Empirical researchers often perform model specification tests, such as the Hausman test and the overidentifying restrictions test, to confirm the validity of estimators rather than the validity of models. This paper examines the effectiveness of specification pretests in finding invalid...

The issue of modelling observations generated in matrix form over time is key in economics, finance and many domains of application. While it is common to model vectors of observations through standard vector time series analysis, original matrix-valued data often reflect different types of...

In many applications of discrete choice modeling, there exist unobserved factors (UFs) driving the consumer demand that are not included in the model. Ignoring such UFs when fitting the choice model can produce biased parameter estimates and ultimately lead to incorrect policy decisions. At the...

While applications of big data analytics have brought many new opportunities to economic research, with datasets containing tens of millions of observations, making usual econometric inferences based on extreme estimators would require huge computing powers and memories that are often not...

A Stochastic Arbitrage Opportunity is defined as a zero-cost investment portfolio that enhances every feasible benchmark portfolio for all admissible utility functions. The present study provides a formal theory of consistent estimation of the set of arbitrage opportunities and an Empirical...

This paper deals with LASSO regression in high-dimensional sparse linear models with time series data. We propose heteroskedasticity and autocorrelation consistent (HAC) and heteroskedasticity and autocorrelation robust (HAR) estimates for the penalty loadings and evaluate the in-sample fitting...

We develop a novel approach based on the generalised canonical correlation (GCC) analysis to consistently estimating the multilevel factor model and providing the proper inference theory for the high dimensional panel data. Importantly, our approach is shown to be robust to a non-zero...

The dynamic conditional correlation (DCC) and co-range models are two main frameworks widely used to employ range-based univariate volatility. Using the two approaches, we build novel multivariate range-based EGARCH (REGARCH) models: a DCC-REGARCH and co-range REGARCH (CRREGARCH) model, as well...

In this paper, I identify the long-run risks (LRR) in the frequency domain, and further estimate the macro (consumption) risk premia in different frequency ranges. To achieve the identification, I employ the long-run projections and the associated inference procedure for I(0) process developed...

In this paper, we show that the availability of multiple price series for the same asset can be exploited to estimate its integrated variance. We use a vector error correction model for those prices and its common trend representation to estimate the efficient price of the asset. Because the...