autoregressive fractionally integrated moving average model with exogenous variables, the three state autoregressive Markov switching model with exogenous variable, Prophet, EXtreme Gradient Boosting, Long-Short-Term Memory and Support Vector Regression. to analyze and forecast electricity consumption during COVID-19 pre-lockdown, lockdown, releasing-lockdown, and post-lockdown phases. Monthly data on Qatar’s total electricity consumption has been used from January 2010 to December 2021. The empirical findings demonstrate that both econometric and machine learning models are able to capture most of the important statistical features characterizing electricity consumption. In particular, it is found that climate change based factors, e.g temperature, rainfall, mean sea-level pressure and wind speed, are key determinants of electricity consumption. In terms of forecasting, the results indicate that the autoregressive fractionally integrated moving average and the three state autoregressive Markov switching models with exogenous variables outperform all other models. Policy implications and energy-environmental recommendations are proposed and discussed.

Nonlinear relationship between monetary policy and stock returns: Evidence from the U.S.

This paper studies the relationship between monetary policy and stock market return in the U.S. using nonlinear econometric models. It first employs a univariate Markov-switching model on each of the three stock indices and three monetary policy variables, displaying significant regime-switching patterns and common movements. This paper then uses a Markov-switching dynamic bi-factor model to simultaneously extract two latent common factors from stock indices and monetary policy variables to represent monetary policy changes and stock market movements separately. The smoothed probabilities of regimes demonstrate that expansionary monetary policy regimes follow economic recessions, but bear stock markets usually occur before economic recessions. The maximum likelihood estimation results show that expansionary monetary policy such as a decrease in the federal funds rate raises stock returns, but stock returns don't directly influence monetary policy decision.

On uniform inference in nonlinear models with endogeneity

This paper explores the uniformity of inference for parameters of interest in nonlinear econometric models with endogeneity. Here the notion of uniformity arises because the behavior of estimators of parameters of interest is shown to vary with where either they or nuisance parameters lie in the parameter space. As a result, inference becomes nonstandard in a fashion that is loosely analogous to inference complications found in the unit root and weak instruments literature, as well as the models recently studied in Andrews and Cheng (2012), Chen et al. (2014), Han and McCloskey (2019). Our main illustrative example is the standard sample selection model, where the parameter of interest is the intercept term as in Heckman (1990), Andrews and Schafgans (1998) and Lewbel (2007). We show here there is a discontinuity in the limiting distribution for an estimator of this parameter despite it being uniformly consistent. This discontinuity prevents standard inference procedures from being valid, and motivates the development of new methods, for which we establish asymptotic properties. Finite sample properties of the procedure are explored through a simulation study and an empirical illustration using the Mroz (1987) data set as in Newey, Powell, and Walker (1990).

COVID-19 and adaptive behavior of returns: evidence from commodity markets

This study examines the Adaptive Market Hypothesis during the COVID-19 pandemic. The pandemic has impacted global economic activity, trade, and financial market activity. There has been much interest in testing financial market theories and relationships during COVID-19. Therefore, we have investigated the varying return predictability from commodities during COVID-19 concerning the adaptive market hypothesis. By applying linear and non-linear econometric models, we find a strong engagement of adaptive behavior of returns from commodities during the ongoing pandemic. The inconsistent return behavior is facilitated by dividing the sample period into different phases. Our results indicate that AMH best explains the impact of COVID-19 on commodity markets.

Economics of soil erosion: case study of Ukraine

Purpose. The main objective of this paper is (і) to determinate the economic loss due to crop productivity loss caused by soil erosion in Ukraine, and (іі) to present the results of the econometric modeling of soil erosion impact on the efficiency crop production at the regional and district level. 
 Methodology / approach. This study uses the following methods: expert assessments and monographic (for the assessment of economic losses due to crop productivity loss from spreading soil erosion); graphical (for building three-dimensional graphs); econometric modeling (to develop a mathematical model of the dependence of the gross crop production and income from sales per 100 hectares from the share of eroded arable land in its total area and production costs in crop industry per 100 hectares); abstract-and-logical (for generalization of the research results). To solve the assigned tasks, linear and quadratic econometric models (production functions) were developed using a dataset (і) from 168 observations (on the example of Ukrainian regions for 2010–2016) and (ii) from 189 observations (on the example of districts of Kharkiv region for 2010–2016). This study was conducted in order to test the hypothesis that the increase in the area of eroded arable land has a negative effect on the gross output of crop production.
 Results. Our expert assessment of economic losses due to crop productivity loss from spread of soil erosion on agricultural land in Ukraine is 224 mln USD. The obtained results confirm the hypothesis about the negative relationship between gross crop output and the level of land erosion. The obtained data confirm that an increase in the area of eroded arable land by 1 % leads to a decrease in the gross output of crop production by 0.20 % per 100 hectares of agricultural land in total, and in the third group of the studied subjects (the share of eroded arable land in their total area is more than 50 %) – by 0.61 %, respectively.
 Originality / scientific novelty. For the first time, linear and nonlinear (quadratic) econometric models were developed, which made it possible to carry out quantitative assessment of the impact of the soil erosion and the financial support (production costs in crop industry) per hectare on the formation of the financial results (gross crop output and income) of business entities in Ukrainian agriculture. The provision on the economics of soil erosion was further developed in terms of expert assessment of losses from this type of degradation and confirmation of the effect of the economic law of diminishing returns, which should be taken into account when developing measures for sustainable land management.
 Practical value / implications. The main results of the study can be used for the development, substantiation and implementation of soil protection measures for the sustainable use of agricultural land and/or to informed decision-making at different levels of management concerning restoration of eroded land.

Estimation and inference of semiparametric models using data from several sources

This paper studies the estimation and inference of nonlinear econometric models when the economic variables are contained in different data sets. We construct a semiparametric minimum distance (SMD) estimator of the unknown structural parameter of interest when there are some common conditioning variables in different data sets. The SMD estimator is shown to be consistent and has an asymptotic normal distribution. We provide the explicit form of the optimal weight for the SMD estimation. We provide a consistent estimator of the variance–covariance matrix of the SMD estimator, and hence inference procedures of the unknown parameter vector. The finite sample performances of the SMD estimators and the proposed inference procedures are investigated in few alternative Monte Carlo simulation studies.

Technological innovation and structural change for economic development in China as an emerging market

As an emerging and transforming market, China has emphasized the key role of technological innovation and industrial structural change in economic growth. Using a nonlinear econometric model and provincial data from 2000 to 2014, this study investigates the direction of technological innovation and structural change for driving China's economic growth. From a national perspective, an inverted U-shaped relationship exists between technological progress and economic growth, revealing a need to shift the technological progress approach from imitation to innovation. Once the turning point is reached, structural upgrading stimulates China's economic growth. Regional heterogeneity is apparent in regional regression. Both energy-saving and environmental conservation technologies positively contribute to the eastern and central regions’ economies, while only environmental conservation technology accelerates the western region's economic development. On structural adjustment, central areas should utilize industrialization and achieve sustainable development, whereas the eastern and western regions do not benefit from industrial structural change. The study's findings can be applied to design policies and strategies that could promote sustainable economic growth through technological innovation and structural change.

Maximal Correlation Techniques in Constructing Non-Linear Econometric Models

Abstract Constructing non-linear stochastic econometric models is considered, from the point of view of revealing and proper reflecting quantitatively stochastic dependencies between input and output variables involved in the models. The dependencies are investigated by applying a corresponding measure of dependence, the maximal correlation. The approach enables one both to obtain corresponding non-linear transformations describing relationships between the input and output variables and to assure the existence of the model parameters estimates. As well, the approach provides obtaining the model identifiability conditions and quantitative description of the model non-linearity. The presentation is preceded with an analysis of a close approach available in the tutorial literature, with emphasizing certain delusions involved.

An Evolutionary Approach to Passive Learning in Optimal Control Problems

We consider the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds frequently producing very large objective function values (outliers). Furthermore, to apply those established methods, the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following Savin and Blueschke (Comput Econ 48(2):317–338, 2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible parameter realizations and optimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our approach provides more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. This result opens new avenues for application of heuristic optimization methods to learning strategies in optimal control research.

The nonlinear effects of firm size on innovation: an empirical investigation

ABSTRACT In conventional studies, large firms tend to emphasize more on process innovation than product innovation. This paper explores factors that could indicate a distinct pattern of firms’ innovation-size relationship: threshold size that implies a positive effect of firm size on the probability of product innovation success; cannibalization effect that creates incentives for large firms to favor product innovations; and financial constraints that have differential effects given different firm sizes. A hypothesis about a non-monotonic relationship between the proportion of product innovation and firm size is tested with nonlinear and dynamic econometric models. For the large firms, empirical evidence shows product innovations result in an overall larger share of new products in total sales, relative to existing products in which process innovations are rooted.

Economic Growth in India, Is It a Jobless Growth? An Empirical Examination Using Okun’s Law

This study empirically examines the relationship between unemployment and output in India by fitting the Okun’s law. To achieve this objective, an annual time series data for unemployment and output were collected from the World Bank and Reserve Bank of St. Louis. The empirical estimation starts with testing the unit root evidence using Perron (1997) and Elliot et al. (Econometrica 64(4):813–836, 1996) DF-GLS, and the impact models were estimated using linear and nonlinear econometric models. The estimates show that quantitatively, there is no significant difference between the two modelling approaches. The evidence indicates that the relationship between unemployment and output for the Indian economy is consistent with Okun’s law. We find most of the coefficients to be negative, less than unity and statistically significant and this conforms to the theoretical expectation. The study also finds that based on the estimated evidence the 11.75% nominal GDP growth rate as targeted by the Indian government will only result in 0.52% decrease in unemployment which is insignificant for the Indian population. The study found that, to get a 1% decline in unemployment, 25% nominal GDP growth rate is required which is twice the targeted value. Therefore, the study concludes that although Indian output is growing, the growth is jobless because it is not up to the threshold level that ensures a decline in unemployment.

Does market attention affect Bitcoin returns and volatility?

In this paper, we analyze the relative impact of attention measures either on the mean or on the variance of Bitcoin returns by fitting nonlinear econometric models to historical data: Two non-overlapping subsamples are considered from January 1, 2012, to December 31, 2017. Outcomes confirm that market attention has an impact on Bitcoin returns and volatility, when measured by applying several transformations on time series for the trading volume or the SVI Google searches index. Specifically, best candidate models are selected via the so-called Box–Jenkins methodology and by maximizing out-of-sample forecasting performance. Overall, we can conclude that trading volume-related measures affect both the mean and the volatility of the cryptocurrency returns, while Internet searches volume mainly affects the volatility. An interesting side finding is that the inclusion of attention measures in model specification makes forecast estimates more accurate.

Nonlinear Effects of Environmental Regulation on Environmental Pollution

This paper classifies environmental regulation into two types and constructs a theoretical framework to explore the influences of fee-based environmental regulation and invest-based environmental regulation on environmental pollution. It then establishes some dynamic spatial autoregressive nonlinear econometric models to test the theoretical hypothesis based on 30-area panel data from 2004 to 2016. The results illustrate that inverted “U” shape curve relationship exists between fee-based environmental regulation and environmental pollution, while a “U” shape curve relationship between invest-based environmental regulation and environmental pollution exists. In addition, the findings suggest that improving the proportion of secondary industry can directly promote the environmental quality while effectively control of foreign direct investment and fiscal decentralization is also indispensable. Thus, the government should make targeted research about the optimal intensity of fee-based environmental regulation and invest-based environmental regulation and make targeted enterprise policy for the environmental pollution reduce, which contains promoting the energy revolution and strengthening the depth and strength of opening-up step by step.

A Neural Network Method for Nonlinear Time Series Analysis

Abstract This paper is concerned with approximating nonlinear time series by an artificial neural network based on radial basis functions. A new data-driven modelling strategy is suggested for the adaptive framework by combining the statistical techniques of forward selection, cross validation and information criterion. The proposed method is fast and simple to implement while avoiding some typical difficulties such as estimation and computation of nonlinear econometric models. Two applications are provided to illustrate the benefits of using the neural network method in time series analysis. First, the proposed modelling method is applied to a neural network test for neglected nonlinearity in conditional mean of univariate time series. A simulation study is carried out to show how the size of the test is improved in finite samples. Further, the new test is compared with alternative popular tests to demonstrate its superior power performance using a variety of nonlinear time series models. Second, the proposed method is applied to obtain a nonlinear forecasting model for daily S&P 500 returns. Forecast accuracy is compared with that of a linear model and other neural network models used in the literature.

Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis

Because of the irreversibility of financialization in nonferrous metals markets, the security of nonferrous metals resources has shifted from a supply security mode based on “production-supply” towards an economic security mode based on “trade-finance”. Thus, the pricing mechanism of nonferrous metals has become more complex and presents features such as nonlinearity, dynamic characteristics and structure dissimilation. Based on this background, we construct a theoretical framework for analysing the effects of financial factors on fluctuations in nonferrous metals prices and employ the Markov-switching vector autoregression (MS-VAR) model to conduct a nonlinear empirical analysis based on monthly data of international copper futures prices from August 2004 to October 2016. Further, we include supply and demand factors for a comparative analysis. The results show that copper futures price fluctuations present a regime switching dynamic that can be characterized as “steep drop”, “small drop” and “steady rise” periods. During each period, financial factors can fully explain the price volatility while in different mechanism, and the “Chinese factor” has apparently been exaggerated. In addition, oil price is key to monitoring the short-term volatility risks of copper futures prices in the downward cycle. The conclusions along with the established nonlinear econometric models provide a new intellectual framework and analysis tool for explaining the financialization of commodity markets.

Shipbuilding and economic cycles: a non-linear econometric approach

Purpose Economic studies have always underlined the cyclical trends of many industries and their different relations to the macro-economic cycles. Shipping is one of those industries and it has been often characterised by peaks that have influenced both the trade patterns and industry investment structure (e.g. fleet, shipyard activity, freight rates). One of the main issues related with the cycles is the effect on overcapacity and prices for newbuilding and how the understanding of these patterns can help in preventing short-hand strategies. The purpose of this paper is to evaluate different effects of business elements on shipbuilding activity, in relation to different economic-cycle phases. Design/methodology/approach This paper proposes a non-linear econometric model to identify the relations between shipbuilding and economic cycles over the past 30 years. The research focuses on identifying the cycle characteristics and understanding the asymmetrical effect of economic- and business-related variables on its development. Findings The study underlines the presence of an asymmetric effect of several business variables on the shipbuilding productions, depending on the cyclical phases (i.e. market expansion or economic slowdown). Moreover, lagged effects seem to be stronger than contemporaneous variables. Originality/value The paper is a first attempt of using non-linear modelling to shipbuilding cycles, giving indications that could be included in relevant investment policies.

Efficiency and Corruption in Local Counties: Evidence from Romania

The current financial crisis and the lack of resources made imperative the analysis of the efficiency for managing public money, both at the central and at the local level. The general objective of the research is constituted by the estimate of the technical efficiency of all counties in Romania, as well as the analysis of its implications in local development. The originality and novelty of the study are supported by the research of a subject that has in the center the efficiency of the public authorities, current research direction for the public sector, in view of achieving a complex study. In order to reach the objective proposed, we will perform calculations on the basis of the Data Envelopment Analysis mathematical model and of the Tobit non-linear econometric model. At the same time, we reach the conclusion that in Romania, as in other states, there is a causality link between the efficiency of the local authorities and the level of corruption.

An Evolutionary Approach to Passive Learning in Optimal Control Problems

This paper considers the optimal control problem of a small nonlinear econometric model under parameter uncertainty and passive learning (open-loop feedback). Traditionally, this type of problems has been approached by applying linear-quadratic optimization algorithms. However, the literature demonstrated that those methods are very sensitive to the choice of random seeds producing frequently very large objective function values (outliers). Furthermore, to apply those established methods the original nonlinear problem must be linearized first, which runs the risk of solving already a different problem. Following a recent study by Savin and Blueschke (2016) in explicitly addressing parameter uncertainty with a large Monte Carlo experiment of possible realizations of the uncertain parameter and minimizing it with the Differential Evolution algorithm, we extend this approach to the case of passive learning. Our conjecture is that the evolutionary approach can provide more robust results demonstrating greater benefit from learning, while at the same time does not require to modify the original nonlinear problem at hand. Our first results support this conjecture pointing to promising results in applying heuristic optimization methods to passive and active learning in optimal control research.