The Unassuming Economist

From a paper by Gazmend Dehari, and Sindise Salihi:

“There are different structures that enable a central bank to strengthen the performance of a monetary policy. Price stability is essential to central banks in order to promote a successful monetary policy and they have unequivocal authority in determining the way on how to achieve it. Controlling price movements between a particular bandwidth through inflation targeting, is one way that can contribute in addressing and achieving the specific goals of a monetary policy. There are different structures that enable a central bank to strengthen the performance of a monetary policy. Price stability is essential to central banks in order to promote a successful monetary policy and they have unequivocal authority in determining the way on how to achieve it. Controlling price movements between a particular bandwidth through inflation targeting, is one way that can contribute in addressing and achieving the specifics goals of a monetary policy. This paper will start by comparing the actual and forecasted inflation, based on quarterly data from OECD for the period 2010 – 2026. Price data is derived from Harmonized core inflation for the 17 member of the Euro area (EA17). For the most part of the period, inflation stud firmly below the 2% level i.e. in accordance with the objectives of the ECB, before spiking to historic levels during the COVID 19 pandemic, peaking around 5.5 percent, and stabilizing afterword’s at the 2 percent level, until the end of 2026. Evaluating the forecast error, three metrics will be employed, based on the work of Czekaj et al. (2024). These methods are: mean absolute forecast error (MAE), root mean squared forecast errors (RMSE) and mean absolute percentage error (MAPE). In the case of MAE for yearly data inflation forecast was off by only 0.83 percent, meaning that inflation targeting by the ECB was in line with the expectations, because predictive inflation error was inferior of 2 percent, lower than the target set by the central bank. The same thing cannot be said for the quarterly data, where mean absolute error is 2.89 percent, considerably greater than the ECB objective. MAPE, method is used to calculate the average forecast error, with yearly and quarterly data, the results show that on average the prediction is off by 0.54 percent and 0.58 percent respectively. Considering the RMSE method, it produces on average a prediction for yearly and quarterly data, off by 1.27 and 1.34 respectively. In the case when forecast accuracy measures are considered separately, q3 and q4 quarters are the quarters where prediction accuracy is the lowest for the three methods. Examining the accuracy of forecasted measure for inflation targeting economies, the results are in line with Czekaj et al. (2024) and Schnabel (2024). Inflation targeting has contributed in stabilizing price increases with one exception, during a sudden economic shock, i.e. COVID 19 crises. Moreover, through forecasted accuracy methods this study showed that in q3 and q4 quarters the errors where the highest and volatility of errors in predicting inflation was the greatest during the shock of COVID 19 crises.”

From a paper by Christian Glocker, and Thomas Url:

“The gradual rise in temperatures motivates conceptualizing climate change as a phenomenon shaping the propagation of macroeconomic shocks, rather than as an independent shock. We formalize this in a theoretical model, showing that climate change induces a structural shift by steepening the aggregate supply curve, exacerbating the price effects of demand shocks while dampening the output response. Our empirical evidence is consistent with this prediction: higher temperatures raise the share of inflation variation attributable to demand shocks by up to 10 percentage points, underscoring the role of climate change in intensifying stagflationary dynamics rather than independently driving business cycles.”

From a paper by Pedro M. Esperança, and Paulo Júlio:

“The literature has focused primarily on the quality of forecasts for real Gross Domestic Product (GDP) and for other macroeconomic variables (e.g. inflation or unemployment), but has not satisfactorily addressed the forecast accuracy of the major expenditure components of GDP – private consumption (C), government consumption (G), investment (I), exports (X) and imports (M)4. In a recent article, Júlio et al. (2011) have analyzed, for the first time, the quality of forecasts for GDP expenditure components. The authors showed that overpredictions in investment and exports explain most of Portuguese GDP overpredictions at 1-year horizons. GDP forecast bias diminishes significantly for same-year predictions, a fact that is mostly explained by canceling out effects in component prediction errors rather than by accurate component predictions. The authors have also proposed two new statistics – Mean of Total Weighted Absolute Error (MTWAE) and Mean of Total Weighted Squared Error (MTWSE) – to objectively evaluate the overall accuracy of component predictions.

This article uses similar techniques to analyze the forecast quality of GDP expenditure components for G7 countries. Three dimensions of forecast quality are addressed here: bias, accuracy, and efficiency. We use forecast data issued by the Organization for Economic Cooperation and Development (OECD) and by the International Monetary Fund (IMF) for the 1993-2010 period, and evaluate both 1-year ahead and same-year predictions. Our focus lies on the overall quality of institutions’ forecasts, and thus we pool evaluation statistics across countries in order to obtain an aggregate overview of the main features driving these forecasts. In addition, we propose panel versions of two types of efficiency tests presented in the literature, and analyze the effects of the 2008 crisis on the quality of forecasts.”

From a paper by Andrew Jackson, Romain Svartzman, David Barmes and Luiz Awazu Pereira da Silva:

“Climate change and volatile fossil fuel prices increasingly drive macroeconomic and price instability. A successful green transition is a precondition for price stability in the long term but could generate inflationary pressures over shorter time horizons. A restrictive monetary response to such pressures would disproportionately affect the capital-intensive green investment needed for a transition. To maintain price stability without compromising the green transition, we propose adaptive inflation targeting, adjustments to monetary operations, and an institutional architecture for systematic monetary–fiscal coordination.”

From a paper by Surjit Bhalla, Karan Bhasin, and Prakash Loungani:

“The trend inflation levels in India and other emerging market economies suggest a downward trajectory regardless of the adoption of inflation targeting. Therefore, it is difficult to conclusively establish that the adoption of inflation targeting in India led to a moderation in inflation or anchoring of inflation expectations. There is also some evidence that the anchoring of household expectations predates the formal adoption of inflation targeting. Similarly, long-term expectations in India have remained firmly anchored since the early 2000s. In terms of growth, the high real interest rates policy followed during the initial years of inflation targeting has adversely affected India’s growth dynamics.”