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This paper evaluates the macroeconomic effects of monetary policy for a central bank digital currency rule with traditional monetary policy in an open environment. We build a dynamic stochastic general equilibrium small open model economy with interest-bearing central bank digital currency assets. The redistribution of asset holdings and the change in asset price are the two pathways through which a retail central bank digital currency rule under the traditional monetary policy affects the macroeconomy. Fiat's digital and traditional monetary policies complement one another in lowering macroeconomic fluctuations. As a result, the real exchange rate, current accounts, and consumption are more stable in transmitting foreign shocks. These results occur when price-based or quantitative-based fiat digital monetary policy parallels traditional price-based monetary policy.

In this paper, we develop a structural model to estimate the current level of the natural rate for a small open economy, featuring a rich set of shocks to provide economic intuition for its underlying drivers. The model follows the New Keynesian tradition with several frictions and is able to draw implications for a monetary policy stance. In contrast to other DSGE models in the literature, this framework includes two main blocks—one related to the foreign sector and one associated with the local economy, linked by the uncovered interest rate parity condition. With this structure, the natural rate is affected by local and external factors, disaggregated in permanent and transitory shocks. Using Bayesian techniques, the model estimates the natural interest rate for two example cases, Mexico and Canada, considering data from these economies and the United States. Results show that the US economy is relevant to explaining natural rates in both countries. For the Mexican case, the drivers are shocks to the US risk premium and the marginal efficiency of investment, as well as country risk premium variations. For Canada, shocks to the households’ discount factor play an important role.

Forecasting is an attractive topic in every field of study because no one knows the exact nature of the underlying phenomena, but it can be guessed using mathematical functions. As the world progresses towards technology and betterment, algorithms are updated to understand the nature of ongoing phenomena. Machine learning (ML) algorithms are an updated phenomenon used in every task aspect. Real exchange rate data is assumed to be one of the significant components of the business market, which plays a pivotal role in learning market trends. In this work, machine learning models, i.e., the Multi-layer perceptron model (MLP), Extreme learning machine (ELM) model and classical time series models are used, Autoregressive integrated moving average (ARIMA) and Exponential Smoothing (ES) model to model and predict the real exchange rate data set (REER). The data under consideration is from January 2019 to June 2022 and comprises 864 observations. This study split the data set into training and testing and applied all stated models. This study selects a model that meets the Key Performance Indicators (KPI) criteria. This model was selected as the best candidate model to predict the behaviour of the real exchange rate data set.

The article analyzes the interaction between monetary and fiscal policy in Mexico. We calibrated a semi-structural model for a small open economy, based on Aguilar and Ramírez-Bulos (2018), for Mexico by using quarterly data from 2001Q1 to 2019Q4. The fiscal policy block models the fiscal deficit depending on output, an endogenous sovereign risk premium, a state-owned oil company, and public debt dynamics with domestic and foreign components. We assumed a fiscal rule whereby the deficit has an upper bound. The monetary policy follows a Taylor rule. We study the effects of different shocks on the economy, such as a drop in commodity prices, an expansion of public spending, an increase in the risk premium, a hike in the interest rate, and depreciation of the real exchange rate. We show that, remarkably, the risk premium channel transmits threats from the fiscal block to the monetary block, calling for the central bank to stabilize inflation. By contrast, starting at the economy’s steady state, an exogenous monetary policy shock affects the fiscal block mainly through the interest rate’s influence on the debt service, prompting a fiscal response to stabilize deficit.

Debido a la incertidumbre al respecto, en este artículo se analiza cuál podría ser la postura de política monetaria más adecuada para México: una que mantenga el objetivo de inflación sin afectar negativamente el crecimiento del producto. Se estima el valor de la tasa de interés neutral en México para los próximos cinco años con base en la regla de Taylor y un modelo vectorial autorregresivo (VAR) cointegrado (CVAR). Los resultados sugieren que la tasa de política monetaria se encuentra por arriba de la tasa neutral de interés, lo que, combinado con el potencial recorte de tasas a escala internacional, podría conceder al banco central durante casi todo el sexenio grados de libertad para mantener su tasa por debajo del nivel actual; en el corto plazo se podría recortar la tasa de política monetaria actual en 25 puntos al menos sin arriesgar la convergencia a la meta de inflación.

This paper presents the first-ever comparison of the forecasting power of two types of narratives: articles in a major daily newspaper and regular research reports released by professional forecasters. The applied testing methodology developed in 22 and extended in this paper includes two natural language processing (NLP) techniques – the sentiment analysis and the wordscores model – that are used to convert the text corpora into the NLP indices. These indices are explanatory variables in linear regression, Granger causality test, vector autoregressive model and random forest model. The paper extends this methodology by applying Latent Dirichlet Allocation (LDA) to the newspaper corpus to filter out articles that discuss topics not relevant for economic and financial analysis. The forecasting test is conducted for two major banks in Poland – BZ WBK and mbank and for major daily newspaper Rzeczpospolita, in Polish. It is shown that mbank narratives have the best forecasting power, while BZ WBK and Rzeczpospolita trade second and third place depending on the model applied. In the vast majority of analyzed cases adding an NLP index to the model improves the forecast accuracy. The answer to the title question is – it depends. Before paying for economic research asset managers are advised to apply methods such as presented in this paper to evaluate whether sell-side research offers any forecasting value in comparison with a newspaper.

This paper presents three measures of the output gap estimated by a dynamic stochastic general equilibrium model of the Czech economy. We argue that the most plausible description of the business cycle provides the output gap defined as a deviation from a flexible price level of output, which is generated solely by permanent growth shocks. Our model shows that 2006-2008 overheating of the economy and the following 2008-2009 slump can be largely attributed to development in a world economy and export and import sectors, while the 2012-2013 recession was caused mainly by a combination of adverse domestic demand and cost shocks.

Objective To investigate the relationship of E47, lysyl oxidase like2 protein(LOXL2) expressions with clinicopathologic parameters of esophageal squamous cell carcinoma(ESCC) patients. Methods The mRNA and protein levels of E47 and LOXL2 in ESCC and adjacent tissues were detected by qRT-PCR and immunohistochemistry method, and their correlation with the clinicopathologic parameters were analyzed. Results The expressions of E47 and LOXL2 mRNA in ESCC tissues were significantly higher than those in adjacent normal tissues (all P < 0.05). The positive expressions of E47 and LOXL2 proteins in ESCC were higher than those in adjacent tissues (all P < 0.05). The expression of E47 protein was closely correlated with invasion depth. The expression of LOXL2 protein was both significantly correlated with the level of histological differentiation and lymphatic metastasis (P < 0.05). E47 protein expression was positively correlated with LOXL2 expression (r=0.582, P < 0.05). Conclusion The mRNA and protein levels of E47 and LOXL-2 are highly expressed in ESCC tissues, and significantly correlated with clinicopathologic characteristics. High expressions of E47 and LOXL2 may promote the invasion and metastasis of ESCC, and they are expected to be the potential biomarkers for the prognosis.

In this paper the authors investigate the statistical properties of some cryptocurrencies by using three layers of analysis: alpha-stable distributions, Metcalfe’s law and the bubble behaviour through the LPPL modelling. The results show, in the medium to long-run, the validity of Metcalfe's law (the value of a network is proportional to the square of the number of connected users of the system) for the evaluation of cryptocurrencies; however, in the short-run, the validity of Metcalfe’s law for Bitcoin is questionable. According to the bidirectional causality between the price and the network size, the expected price increase is a driver for more investors to join the Bitcoin network, which may lead in the end to a super-exponential price growth, possibly due to a herding behaviour of investors. The authors then used LPPL models to capture the behaviour of cryptocurrencies exchange rates during an endogenous bubble and to predict the most probable time of the regime switching. The main conclusion of this paper is that Metcalfe’s law may be valid in the long-run, however in the short-run, on various data regimes, its validity is highly debatable.

European interest rates movements are affected by various internal and external factors. This paper studies the link between European and American short- and long-term interest rates. In particular, we consider the forward interest rates coming from euro and dollar IRS term structures. The econometric techniques employed are co-integration, Granger-causality, OLS and GMM. Our results indicate that European remote settlement forward and long-term interest rates are primarily driven by US rates and confirm that the causality acts mainly from the US to the Eurozone. This was true even during the recent periods of European Central Bank quantitative easing. These factors weaken the ECB’s ability to intervene. In fact, we found the impact of American monetary policy on long-term interest rates to be also relevant for European bonds.

Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR)-models are among the most popular tools.

In this paper, it is tried to propose a robust model for predicting inflation in Iran among alternative models. For doing this, monthly data from April 1990 to the end of September 2009 is used. Firstly, it is tried to determine whether the CPI data is chaotic or stochastic. It is shown that it is chaotic rather than stochastic. Therefore, it is predictable. Then, a stochastic differential equation model is estimated (specifically a geometric Brownian motion) for CPI in Iran. In order to compare the prediction power of the model other alternative models of prediction like ARMA, non-linear GARCH, EGARCH, TGARCH are also used to extrapolate inflation during a six month prediction period. Based on RMSE, MAE, U-Tail, it is revealed that stochastic differential equation model is much more robust than the alternative models mentioned above.