Economic Research and Perspectives

Economic Research and Perspectives

Inflation Expectations in the Iranian Economy A Random Forest Approach

Authors
abolfazl dehghani 1
Kazem Yavari 2
Mehdi Haj amini 3
Mohammad Hassan Zare 4
1 Ph. D. Student of Economics, Department of Economics, Yazd University, Yazd, Iran
2 Professor of Economics, Department of Economics, Yazd University, Yazd, Iran
3 Associate Professor of Economics, Department of Economics, Yazd University, Yazd, Iran,
4 Associate Professor of Economics, Department of Economics, Yazd University, Yazd, Iran
10.48311/ecor.2025.13665
Abstract
Measuring unobservable variables such as inflation expectations or potential output is inherently challenging. Inflation expectations, as a key variable, play a central role in many macroeconomic and monetary models. In Iran, despite decades of persistent double-digit inflation, no official survey-based data on inflation expectations has been produced, unlike in many other countries. However, the literature suggests that comparing model-based approaches with survey data can yield valuable insights. This study aims to estimate and present quarterly inflation expectations for Iran during 1996–2021 using the Random Forest regression method within the framework of rational expectations. After training the model, in-sample predictions were generated and evaluated based on rational expectations properties. The analysis also ranked the importance of variables contributing to the formation of inflation expectations. The results indicate that inflation expectations in Iran can be reasonably explained within the rational expectations framework, with no evidence of systematic forecast errors. Among the informational variables considered, inflation lags, exchange rate, and economic sanctions were identified as the most influential factors

Aim and Introduction

Measurement and examination of unobservable variables directly such as inflation expectations or potential output, is really challenging. Inflation expectations have been considered a key variable in many macroeconomic models, particularly in the realm of monetary economics. Macroeconomic models assume that economic agents make consumption, savings, and labor market decisions based on their perception of future inflation levels, and these decisions play a great role in realizing economic variables, including inflation. The role of inflation expectations differs from other inflation-generating factors. While factors such as money supply, budget deficit, exchange rate, and to some extent, economic sanctions can be considered as policy tools. Inflation expectations normally result from the interaction of other factors and may potentially predict future inflation. For example, an increase in the budget deficit, if not addressed independently by the Central Bank, can lead to an increase in money supply, inflation, and intensification of inflation expectations. Thus, inflation expectations can be considered as a variable that evolves within society and changes due to other inflation-generating factors. However, once formed, these expectations themselves become significant factors in inflation and other economic variables. Unlike many countries, in Iran, despite the importance of inflation due to decades of double-digit inflation, no action has been taken to produce and provide survey data related to this variable. However, according to existing literature, comparing the results of alternative methods incorporating inflation expectations with survey data can provide valuable insights. In practice, incorporating inflation expectations can improve the performance of inflation prediction models.

Methodology

Empirical research indicates that methods that consider inflation expectations along with its fluctuations and dynamics outperform models that do not consider these dynamics. Therefore, paying proper attention to how inflation expectations form and fluctuate, as well as avoiding simple methods, is necessary in calculating inflation expectations. In this research, an attempt was made to calculate and present data related to this variable in the framework of rational expectations for the period of 1996 to 2021 using the random forest regression method, considering the strengths and weaknesses of each method of mapping inflation expectations. Subsequently, after learning the random forest-based model, by conducting an in-sample prediction, the data were extracted and the features related to rational expectations regarding these data were examined.

Findings

The coefficient of determination value for the test data was found to be 80%, indicating that, on average, 80% of inflation variations are correctly predicted by economic factors using the model inputs or features. Based on this and by examining the features related to estimation residuals, it was determined that economic factors in predicting inflation do not exhibit systematic errors and, with a sufficiently large time interval and having an adequate information set, can have a proper understanding of inflation behavior. Moreover, the results of comparing inflation expectations based on random forest regression-based predictions show superiority of this approach compared to competing methods such as the Hodrick-Prescott filter. After that, the importance of each of the factors in the basket of information related to inflation expectations was ranked. It should be noted that the selection of features for predicting inflation expectations was not based on the direct attention of households and economic factors to these features. Rather, economic factors and households may find the effect of these features in other evidence. For example, the effect of an increase in the exchange rate on the prices of goods that are somehow related to this variable may be apparent to households, and fundamentally, the prevalent interpretation of rational expectations in the literature of this field is based on this approach. The results of this ranking indicate that among the entire information set, factors such as inflation breaks, exchange rates, and economic sanctions had the highest importance in shaping inflation expectations.

Discussion and Conclusion

It is worth mentioning that inflation breaks have been identified as the most important factor among the entire information set as a manifestation of the adaptive section of inflation expectations. However, this does not mean that expectations are entirely adaptive. Based on the research findings, it is clear that if economic factors rely solely on the adaptive section to predict inflation, zero estimation error, unpredictability of errors, and consequently the formation of rational expectations will not be achieved. Using a combination of three approaches: gradient boosting algorithm, random forest algorithm, and linear regression, a voting regression was also performed, showing a 3% improvement in determination coefficient compared to random forest (83%). Moreover, other results, such as the order and intensity of feature importance, and predicted inflation values, are similar to the random forest method with slight variations which means, estimating rational expectations is reliable
Keywords
Expectations
Inflation expectations
Rational expectations
Random Forest Regression

Subjects

افشاری، زهرا؛ بیات، مرضیه. (1393). مقایسه قدرت پیش‌بینی منحنی فیلیپس کینزین جدید هایبریدی و مدل ARIMA از تورم. اقتصاد مالی (اقتصاد مالی و توسعه)، 8(26)، 1-11.
افشاری، زهرا؛ یزدان پناه، احمد؛ بیات، مرضیه. (1388)، NAIRU و سیاست‌گذاری اقتصادی در ایران. تحقیقات اقتصادی، 44(87)، 1-26.
باستانی‌فر، ایمان. (1395). برآورد تابع تقاضای پول تعدیل‌شده کیگان با تکانه‌های برون‌زا در اقتصاد ایران. تحقیقات اقتصادی، 51(4)، 759-776.
باستانی‌فر، ایمان؛ صمدی، سعید. (1396). تحلیل عوامل موثر بر شکل‌گیری انتظارات تورمی ناشی از تحولات سیاسی و تغییرات نقدینگی. سیاست‌گذاری اقتصادی، 10(20)، 45-49.
پژوهشکده پولی و بانکی، بانک مرکزی جمهوری اسلامی ایران. (1382). چهارچوب پولی متکی بر هدف گذاری تورم و زمینه اجرای آن در ایران. تهران: پژوهشکده پولی و بانکی.
توکلیان، حسین. (1391). بررسی منحنی فیلیپس کینزی جدید در قالب یک مدل تعادل عمومی پویای تصادفی برای ایران. مجله تحقیقات اقتصادی، 47(3)، 1-22.
جلائی، سید عبدالمجید؛ شیرافکن، مهدی. (1388). تاثیر سیاست‌های پولی بر سطح بیکاری از طریق تحلیل منحنی فیلیپس نیوکینزین در ایران، پژوهشنامه علوم اقتصادی، 18(24)، 91-152.
حسینی، صفدر؛ شکوهی، مریم؛ (1392). بررسی عوامل موثر بر تورم با تاکید بر نقش انتظارات گذشته‌نگر و آینده‌نگر. فصلنامه پژوهش‌های اقتصادی، 15، 209-228.
عیسی‌زاده، سعید؛ مروت، حبیب؛ شریفی، امید. (1395). شبیه‌سازی انتظارات تورمی ناهمگن در ایران. فصلنامه مدل‌سازی اقتصادی، 10(4)، 123-101.
کازرونی، علیرضا؛ اصغرپور، حسین؛ نفیسی مقدم، مریم. (1396). بررسی عوامل موثر بر تورم در ایران: کاربرد منحنی فیلیپس هایبریدی کینزی‌های جدید(رویکرد رگرسیون کوانتایل). پژوهش های اقتصاد پولی مالی، 24(13)، 115-134.
گرجی، ابراهیم؛ فولادی، مهدی(1388). مقایسه تطبیقی منحنی فیلیپس کینزی‌های جدید با منحنی‌های فیلیپس متعارف برای اقتصاد ایران. مجله تحقیقات اقتصادی،48(13). 7-18.
متقی، لیلی. (1377). تبادل میان تورم و بیکاری و آزمون نرخ طبیعی بیکاری و NAIRU در ایران، رساله دکتری، دانشگاه تهران.
منجذب، محمدرضا؛ علیمردانی، مهرنوش. (1400). بررسی تاثیر انتظارات تورمی بر مصرف در ایران: انتظارات تطبیقی در برابر عقلایی (رهیافت کالمن‌فیلتر). فصلنامه اقتصاد مقداری (بررسی‌های اقتصادی سابق)، 18(2)، 27-42.
والش، ک (1394). تئوری و سیاست پولی (ترجمه محمد جوادی، امیررضا عبدلی)، دنیای اقتصاد.
هاندا، ج. (2009). اقتصاد پولی (ترجمه علی سوری)، نور علم.
Adam, K. & Padula, M. 2011. Inflation Dynamics and Subjective Expectations in the United States. Economic Inquiry, 49, 13-25.
Arnold, E. A. 2013. The Role of Data Revisions and Disagreement in Professional Forecasts. National Bank of Poland.
Ball, L. 2000. Near-Rationality and Inflation in Two Monetary Regimes. Cambridge MA: NBER.
Barret, Ph and Adams, J. 2022. Shocks to Inflation Expectations. IMF Working Papers.
Bellemare, CH, Tossou, R and Moran, K. 2020. The Determinants of Consumers’ Inflation Expectations: Evidence from the US and Canada. Staff Working Paper, bank of canada.
Branch,W.A., 2004. The theory of rationally heterogeneous expectations: evidence from survey data on inflation expectations. Econ.J.114(497), pp. 592–621.
Bray, M. M. & SAVIN, N. E. 1986. Rational Expectations Equilibria, Learning and Model Specification. Econometrica, 54, 1129-1160.
Breiman, L. (2001). RANDOM FORESTS. Ensemble machine learning: Methods and applications, 157-175.
Cagan, P. 1956. The Monetary Dynamics of Hyperinflation. In: FRIEDMAN, M. (ed.) Studies in the Quantity Theory o f Money. Chicago: University of Chicago Press.
Carlson, J. A. & PARKIN, M. 1975. Inflation Expectations. Economica, 42, 123-138.
Curtin, R. T. 2005. Inflation Expectations: Theoretical Models and Empirical Tests. University of Michigan.
Evan, G.W., Honkapohja, S., (2001), Learning and Expectations in Macroeconomics, Princeton University Press, Princeton, NJ.
Figlewski, S. & Wachtel, P. 1981. The Formation of Inflationary Expectations. The Review of Economics and Statistics, 63.
Friedman, M. (1968) The role of monetary policy; The American Economic Review, 58, pp. 1-17.
Friedman, M. 1957. Theory o f the Consumption Function, Princeton, Princeton University Press.
Fuertes, A & Gimeno, R. 2017. Inflation expectation indicators based on financial instrument prices. ECONOMIC BULLETIN.
Gali, J., & Gertler, M. (1999). "Inflation Dynamics: A Structural Econometric analysis" . Journal of monetary Economics, 44(2), pp. 195-222.
George W. Evans & Seppo Honkapohja, (2001), Learning and Expectations in Macroeconomics, Princeton University Press.
Gramlich, E. M. 1983. Models of Inflation Expectations Formation: A Comparison of Household and Economist Forecasts. Journal o f Money, Credit and Banking, 15, 155-173.
Gros, D, Shamsfakhr, F and Alcidi, C. 2022. Inflation expectations: models and measures. Policy Department for Economic, Scientific and Quality of Life Policies.
Henry, R. 2013. Exploring the Formation of Inflation Expectations in Jamaica: A Pragmatic Approach. Research Services Department, Bank of Jamaica. 21, 46-52.
Hicks, J. R. 1939. Value and Capital, New York, Oxford University Press.51, 581-606
Ho, T.K. (1995) Random Decision Forest. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, 14-16 August 1995, 278-282.
Keane, M. P. & RUNKLE, D. E. 1990. Testing the Rationality of Price Forecasts: New Evidence from Panel Data. The American Economic Review, 80, 714-735.
Keynes, J. M. 1936. The General Theory o f Employment, Interest and Money, London, Macmillan.
Lahari, K. 1976. Inflationary Expectations: Their Formation and Interest Rate Effects. The American Economic Review, 66, 124-131.
Lai, K. 1990. An Evaluation of Survey Exchange Rate Forecasts. Economics Letters, 32, 61-65.
Lucas, E. R, (1976), Econometric Policy Evolution: A Critique, North-Holland Publishing Co., Amesterdam, pp. 19-46.
Lucas, R. E. (1972). Expectations and the Neutrality of Money. Journal of economic theory, 4(2), pp. 103-124.
Madeira, C. & ZAFAR, B. 2012. Heterogeneous Inflation Expectations.
Mankiw N. G. (2007) Comments Presented at Federal Reserve Conference Price Dynamics: Three Open Questions; Journal of Money, Credit and Banking, Supplement to Vol. 39, No. 1, pp. 187-192.
Mankiw, N. G., REIS, R. & WOLFERS, J. 2003. Disagreement About Inflation Expectations. NBER Macroeconomics Annual, 18, 209-248.
Maugeri, N. 2012. How Rational Are Rational Expectations? New Evidence from Well Known Survey Data. European University Institute and University of Sienna.
Mehra, Y. P. 2002. Survey Measures of Expected Inflation: Revisiting the Issues of Predictive Content and Rationality. Federal Reserve Bank o f Richmond Economic Quarterly, 88, 17-36.
Minford, P. 1986. Expectations and the Economy. The Institute of Economic Affairs.86- 329-336.
Muller, A, Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists.
Muth, J, F. (1961), Rational Expectations and the Theory of Price Movements, Econometrical, July, 29, pp. 315-335.
Muth, J, F. (1961), Rational Expectations and the Theory of Price Movements, Econometrical, July, 29, pp. 315-335.
Nerlove, M. 1958. The Dynamics of Supply: Estimation of the Farmers' Response to Price, Baltimore, John Hopkins University Press.
Rich, R. W. 1989. Testing the Rationality of Inflation Forecasts from Survey Data:Another Look at the SRC Expected Price Change Data. The Review of Economics and Statistics, 71, 682-686.
Rodina, F. 2018. Estimating unobservable inflation expectations in the New Keynesian Phillips Curve. WORKING PAPER, Department of Economics, University of Ottawa.
Sargent, J. (1996), Expectations and the Nonneutrality of Lucas, Journal of Monetary Economics, 37(3), pp. 535-548.
Sargent, J. 1983. An Economist's Foreword to Prediction and Regulation by Linear Least-Squares Methods. Journal of Monetary Economics, 72, 382-389.
Sargent. J. Th, (1982), The Ends of Four Big Inflations, in Robert E. Hall(ed.), University of Chicago Press, Chicago, pp. 21-30.
Schafer, J. 2022. Inflation Expectations and Their Formation. Working Paper Series, Washington, D.C press.
Shaw, G. K. 1984. Rational Expectations: An Elementary Exposition, Great Britain, Wheatsheaf Books Ltd.
Tanzi, V. 1980. Inflationary Expectations, Economic Activity, Taxes, and Interest Rates. The American Economic Review, 70, 12-21.
Thomas, L. B., JR. 1999. Survey Measures of Expected U.S. Inflation. Journal o f Economic Perspectives, 13, 125-144.
Turnovsky, S. J. 1970. Empirical Evidence on the Formation of PriceExpectations. Journal o f the American Statistical Association, 65.
Vellekoop, N & Wiederholt, M. 2019. Inflation Expectations and Choices of Households. University of Frankfurt press.