Macroeconomic Time Series Research Articles

Forecasting economic growth by combining local linear and standard approaches

Today, developing economies are of major importance for global macroeconomic development. However, the empirical analysis and especially the forecasting of macroeconomic time series remain difficult due to a lack of sufficient data, data frequency, high volatility, and non-linear developments. These difficulties require more sophisticated approaches to obtain reliable forecasts. Therefore, we propose an improved forecasting method especially for growth data based on a data-driven local linear trend estimation with an extended iterative plug-in algorithm for determining the bandwidth endogenously. This approach allows a smooth trend estimation that takes care of temporary changes in trend processes. Further, the naïve random walk model is extended for forecasting by including a local linear, time-varying drift. We apply this method to GDP development for six developing and two advanced economies and compare different forecast combinations. The combinations that include the local linear approach and the random walk with a local linear trend improve forecasting accuracy and reduce variance.

Boosted Whittaker–Henderson Graduation

The Whittaker–Henderson (WH) graduation is a smoothing method for equally spaced one-dimensional data such as time series. It includes the Bohlmann filter, the Hodrick–Prescott (HP) filter, and the Whittaker graduation as special cases. Among them, the HP filter is the most prominent trend-cycle decomposition method for macroeconomic time series such as real gross domestic product. Recently, a modification of the HP filter, the boosted HP (bHP) filter, has been developed, and several studies have been conducted. The basic idea of the modification is to achieve more desirable smoothing by extracting long-term fluctuations remaining in the smoothing residuals. Inspired by the modification, this paper develops the boosted version of the WH graduation, which includes the bHP filter as a special case. Then, we establish its properties that are fundamental for applied work. To investigate the properties, we use a spectral decomposition of the penalty matrix of the WH graduation

Modeling Exchange Rate of Naira to Euro with the APLSTAR-GARCH model

Application of the asymmetric power logistic smooth transition autoregressive (APLSTAR) model proposed by [1] to naira/Euro exchange rate spanning from January, 2006 to April, 2021, which is a nonlinear macroeconomic time series was considered. The APLSTAR model was justifiably fitted to the series and the fit of the APLSTAR model compared with the fits of the competing models revealed that the APLSTAR model fits the data exchange rate of naira to Euro better than the other asymmetric STAR models. Lagrange Multiplier tests for autoregressive conditional heteroscedastic (ARCH) effects were carried out and there was no substantial evidence to reject the presence of ARCH effects in the set of residuals used. Hence, we compared hybrid smooth transition autoregressive-generalized ARCH (STAR-GARCH) models using model evaluation criteria. On balance, the APLSTAR-GARCH (0, 1) model outperforms the other models under consideration.

Estimating Posterior Sensitivities with Application to Structural Analysis of Bayesian Vector Autoregressions

The inherent feature of Bayesian empirical analysis is the dependence of posterior inference on prior parameters, which researchers typically specify. However, quantifying the magnitude of this dependence remains difficult. This article extends Infinitesimal Perturbation Analysis, widely used in classical simulation, to compute asymptotically unbiased and consistent sensitivities of posterior statistics with respect to prior parameters from Markov chain Monte Carlo inference via Gibbs sampling. The method demonstrates the possibility of efficiently computing the complete set of prior sensitivities for a wide range of posterior statistics, alongside the estimation algorithm using Automatic Differentiation. The method’s application is exemplified in Bayesian Vector Autoregression analysis of fiscal policy in U.S. macroeconomic time series data. The analysis assesses the sensitivities of posterior estimates, including the Impulse response functions and Forecast error variance decompositions, to prior parameters under common Minnesota shrinkage priors. The findings illuminate the significant and intricate influence of prior specification on the posterior distribution. This effect is particularly notable in crucial posterior statistics, such as the substantial absolute eigenvalue of the companion matrix, ultimately shaping the structural analysis.

Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty

We develop a nonparametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence, the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroscedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is used to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.

Revisiting the relationship between oil supply news shocks and U.S. economic activity: Role of the zero lower bound

Do oil supply news shocks affect the U.S. economy differently at the zero lower bound? We provide evidence consistent with this hypothesis. Using various U.S. macroeconomic time series data from 1975, we find that the transmission of oil price shocks driven by changes in supply expectations is substantially different at the zero lower bound. These shocks tend to be contractionary when interest rates are higher, and expansionary when monetary policy is operating in the ZLB. Private business investment plays a key role in altering the transmission of oil supply news shocks around the ZLB. The results continue to hold even after controlling for a potentially confounding non-linearity associated with the U.S. shale oil boom. Our empirical investigation should improve our understanding of the role played by policy and structural factors in altering the transmission of oil shocks to the U.S. economy.

Pricing of Climate Risks in the Capital Market of South Africa

ABSTRACT: Climate risk represents an increasingly vital issue to countries, companies, and institutional investors, making it a reality but not a distant threat to humanity. Considering the effects of climate risks on firms' financial indices and financing options, the study investigates whether climate risk is priced by the capital markets of South Africa. The study used reported carbon emissions as a measure of climate risk of 81 listed companies in the Johannesburg Stock Exchange from 2011 to 2020 to examine whether climate risk is considered and priced by the South Africa capital market. Data was sourced from DataStream database- a global financial and macroeconomic time-series database providing data on equities, stock market indices, currencies, company fundamentals, fixed income securities, and key economic indicators. We used the two-step system Generalized Method of Moments estimation technique that copes with endogeneity concerns to corroborate the effects of climate risk on cost of capital and capital structure. We find that climate risk is priced in both cost of debt capital and cost of equity capital. Specifically, we find that an increase in a firm's exposure to climate risk increases the cost associated with issuing debt and equity capital. We also find that climate risk exposure decreases the debt-equity ratio. Additionally, the study showed that firm size, leverage ratio, capitalization, profitability, and turnover affect both cost of capital and capital structure of listed firms. The study concludes that climate risk is priced in cost of financing in the capital market of South Africa. The study recommends that firms should invest in installing eco-friendly machinery that aligns with changing market expectations in order to reduce their carbon emissions. The study therefore highlights the need for companies to proactively assess and manage climate risks, incorporate climate considerations into their strategic decision-making, and enhance their resilience to climate-related challenges.

Macroeconomic and financial mixed frequency factors in a big data environment

Abstract In this paper, we evaluate the predictive content of 3 new business condition indexes and uncertainty measures that are estimated using high-frequency financial and low-frequency macroeconomic time series data. More specifically, our measures are defined as latent factors that are extracted from a state space model that includes multiple different frequencies of non-parametrically estimated components of quadratic variation, as well as mixed frequency macroeconomic variables. When forecasting growth rates of various monthly financial and macroeconomic variables, use of our new mixed frequency factors is shown to result in significant improvement in predictive performance, relative to a number of benchmark models. Additionally, when used to forecast corporate yields, predictive gains associated with the use of our measures are shown to be monotonically increasing, as one moves from predicting higher to lower rated bonds. This is consistent with the existence of a natural pricing channel wherein financial risk (as measured using our volatility factors) contains more predictive information for lower grade bonds. We also find that a variety of extant risk factors including the Aruoba et al. [(2009a). Real-time measurement of business conditions. Journal of Business & Economic Statistics, 27(4), 417427] business conditions index also contain marginal predictive content for the variables that we examine, although their inclusion does not reduce the usefulness of our measures.

Multistep Forecast Averaging with Stochastic and Deterministic Trends

This paper presents a new approach to constructing multistep combination forecasts in a nonstationary framework with stochastic and deterministic trends. Existing forecast combination approaches in the stationary setup typically target the in-sample asymptotic mean squared error (AMSE), relying on its approximate equivalence with the asymptotic forecast risk (AFR). Such equivalence, however, breaks down in a nonstationary setup. This paper develops combination forecasts based on minimizing an accumulated prediction errors (APE) criterion that directly targets the AFR and remains valid whether the time series is stationary or not. We show that the performance of APE-weighted forecasts is close to that of the optimal, infeasible combination forecasts. Simulation experiments are used to demonstrate the finite sample efficacy of the proposed procedure relative to Mallows/Cross-Validation weighting that target the AMSE as well as underscore the importance of accounting for both persistence and lag order uncertainty. An application to forecasting US macroeconomic time series confirms the simulation findings and illustrates the benefits of employing the APE criterion for real as well as nominal variables at both short and long horizons. A practical implication of our analysis is that the degree of persistence can play an important role in the choice of combination weights.

Renewable Energy Mix Enhancement: The Power of Foreign Investment and Green Policies

The objective of this study is to examine and assess the viewpoint of population and economic expansion, as well as foreign investment in the renewable energy sector, by examining the influence of green investment on indexed listed businesses in Indonesia's Sri-Kehati. The dataset utilised in this study include macroeconomic time series data from Indonesia spanning the years 2015 to 2021, as well as issuers indexed under the SRI-KEHATI framework. The data underwent analysis using the multiple regression approach. The study employed an associative research technique and utilised the Eviews 10 programme tool for data analysis. The findings of the study demonstrate a noteworthy positive impact of economic growth on the composition of renewable energy sources. The impact of population growth on the composition of renewable energy sources is shown to be insignificant, but foreign investment is observed to have a notable and adverse influence on the renewable energy mix. The impact of foreign investment on the Renewable Energy Mix is found to be both statistically significant and unfavourable. The variable of economic growth exhibits a noteworthy and favourable impact when moderated by green investment in Sri-Kehati indexed issuers, specifically in relation to the composition of renewable energy sources. The variable of population growth exhibits a noteworthy and favourable impact by means of moderating green investment in the perspective of Sri-Kehati indexed issuer companies regarding the composition of renewable energy sources. The variable of economic growth demonstrates a statistically significant and favourable impact when moderated by green investment in the Sri-Kehati Indexed Issuer Company, specifically in relation to the renewable energy mix. The impact of population growth on the renewable energy mix in the perspective of Sri-Kehati indexed issuer companies is found to be positive and statistically significant when considering the moderation effect of green investment. The variable representing economic growth demonstrates a notable and favourable impact when moderated by green investment in the Sri-Kehati Indexed Issuer Company, particularly in relation to the composition of renewable energy sources. The impact of population growth on the renewable energy mix in the perspective of Sri-Kehati indexed issuer companies is found to be positive and statistically significant when considering the moderation effect of green investment.

A machine learning approach to construct quarterly data on intangible investment for Eurozone

We develop a novel approach to construct quarterly time series data for annually measured intangible investment variables. We accomplish this by using machine learning methods to explore the relationship between these variables and key macroeconomic time series available on a quarterly frequency. The proposed approach offers some advantages over other econometric techniques. Specifically, it does not require any ex-ante assumptions for the link between the quarterly time series and their annual counterpart, while minimizing the need for computationally expensive algorithms and necessitating almost no data pre-processing. To demonstrate the usefulness of the constructed data, we present some business cycles facts for the intangible economies of Eurozone and estimate a dynamic factor model.

Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches

Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches

Government expenditure structure, technological progress and economic growth

PurposeThis study examines the differences in the economic stimulus effects, transmission mechanisms, and output multipliers of government consumption, government traditional investment, and government science and technology investment.Design/methodology/approachThis study constructs and estimates a New Keynesian model of endogenous technological progress embedded in the research and development (R&D) and technology transfer sectors. Using Chinese macroeconomic time series data from 1996 to 2019, this study calibrates and estimates the model and analyzes the impulse response function and a counterfactual simulation of expenditure structure adjustment.FindingsThe results show that compared with the traditional dynamic stochastic general equilibrium (DSGE) model, the endogenous process of technological progress amplifies the impact of government consumption shock and traditional government investment shock on the macroeconomy, leading to greater economic cycle fluctuations. As government investment in science and technology has positive external spillover effects on firm R&D activities and the application of innovation achievements, it can promote more sustainable economic growth than government consumption and traditional investment in the long run.Originality/valueThis study constructs an extended New Keynesian model with different types of government spending, which includes endogenous technological progress within the R&D and technology transfer sectors, thereby linking fiscal policy, business cycle fluctuations and long-term economic growth. This model can study the macroeconomic impact of fiscal expenditure structure adjustment when fiscal expansion is limited. In the Bayesian estimation of model parameters, this study not only uses macroeconomic variables but also adds a sequence of private R&D investment.

Bayesian optimization of hyperparameters from noisy marginal likelihood estimates

SummaryBayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is an iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new function evaluations. We propose a novel Bayesian optimization framework for situations where the user controls the computational effort and therefore the precision of the function evaluations. This is a common situation in econometrics where the marginal likelihood is often computed by Markov chain Monte Carlo or importance sampling methods. The new acquisition strategy gives the optimizer the option to explore the function with cheap noisy evaluations and therefore find the optimum faster. The method is applied to estimating the prior hyperparameters in two popular models on US macroeconomic time series data: the steady‐state Bayesian vector autoregressive (BVAR) and the time‐varying parameter BVAR with stochastic volatility.

Deep Learning in Asset Pricing

We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, keeps a fully flexible form, and accounts for time variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation, and pricing errors and identifies the key factors that drive asset prices. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4695 .

MAKROEKONOMSKI ČIMBENICI NEPRIHODONOSNIH KREDITA: PRIMJER BOSNE I HERCEGOVINE

This paper examines the key macroeconomic factors of non-performing loans (NPLs) in the banking sector of Bosnia and Herzegovina. The research is conducted on quarterly time series data within the period from 2005 to 2019. The autoregressive distributed lag (ARDL) approach is used to model the long-run and short-run relationship between macroeconomic time series. The research results show that increase in real GDP is associated with decrease in NPLs, while rising unemployment and consumer prices are correlated with higher levels of NPLs at 5% significance level.

Modelling and Forecasting of EUR/USD Exchange Rate Using Ensemble Learning Approach

Abstract The aim of the study is to obtain an accurate result from forecasting the EUR/USD exchange rate. To this end, high-performance machine learning models using CART Ensembles and Bagging method have been developed. Key macroeconomic indicators have been also examined including inflation in Europe and the United States, the index of unemployment in Europe and the United States, and more. Official monthly data in the period from December 1998 to December 2021 have been studied. A careful analysis of the macroeconomic time series has shown that their lagged variables are suitable for model’s predictors. CART Ensembles and Bagging predictive models having been built, explaining up to 98.8% of the data with MAPE of 1%. The degree of influence of the considered macroeconomic indicators on the EUR/USD rate has been established. The models have been used for forecasting one-month-ahead. The proposed approach could find a practical application in professional trading, budgeting and currency risk hedging.

Fiscal policy and uncertainty

We investigate the impact of macroeconomic uncertainty on the stimulative effect of government spending. Using historical macroeconomic time series, we show that government spending multipliers are smaller in episodes characterized by high macroeconomic uncertainty. This state dependence is found to be statistically and economically significant and robust to several alternative specifications. To provide structure, we then build a DSGE model with an agency problem and a forecast-error based uncertainty index. We solve the model via a higher-order perturbation method and use the model to evaluate state-dependent government stimulus. In simulations, we find significant volatility in multipliers as well as co-movements that qualitatively match our empirical results. We find that these results are generated by a reallocation away from risky assets following the government spending shock which triggers a financial accelerator mechanism in our model; we then show that this channel is also active in the data. We conclude that uncertainty plays a significant role in determining the effectiveness of fiscal policy and that business cycle models which abstract from uncertainty can produce misleading policy recommendations.

Timely Estimates of the Monthly Mexican Economic Activity

Abstract In this article, we present a new approach based on dynamic factor models (DFMs) to perform accurate nowcasts for the percentage annual variation of the Mexican Global Economic Activity Indicator (IGAE), the commonly used variable as an approximation of monthly GDP. The procedure exploits the contemporaneous relationship of the timely traditional macroeconomic time series and nontraditional variables as Google Trends with respect to the IGAE. We evaluate the performance of the approach in a pseudo real-time framework, which includes the pandemic of COVID-19, and conclude that the procedure obtains accurate estimates, for one and two-steps ahead, above all, given the use of Google Trends. Another contribution for economic nowcasting is that the approach allows to disentangle the key variables in the DFM by estimating the confidence interval for the factor loadings, hence allows to evaluate the statistical significance of the variables in the DFM. This approach is used in official statistics to obtain preliminary and accurate estimates for IGAE up to 40 days before the official data release.

Quantile regression version of Hodrick–Prescott filter

Hodrick–Prescott (HP) filter is a popular trend filtering method for univariate macroeconomic time series such as real gross domestic product. This paper considers the quantile regression version of HP filter (qHP filter), which is a filtering method defined by replacing quadratic loss function of HP filter with quantile regression loss function. One of the essential properties of quantile regression is that if the regression includes intercept, then the ratio of negative residuals can be almost controlled. Does the suggested qHP filter also have the property? This paper answers this question. In addition to the main result, we provide an empirical illustration.