Predictability is time and frequency dependent. We propose a new forecasting method - forecast combination in the frequency domain - that takes this fact into account. With this method we forecast the equity premium and real GDP growth rate. Combining forecasts in the frequency domain produces...

In this paper we develop a novel semi-nonparametric panel copula model with external covariates for the study of wage rank dynamics. We focus on nonlinear dependence between the current and lagged worker's ranks in the wage residuals distribution, conditionally on individual characteristics. We...

This paper examines the main drawbacks of technical analysis. Although this is widely used by practitioners, from an academic perspective it can only be seen as a form of "voodoo finance". In particular, it runs into the following pitfalls: Subjectivity; Doubtful assumptions; Unjustified...

Over the past two decades, the Black Sea region has exhibited significantly growing wheat production and exports. In 2017/18, Russia ultimately became the world’s largest wheat exporter, a position that was held by the USA for decades. Mostly serving destination markets in the Middle East and...

This paper investigates the impact of the money supply in different states of inflation and economic growth in South Africa from 1990 to 2021. The term "states" defines periods of low and high rates of economic variables of interest. Markov-switching dynamic regression (MSDRM) and time-varying...

In this paper, we add new evidence to a long-debated macroeconomic question, namely whether money growth has predictive power for inflation or, put differently, whether money growth Granger causes inflation. We use a historical dataset - consisting of annual Swedish data on money growth and...

There is ample evidence that Islamic stock markets perform differently from conventional stock markets, particularly when economic policy uncertainty (EPU) or any other uncertainty such as geopolitical uncertainty is present. Considering this context, this paper examines the US EPU's...

We propose a statistical identification procedure for structural vector autoregressive (VAR) models that present a nonlinear dependence (at least) at the contemporaneous level. By applying and adapting results from the literature on causal discovery with continuous additive noise models to...

The low-frequency movements of many economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes, which is guided by a judicious choice of priors and is characterized by sparsity. We present some...

To date, only annual information on economic activity is published for the 16 German states. In this paper, we calculate quarterly regional GDP estimates for the period between 1995 to 2021, thereby improving the regional database for Germany. The new data set will regularly be updated when...

Despite the obvious benefits and growing popularity of Machine Learning (ML) technology, there are still concerns regarding its ability to provide Financial Distress Prediction (FDP). An accurate FDP model is required to avoid financial risk at the lowest possible cost. However, in the Internet...

This paper uses fractional integration methods to measure the degree of persistence in historical annual data on the world population over the period 1800-2016. The analysis is carried out for the original series, and also for its log transformation and its growth rate. The results indicate that...

This paper provides estimates of persistence in historical UK data on life expectancy applying fractional integration methods to both an annual series from 1842 to 2019 and a 5-year average from 1543 to 2019. The results indicate that the former exhibits an upward trend and is persistent but...

This paper analyzes the performance of temporal fusion transformers in forecasting realized volatilities of stocks listed in the S&P 500 in volatile periods by comparing the predictions with those of state-of-the-art machine learning methods as well as GARCH models. The models are trained on...

We provide a systematic approach in obtaining an estimator asymptotically more efficient than the popular fixed effects Poisson (FEP) estimator for panel data models with multiplicative heterogeneity in the conditional mean. In particular, we derive the optimal instrumental variables under...

In Europe differences among countries in the overall change in happiness since the early 1980s have been due chiefly to the generosity of welfare state programs - increasing happiness going with increasing generosity and declining happiness with declining generosity. This is the principal...

In this paper we augment the Bayesian unobserved components model of the EU Commission to estimate the cyclical component of total factor productivity (TFP gap) with a factor structure to include a wide array of business cycle indicators. We demonstrate that this model extension considerably...

The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21-...

Disaggregation of mortality by cause has advanced the development of life tables for life insurance and pension purposes. However, the assumption that the causes of death are independent is a challenge in reality. Furthermore, models that determine relationships among causes of death such as...

In this paper, we test cointegration between GDP and Public consumption of the Republic of North Macedonia, for quarterly data of twenty years' time series (2000Q1-2019Q4). We present results of two methods for cointegration test: first, residual regression test table and second, Engle & Granger...

A recent article by J.D. Hamilton from 2018 attracted a great deal of attention, not only because of its telling title, "Why you should never use the Hodrick- Prescott filter", but also because it offered an alternative approach to detrending, the Hamilton regression filter (HRF). His...

I construct a new dataset of subjective total return expectations at the single stock level using forecast data of sell-side analysts, which I then aggregate at both market and portfolio level. Sell-side analysts' expectations appear to be countercylical, contrarian and less persistent than...

In this study, we consider a simple conditional quantile estimator in a nonparametric framework with time series data. We prove the consistency and asymptotic normality of our simple estimator for absolutely regular processes ( β -mixing). This simple estimator can get better finite sample...

We employ a semi-supervised topic model to extract the rare disaster risks and economic narratives from 7,000,000 NYT articles over 160 years. Our approach addresses the look-ahead bias and changes in semantics. War positively predicts market return in- and out-of-sample, while the economic...

One of the most important risks in the actuarial industry is the longevity risk. The accurate prediction of mortality rates plays a crucial role in the management of the aforementioned risk. Such predictions are performed by modelling the mortality rates using mortality models. Aiming at...

Structural breaks have attracted considerable attention recently, especially in light of the financial crisis, Great Recession, the COVID-19 pandemic, and war. While structural breaks pose significant econometric challenges, machine learning provides an incisive tool for detecting and...

A survey is provided dealing with the formulation of modelling problems for dynamic factor models, and the various algorithm possibilities for solving these modelling problems. Emphasis is placed on understanding requirements for the handling of errors, noting the relevance of the proposed...