Computational Economics最新文献

Structural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.

This paper introduces an innovative paradigm in cryptocurrency market analysis and prediction by exploiting the potency of the gradient boosting neural network (GBNN). This pioneering machine learning model amalgamates neural networks and gradient boosting techniques to offer a robust methodology. To enhance the GBNN's predictive capabilities, we enriched its input data with a spectrum of technical indicators. Moreover, we employed the support vector regressor for feature engineering, contributing to the exclusion of insignificant variables. We coined the term "hybrid approach" to describe our pipeline, employing it to train the GBNN model using historical cryptocurrency data. A multitude of experiments were conducted to demonstrate the superior performance of our approach in terms of model accuracy and error on previously unseen data. Notably, our proposed method outperformed state-of-the-art machine learning models, showcasing its effectiveness.

Predictive ability of time series models is easily compromised in the presence of structural breaks, common among financial and economic variables amidst market shocks and policy regime shifts. We address this problem by estimating a semiparametric mixed-frequency model, that incorporate high frequency data either in the conditional mean or the conditional variance equation. The inclusion of high frequency data through non-parametric smoothing functions complements the low frequency data to capture possible non-linear relationships triggered by the structural change. Simulation studies indicate that in the presence of structural change, the varying frequency in the mean model provides improved in-sample fit and superior out-of-sample predictive ability relative to low frequency time series models. These hold across a broad range of simulation settings, such as varying time series lengths, nature of structural break points, and temporal dependencies. We illustrate the relative advantage of the method in predicting stock returns and foreign exchange rates in the case of the Philippines.

This paper describes a new multiplicative, generalized hyperbolic distance function (GHDF) that allows the researcher to measure technical efficiency while holding a subset of inputs or outputs fixed. This is useful when dealing with “bad” or undesirable outputs, or in applications where some inputs or outputs are regarded as quasi-fixed. The paper provides computational methods for both free-disposal hull and data envelopment analysis estimators of the GHDF. In addition, statistical properties of the estimators are derived, enabling researchers to make inference and test hypotheses. An empirical illustration using data on U.S. credit unions is provided, as well as Monte Carlo evidence on the performance of the estimators. As illustrated in the empirical example, estimates of the GHDF are easier to interpret than estimates of additive, directional distance functions that until know have been the only non-parametric estimator of efficiency allowing subsets of input our outputs to be held constant.

This paper presents the steps of the building of PAC (Active available population), PEMP (Population in employment) and TCHO (Unemployment rate) time series along the period 1959–2021 in order to complete those produced by INSEE along the period 1975–2021. Most of the annual macroeconomic INSEE’s data describe the period 1959–2020. So it seems relevant to complete the labor market INSEE’s time series (1975–2020). Our work was based on INSEE’s data which had various degrees of revision. In a first step, we used some rare overseas department data (1954 to 1974) and some data of France metropolitan (1987 and 1994) that we combined with those published in 2020. In a second step, we updated them thanks an other econometric adjustement with the last INSEE’s data published in 2022. During the discussion, we recalled the dilemma that INSEE systematically encounters, namely the dilemma Data quality/quick delivery. Finally, we proposed some assessement’s criteria of our results, based on econometric adjustement and a “confidential interval” that we built.

This paper presents an alternative and straightforward two-step estimation method for the Nelson–Siegel yield curve model. The goal is to generate smoothed time series for the time-varying decay parameter and establish stable yield curve factors. To rectify excessive parameter estimates such as jumps or spikes, the decay parameter is adjusted towards its long-run mean using a closed-form expression. Empirical studies conducted with U.S. Treasury data reveal that this method generates stable and easily interpretable outcomes while the confounding effect, which is characterized by large magnitudes with opposite signs among parameters, is effectively mitigated. In out-of-sample forecasting exercises, the proposed model demonstrates comparable or modest performance compared to other competing models, including the random walk model. In particular, the shifting endpoints technique enhances the overall forecasting ability. Finally, the proposed model demonstrates an effective smoothing effect robustly even when applied to other countries.

Cryptocurrencies, recognized for their transformative impact on both emerging economies and the global financial landscape, are increasingly integral to investment strategies due to their widespread adoption and significant market volatility driven by socio-political news. This study analyzes the price trends of four major cryptocurrencies in decentralized storage—Filecoin, Arweave, Storj, and Siacoin—using a novel approach that combines network analysis, textual analysis, and market analysis. By constructing a network of relevant entities, summarizing pertinent news articles, assessing sentiment with the FinBert model, and evaluating financial market data through transformer encoders, our methodology provides a comprehensive analysis of factors influencing cryptocurrency prices. The integration of these analyses enables us to predict the price trends of the examined cryptocurrencies with accuracies of 76% for Filecoin, 83% for Storj, 61% for Arweave, and 74% for Siacoin, highlighting the model's effectiveness in navigating the complexities of the cryptocurrency market.

A carbon trading price fusion prediction model is proposed to capture the non-linear, non-stationary, multi-frequency, and other irregular characteristics of carbon price data, as well as the temporal periodicity of environmental factors. Firstly, an adaptive Symmetric geometric mode decomposition method is introduced to address the irregularities in carbon trading prices, including nonlinearity, non-stationarity, and multi-frequency. Bubble entropy is employed to extract global features in the frequency and time domains of carbon price data. Secondly, to handle the nonlinearity, temporal periodicity, and noise in environmental influencing factors, a mapping function between the frequency components of carbon price data and environmental influencing factors is established using LightGBM (Light gradient boosting machine) with a regularization term, enabling enhanced fusion of carbon price data features. Thirdly, a Bald Eagle Search-optimized Bi-directional long short-term memory (BiLSTM) model is proposed for predicting carbon prices with different cycle and frequency components. Finally, experimental results demonstrate the superior performance of the proposed fusion prediction model over other models.

This research delves into the dynamics that underlie the relationship between changes in a company's sales and its cost structure. It also explores the influence of short-term debt, often associated with sales, on a phenomenon known as cost stickiness. Furthermore, we examine the roles of product market competition and various business strategies in shaping these interactions. We used financial data from 153 companies listed on the Tehran Stock Exchange from 2015 to 2021 to conduct a rigorous regression analysis to unearth significant insights. Our findings reveal that short-term debt serves as a mechanism for companies to effectively manage their financial obligations with lenders and creditors, and it is inversely correlated with cost stickiness. Moreover, our results shed light on how the impact of short-term debt on cost stickiness varies in response to the competitive nature of the product market and a company's chosen business strategy. Specifically, when companies adopt a "prospector strategy" to explore new markets and expand their product offerings, the negative association between short-term debt and cost stickiness weakens. In a broader context, our study contributes to comprehending cost stickiness and carries practical implications for industry professionals and future scholarly pursuits.