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To capture location shifts in the context of model selection, we propose selecting significant step indicators from a saturating set added to the union of all of the candidate variables. The null retention frequency and approximate non-centrality of a selection test are derived using a \"split-half\" analysis, the simplest specialization of a multiple-path block-search algorithm. Monte Carlo simulations, extended to sequential reduction, confirm the accuracy of nominal significance levels under the null and show retentions when location shifts occur, improving the non-null retention frequency compared to the corresponding impulse-indicator saturation (IIS)-based method and the lasso.

The diversification of the economy including its exports is at the core of Saudi Vision 2030. The vision targets to raise non-oil export from 16% to 50% of non-oil GDP by 2030. Achieving this, in addition to other goals, necessitates a better understanding of the non-oil export relationship with its determinants. However, we are not aware of a study that estimates the impacts of the determinants on Saudi non-oil exports covering the recent years of reforms and low oil prices and that conducts simulations for future. The purpose of this study is to develop an econometric modeling framework for Saudi non-oil export that can enhance informing the policymaking process through empirical estimations and simulations. For estimations, we applied cointegration and equilibrium correction methodology to the annual data for the period 1983–2018. Results show that Middle Eastern and North African countries’ GDP, as a measure of foreign income, and Saudi Arabia’s non-oil GDP, as a measure of production capacity, have statistically significant positive effects on Saudi non-oil exports in the long run. The real effective exchange rate (REER), as a measure of competitiveness, also exerts a positive effect in the long run if it depreciates and vice versa. Furthermore, our findings support the Export-led growth concept, which articulates that export can be an engine of economic growth and does not support the Dutch disease concept, which highlights the consequences of the resource sector for the non-resource tradable sector for Saudi Arabia. Macroeconometric model-based simulations conducted up to 2030 reveal out that the Saudi non-oil export is more responsive to the changes in REER than any other determinants. The simulation results also show that non-oil manufacturing makes a three times larger contribution to the future expansion of non-oil exports than agriculture. Moreover, the simulations discover that finance, insurance, and other business services, as well as transport and communication play an important role in improving the Saudi non-oil export performance in the coming decade. The key policy recommendation is that measures should be implemented in a coordinated and balanced way to achieve non-oil exports and other targets of the Vision.

Saudi Arabia's petrochemical sector accounts for a significant portion of non-oil exports and has the potential to contribute significantly to the Kingdom's diversification. In this study, Autometrics—a machine learning method, was first employed to estimate export equations of chemicals and rubber-plastics for 1993-2020. The estimated equations were then integrated into a macroeconometric model called KAPSARC Global Energy Macroeconometric Model (KGEMM) and a scenario analysis was performed for the diversification effects of foreign and domestic price shocks till 2035. The scenario analysis showed that a 10% increase in foreign prices leads to 0.40 percentage point and 0.13 percentage point more diversified exports and economy on average for 2023-2035. Regarding domestic prices, a 19% increase in industrial fossil fuel prices and a 10% increase in ethane price result in less than a 0.1 percentage point contraction in the diversification of exports and economy if the revenues from the price reforms are not recycled back to the economy. The reforms can boost economic diversification by 0.05 percentage point if the revenues are recycled back to the petrochemical sector as an investment. If domestic price reforms are coupled with the investment in the petrochemical sector and 50% of this investment goods are locally produced, then diversification of Saudi export and economy enlarge considerably—by 0.20 percentage point and 0.26 percentage point, respectively.

This study examined the impact of financial development, alongside income, renewable energy consumption and exports, on CO2 emissions in Kazakhstan. To conduct this analysis, Autometrics- a machine learning modeling approach- was applied to data spanning from 1993 to 2020. The findings of estimation revealed a positive and statistically significant effect of financial development and income on CO2 emissions in Kazakhstan. Numerically, a 1% increase in financial development is associated with a 0.17% rise in CO2 emissions. The positive impact of financial development on CO2 emissions can be seen as an advanced financial system can boost funding accessibility for energy and carbon-intensive sectors, such as manufacturing and large-scale infrastructure projects, potentially resulting in increased output and economic growth, often accompanied by elevated emission levels. In addition, exports have a negative influence on CO2, whereas the impacts of renewable energy consumption are insignificant. Our findings suggest that Kazakhstan’s policymakers should prioritize channeling financial resources towards eco-friendly technologies, facilitating energy transitions, and promoting sustainable economic activities.

We investigate forecasting in models that condition on variables for which future values are unknown. We consider the role of the significance level because it guides the binary decisions whether to include or exclude variables. The analysis is extended by allowing for a structural break, either in the first forecast period or just before. Theoretical results are derived for a three-variable static model, but generalized to include dynamics and many more variables in the simulation experiment. The results show that the trade-off for selecting variables in forecasting models in a stationary world, namely that variables should be retained if their noncentralities exceed unity, still applies in settings with structural breaks. This provides support for model selection at looser than conventional settings, albeit with many additional features explaining the forecast performance, and with the caveat that retaining irrelevant variables that are subject to location shifts can worsen forecast performance.

This research studies the impact of macroeconomic announcement surprises on daily U.S. Treasury excess returns during the heart of Alan Greenspan’s tenure as Federal Reserve Chair, addressing the possible limitations of standard static regression (SSR) models, which may suffer from omitted variable bias, parameter instability, and poor mis-specification diagnostics. To complement the SSR framework, an automated general-to-specific (Gets) modeling approach, enhanced with modern indicator saturation methods for robustness, is applied to improve empirical model discovery and mitigate potential biases. By progressively reducing an initially broad set of candidate variables, the Gets methodology steers the model toward congruence, dispenses unstable parameters, and seeks to limit information loss while seeking model congruence and precision. The findings, herein, suggest that U.S. Treasury market responses to macroeconomic news shocks exhibited stability for a core set of announcements that reliably influenced excess returns. In contrast to computationally costless standard static models, the automated Gets-based approach enhances parameter precision and provides a more adaptive structure for identifying relevant predictors. These results demonstrate the potential value of incorporating interpretable automated model selection techniques alongside traditional SSR and Markov switching approaches to improve empirical insights into macroeconomic announcement effects on financial markets.

Economic policy agencies produce forecasts with accompanying narratives, and base policy changes on the resulting anticipated developments in the target variables. Systematic forecast failure, defined as large, persistent deviations of the outturns from the numerical forecasts, can make the associated narrative false, which would in turn question the validity of the entailed policy implementation. We establish when systematic forecast failure entails failure of the accompanying narrative, which we call forediction failure, and when that in turn implies policy invalidity. Most policy regime changes involve location shifts, which can induce forediction failure unless the policy variable is super exogenous in the policy model. We propose a step-indicator saturation test to check in advance for invariance to policy changes. Systematic forecast failure, or a lack of invariance, previously justified by narratives reveals such stories to be economic fiction.

Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem), using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem) while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem), using a viable approach that resolves the computational problem of immense numbers of possible models.

This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market’s reaction to economic news. This study’s findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers.

This paper reviews earlier studies and shows that the money demand (MD) relationship under a fixed exchange rate (ER) regime differs from that under a floating ER regime, mainly due to the limited role of monetary policy in the former regime. It then empirically demonstrates that an open-economy model augmented with country-specific factors is a better framework for characterizing the MD function under a fixed ER regime by applying cointegration and equilibrium correction modeling to the Saudi data as a case study. The main message for monetary authorities is that there are other factors, besides those theoretically predicted, shaping MD under a fixed ER regime. This information is important for providing adequate money supply to support economic growth and maintain the stability of the fixed ER, as well as for checking the stability of the MD to make appropriate policy decisions.