One of the contexts where small area estimation techniques have proved their potential is the analysis of data collected in national labour force surveys to obtain estimates for small geographical domains. Applications of small area estimation methods to data from labour force surveys have...

The extant literature predicts market returns with "simple" models that use only a few parameters. Contrary to conventional wisdom, we theoretically prove that simple models severely understate return predictability compared to "complex" models in which the number of parameters exceeds the...

We introduce artificial intelligence pricing theory (AIPT). In contrast with the APT's foundational assumption of a low dimensional factor structure in returns, the AIPT conjectures that returns are driven by a large number of factors. We first verify this conjecture empirically and show that...

This paper applies Machine learning techniques to Google Trends data to provide real-time estimates of national average subjective well-being among 38 OECD countries since 2010. We make extensive usage of large custom micro databases to enhance the training of models on carefully pre-processed...

Past literature investigating the determinants of tourist expenditure have made a wide use of econometric models to assess conditional relationships of a set of regressors in predicting individual spending. However such techniques provide little or no information about the relationships within...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

Accurate and robust short-term load forecasting plays a significant role in electric power operations. This paper proposes a variant of genetic programming, improved by incorporating semantic awareness in algorithm, to address a short term load forecasting problem. The objective is to...

In order to cope with daily foreign currency exchange payments or trades and avoid liquidity crisis, central banks need to maintain the liquidity of foreign exchange reserves. In this paper, we develop a Foreign Exchange Reserves Liquidity Management (FERLM) model based on stochastic process by...

Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus,...

There have been numerous applications of partial observability bivariate probit models. These models identify determinants of individual discrete outcomes when all that is observed are collective outcomes and individual characteristics. Numerical difficulties with their likelihood functions are...