This article defines the Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) model for modelling ILM time series with fractional difference value in the interval of 1൏𝑑൏2. The performance of the ARFURIMA model is examined through a Monte Carlo simulation. Also, some...

This paper investigates the volatility of daily returns on the Romanian stock market between January 2020 and April 2021. Volatility is analyzed by means of the representative index for Bucharest Stock Exchange (BSE), namely, the Bucharest Exchange Trading (BET) index, along with twelve...

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...

In this study we investigate possible long-range trends in the cryptocurrency market. We employed the Hurst exponent in a sample covering the period from 1 January 2016 to 26 March 2021. We calculated the Hurst exponent in three non-overlapping consecutive windows and in the whole sample. Using...

This article defines the Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) model for modelling ILM time series with fractional difference value in the interval of 1൏𝑑൏2. The performance of the ARFURIMA model is examined through a Monte Carlo simulation. Also, some...

We propose a novel dynamic mixture vector autoregressive (VAR) model in which timevarying mixture weights are driven by the predictive likelihood score. Intuitively, the state weight of the k-th component VAR model in the subsequent period is increased if the current observation is more likely...

This paper investigates the role of investor attention in forecasting realized volatility for fourteen international stock markets, by means of Google Trends data, over the sample period January 2004 through November 2021. We devise an augmented Empirical Similarity model that combines three...

The main purpose of this study is to explore the determinants of average household consumption spending in counties and cities from the two aspects of government fiscal expenditure and household characteristics. A spatial econometric model, the spatial Durbin model, was used to analyze...

Implicit gender bias may affect hiring and promotion decisions, implying inefficiencies in the outcome of selection processes. We focus on the dynamics of gender bias when selecting candidates for a committee or position, and obtain the long-run female share as well as the conditions for a glass...

In order to allocate resources and formulate policies effectively, governments and enterprises often need accurate geographical information on profit-seeking enterprises. This study explores the impact of local fiscal expenditure and environmental regulation on the number of profit-seeking...

Purpose This study proposes spatial origin-destination threshold Tobit to address spatial interdependence among bilateral trade flows while accounting for zero trade volumes. Design/methodology/approach This model is designed to capture multiple forms of spatial autocorrelation embedded in...

This paper proposes a new spatial lag regression model which addresses global spatial autocorrelation arising from cross-sectional dependence between counts. Our approach offers an intuitive interpretation of the spatial correlation parameter as a measurement of the impact of neighbouring...

World coffee prices may have crucial implications on domestic prices of coffee. However, empirical evidence on the effect of world coffee prices on the price of coffee traded at the Ethiopian Commodity Exchange (ECX) is very scant. The main objective of this study is to analyze the response of...

Mildly explosive autoregressions have been extensively employed in recent theoretical and applied econometric work to model the phenomenon of asset market bubbles. An important issue in this context concerns the construction of confidence intervals for the autoregressive parameter that...

We provide nonparametric quantile regressions to test for autocorrelation patterns for weekly and monthly stock returns. We test in four developed markets (North America, Europe, Japan, and Asia without Japan) and five market-size portfolios. We find greater heteroskedasticity for the...

We extend LeSage and Pace's (2008) spatial autoregressive model for origin-destination flows by accommodating two-way fixed effects. Those fixed effects represent unobserved characteristics of origin and destination units. A partial likelihood approach is used to remove fixed effects in the...

Autoregressive (AR) model and conditional autoregressive (CAR) model are specific regressive models in which independent variables and dependent variable imply the same object. They are powerful statistical tools to predict values based on correlation of time domain and space domain, which are...

We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a GMM estimator...

The steady-state turnover of a trading strategy is of clear interest to practitioners and portfolio managers, as is the steady-state Sharpe ratio. In this article, we show that in a convenient Gaussian process model, the steady-state turnover can be computed explicitly, and obeys a clear...

We propose a new time series model where the threshold is specified as an empirical quantile of recent observations of a threshold variable. The resulting conditional threshold traces the fluctuation of the threshold variable, which can enhance the fit and interpretation of the model. In the...

Movements in expected returns (ER) can cause a bias in measured autocorrelations, and the resulting spurious component is positive for infrequent regime shifts. We demonstrate this point analytically and investigate its empirical prevalence. In a key contribution, we use shifts in ex ante ER...

The purpose of the article is to carry out the analysis of interrelatedness of cities in the territorial space of Russia. The evaluation method is based on the calculation of global and local Moran's indexes for determination of interrelatedness of the adjacent territories by the following...

The literature on heteroskedasticity and autocorrelation robust (HAR) inference is extensive but its usefulness relies on stationarity of the relevant process, say Vt, usually a function of the data and estimated model residuals. Yet, a large body of work shows widespread evidence of various...

In this paper, we examine the herding behaviour of the Chinese renewable energy sector using both static and time-varying coefficient models. Examining daily data from January 05, 2015 to April 29, 2022, we find strong evidence of herding behaviour changing over time in this market. We find that...

This paper examines to what extent the momentum spread ratio (MSR) can predict momentum profits. The momentum spread ratio as a potential proxy of investor underreaction can significantly predict the momentum, industry momentum, and residual momentum, especially after 1994, suggesting that...

This paper introduces a model for intraday copper futures prices based on a stochastic differential equation (SDE). In particular, we derive an SDE that fits the model to the data and that is based on the whitening filter approach, a method characterizing linear time-variant systems. This method...

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...

This paper aims to test the adaptive market hypothesis in the two main Vietnamese stock exchanges, namely Ho Chi Minh City Stock Exchange (HSX) and Hanoi Stock Exchange (HNX), by measuring the relationship between current stock returns and historical stock returns. In particular, the tests...