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The purpose of this paper is to show how dynamic regression models based on equilibrium correction principles can be used to form auditor expectations of account balances as part of the analytic review.

The design and method are empirical, using the automated econometric software of PcGets and annual data of the Toyota Company over the period 1950‐2004 to generate forecasts of sales and earnings.

Automated equilibrium correction models (AECMs) are shown to possess stable parameters and provide reliable one year ahead forecasts of sales based on macro‐economic data. AECMs are then used to generate indicative earnings forecasts conditional upon sales as an expectation generating tool for directing auditors' attention to possible sources of error in financial statements.

Analysis is illustrative of a general method and does not provide exhaustive treatment of the full range of potential application of AECMs.

Until recently, econometric problems have made the use of dynamic regression models in auditing difficult for non‐specialists to implement. Developments in automated software packages such as PcGets now make the use of such procedures by audit practitioners possible.

Relatively little is known about dynamic regression models in the accounting and auditing literature. This paper introduces the basic concepts underpinning AECMs and demonstrates their potential to contribute to the analytic review toolkit of the auditor.

Structural models' inflation forecasts are often inferior to those of naïve devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, helping to interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.

This paper examines empirically the dynamic relationship between spot and futures prices in stock index futures market using data for the KOSPI200 during 1996 to 2001, and employing nonlinear-equilibrium-correction approach that essentially is based on the extension of Markovian regime shifts to nonstationary framework. A linear-VECM was rejected strongly when tested against a Markov-switching (MS) VECM that allowed for two regimes in the mean of equilibrium correction model, as well as in the variance-covariance matrix. The empirical model ultimately proposed therefore, is consistent with the spirit of Cost of Carry model, as well as with the increasingly growing empirical literature stressing the existence of important nonlinearities in both spot and futures prices movements.

The purpose of this paper is to provide further insights into understanding the finance‐growth nexus by verifying the hypothesis that financial development promotes economic growth through its capacity to attract increased international migrant remittances to Ghana.

A dynamic equilibrium‐correction mechanism model for the period 1987(3)‐2007(4) was estimated following the Johansen cointegration procedure. This approach produced maximum likelihood estimators of the unconstrained cointegrating vector, and suggested the number of cointegrating vectors without relying on an arbitrary normalization.

The findings reveal two stylized facts with reference to Ghana. First, although financial development Granger‐causes international migrant remittance inflows, it is in itself directly detrimental to endogenous growth. Second, international migrant remittance inflows are statistically significant in explaining variations in endogenous growth in the short run as well as in the long run.

Since directly, financial development hampers endogenous growth, but Granger‐causes increased inflows of migrant remittances, and these remittances impact positively but marginally on endogenous growth, it follows that the sequencing of implementing Ghana's financial reform programmes should be re‐examined, whilst an enabling environment is created to induce Ghanaians living abroad to remit home through official channels.

International migrant remittances were found to be statistically significant in promoting endogenous growth, albeit marginally. Financial development does not directly engender growth, unless it succeeds in attracting non‐debt foreign capital in the form of remittances through the formal sector. Financial development causes migrant remittance inflows which impact positively on growth.

We investigate whether smooth robust methods for forecasting can help mitigate pronounced and persistent failure across multiple forecast horizons. We demonstrate that naive predictors are interpretable as local estimators of the long-run relationship with the advantage of adapting quickly after a break, but at a cost of additional forecast error variance. Smoothing over naive estimates helps retain these advantages while reducing the costs, especially for longer forecast horizons. We derive the performance of these predictors after a location shift, and confirm the results using simulations. We apply smooth methods to forecasts of UK productivity and US 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.

This study aims to explore the short-run and long-run relationships and causality between economic growth and financialization in the new member states (NMS-11) and to provide some policy implications drawn from the empirical findings.

The autoregressive distributed lag (ARDL) bounds test approach to cointegration with the vector error correction model and the cumulative sum of squares (CUSUMQ) test for stability of functions is used between 1995q1 and 2021q4 to examine the existence of cointegration, relationships and causality between economic growth and financialization.

The findings of the ARDL bounds test demonstrate that the variables included in the models are bound together in the long run, as confirmed by the associated equilibrium correction. The estimated models indicate that the association between selected variables and economic growth is stronger and more statistically significant in the short run compared with the long run. Also, for NMS-11 understudied countries, short-run causality prevails over long-run causality. The changes in the level of financialization have a significant negative effect on the growth rates in the short run, which aligns with findings from previous empirical studies.

This study extends the existing very limited literature about short-run and long-run relationships and causality among economic growth and financialization, including inflation and unemployment variables, to determine their link in the NMS-11. Specifically, the present study reveals that the current level of financialization hampers economic growth and promoting such economic policies further can have adverse effects on the overall economic growth.

This paper investigates the empirical relationship between money, real income, interest rates, inflation and expected exchange rate, and examines the constancy of this relationship, especially in the light of financial reform, deregulation of financial markets and financial crises in Turkey. The estimation results show that expected exchange rate is statistically significant in the money demand function, indicating existence of currency substitution in Turkey. The dynamics of money demand is important, the inflation and income effects are much smaller in the short‐run than long‐run. The results also reveal that the demand for money in Turkey is stable, despite the economic reforms and financial crises.

In recent work, we have developed a theory of economic forecasting for empirical econometric models when there are structural breaks. This research shows that well-specified models may forecast poorly, whereas it is possible to design forecasting devices more immune to the effects of breaks. In this chapter, we summarise key aspects of that theory, describe the models and data, then provide an empirical illustration of some of these developments when the goal is to generate sequences of inflation forecasts over a long historical period, starting with the model of annual inflation in the UK over 1875–1991 in Hendry (2001a).