Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Tác động của sự không chắc chắn đến sai số dự đoán lạm phát ở các nước Trung và Đông Âu
Tóm tắt
Câu hỏi nền tảng của vấn đề nghiên cứu mà nghiên cứu này đề cập liên quan đến nhiều yếu tố, bao gồm sự không chắc chắn, có thể ảnh hưởng đến sai số dự đoán. Các công trình trước đây, chủ yếu tập trung vào các nền kinh tế hàng đầu thế giới, chưa đạt được đồng thuận về cách mà các tác nhân kinh tế xây dựng dự đoán về lạm phát hoặc lý do tại sao sai số dự đoán xảy ra. Có một khoảng trống trong tài liệu thực nghiệm cần được lấp đầy. Phân tích này bao gồm giai đoạn từ năm 2016 đến 2020 và bảy nền kinh tế: Albania, Cộng hòa Séc, Hungary, Ba Lan, Romania, Serbia và Thổ Nhĩ Kỳ. Chúng tôi xác minh xem liệu sai số dự đoán có bị ảnh hưởng bởi sản xuất, lạm phát, tỷ giá hối đoái, lãi suất, giá dầu, sự thay đổi trong ngữ điệu của các thông cáo từ ngân hàng trung ương và sự không chắc chắn của chúng hay không. Chúng tôi đánh giá xem liệu các tác nhân kinh tế có thể xử lý thông tin sẵn có để đưa ra dự đoán chính xác về lạm phát hay không. Kết quả cho thấy rằng cả người tiêu dùng và các chuyên gia đều không thực hiện được điều đó - họ thường xuyên trình bày các dự đoán không chính xác. Kết quả cũng cho thấy rằng sự biến động của tỷ giá hối đoái là biến số quan trọng nhất có ảnh hưởng tích cực đến sai số dự đoán, tiếp theo là lạm phát và sự biến động của nó. Điều này xác nhận (trong hầu hết các trường hợp) một giả định lý thuyết rằng một môi trường ổn định tốt hơn cho sự phát triển dài hạn khi các sai số dự đoán về lạm phát thấp hơn cho phép tối ưu hóa các quyết định kinh tế. Nghiên cứu này cho thấy rằng các cơ chế hỗ trợ dự đoán trong thời gian bất ổn cần phải được củng cố. Nó trình bày tập hợp các biến số cần được phân tích cẩn thận hơn bởi người tiêu dùng và các chuyên gia. Ngoài ra, các ngân hàng trung ương có thể cung cấp thông tin chính xác hơn về sự phát triển của các yếu tố gây sai số. Kết quả của chúng tôi dựa trên tài liệu hiện có bằng cách liên kết rõ ràng sự không chắc chắn trong kinh tế vĩ mô với sai số dự đoán, bao gồm cả cho các nền kinh tế nhỏ mở từ Eurasia.
Từ khóa
#lạm phát #sai số dự đoán #sự không chắc chắn #nền kinh tế #tỷ giá hối đoái #ngân hàng trung ươngTài liệu tham khảo
Abildgren, K., & Kuchler, A. (2021). Revisiting the inflation perception conundrum. Journal of Macroeconomics. https://doi.org/10.1016/j.jmacro.2020.103264
Algaba, A., Ardia, D., Bluteau, K., Borms, S., & Boudt, K. (2021). Econometrics meets sentiment: An overview of methodology and applications. Journal of Economic Surveys, 34(3), 512–547.
Amiram, D., Landsman, W. R., Owens, E. L., & Stubben, S. R. (2018). How are analysts’ forecasts affected by high uncertainty? Journal of Business Finances & Accounting, 45, 295–318. https://doi.org/10.1111/jbfa.12270
Anderl, C., & Caporale, G. M. (2023). Nonlinearities in the exchange rate pass-through: The role of inflation expectations. International Economics, 173, 86–101. https://doi.org/10.1016/j.inteco.2022.10.003
Apel, M., & Grimaldi, M. B. (2014). How informative are central bank minutes? Review of Economics, 65(1), 53–76.
Apergis, N. (2017). New evidence on the ability of asset prices and real economic activity forecast errors to predict inflation forecast errors. Journal of Forecasting, 36(5), 557–565. https://doi.org/10.1002/for.2453
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://academic.oup.com/qje/article-pdf/131/4/1593/30636768/qjw024.pdf. https://doi.org/10.1093/qje/qjw024
Balcilar, M., Gupta, R., & Segnon, M. (2016). The role of economic policy uncertainty in predicting us recessions: A mixed-frequency Markov-switching vector autoregressive approach. Economics, 10(27), 1–20. https://doi.org/10.5018/economics-ejournal.ja.2016-27
Baranowski, P., Bennani, H., & Doryń, W. (2021). Do the ECB’s introductory statements help predict monetary policy? Evidence from a tone analysis. European Journal of Political Economy, 66, 101964. https://doi.org/10.1016/j.ejpoleco.2020.101964
Baranowski, P., Doryń, W., Łyziak, T., & Stanisławska, E. (2021). Words and deeds in managing expectations: Empirical evidence on an inflation targeting economy. Economic Modelling, 95, 49–67. https://doi.org/10.1016/j.econmod.2020.12.003
Batchelor, R. A., & Orr, A. B. (1988). Inflation expectations revisited. Economica, 55(219), 317. https://doi.org/10.2307/2554010
Bec, F., & De Gaye, A. (2016). How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US, French and UK inflation forecasts. Economic Modelling, 53, 75–88. https://doi.org/10.1016/j.econmod.2015.11.008
Bekiros, S., & Paccagnini, A. (2015). Estimating point and density forecasts for the us economy with a factor-augmented vector autoregressive dsge model. Studies in Nonlinear Dynamics & Econometrics, 19(2), 107–136. https://doi.org/10.1515/snde-2013-0061
Bennani, H., & Neuenkirch, M. (2017). The (home) bias of European central bankers: New evidence based on speeches. Applied Economics, 49(11), 1114–1131. https://doi.org/10.1080/00036846.2016.1210782
Berge, T. J. (2018). Understanding survey-based inflation expectations. International Journal of Forecasting, 34(4), 788–801. https://doi.org/10.1016/j.ijforecast.2018.07.003
Binder, C. (2017). Fed speak on main street: Central bank communication and household expectations. Journal of Macroeconomics, 52, 238–251. https://doi.org/10.1016/j.jmacro.2017.05.003
Binder, C., McElroy, T. S., & Sheng, X. S. (2022). The term structure of uncertainty: New evidence from survey expectations. Journal of Money, Credit and Banking, 54(1), 39–71. https://doi.org/10.1111/jmcb.12811
Biswas, R. (2019). Does economic policy uncertainty affect analyst forecast accuracy? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3407668
Burke, M. A., & Ozdagli, A. (2023). Household inflation expectations and consumer spending: Evidence from panel data. The Review of Economics and Statistics. https://doi.org/10.1162/rest_a_01118
Carlson, J. A., & Parkin, M. (1975). Inflation expectations. Economica, 42(166), 123–38.
Castillo, P., Montoro, C., & Tuesta, V. (2020). Inflation, oil price volatility and monetary policy. Journal of Macroeconomics, 66, 103259. https://doi.org/10.1016/j.jmacro.2020.103259
Ca’Zorzi, M., Hahn, E., S’anchez, M. (2007). Exchange rate pass-through in emerging markets. Working Paper Series 739, European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp739.pdf
Chahine, S., Daher, M., & Saade, S. (2021). Doing good in periods of high uncertainty: Economic policy uncertainty, corporate social responsibility, and analyst forecast error. Journal of Financial Stability, 56, 100919. https://doi.org/10.1016/j.jfs.2021.100919
Cheikh, N. B., & Zaied, Y. B. (2020). Revisiting the pass-through of exchange rate in the transition economies: New evidence from new EU member states. Journal of International Money and Finance, 100, 102093. https://doi.org/10.1016/j.jimonfin.2019.102093
Chourou, L., Purda, L., & Saadi, S. (2021). Economic policy uncertainty and analysts’ forecast characteristics. Journal of Accounting and Public Policy, 40, 106775. https://doi.org/10.1016/j.jaccpubpol.2020.106775
Coibion, O., Gorodnichenko, Y. (2015). Is the Phillips curve alive and well after all? Inflation expectations and the missing disinflation. American Economic Journal: Macroeconomics, 7, 197–232. http://www.jstor.org/stable/43189954
Coibion, O., Gorodnichenko, Y., & Weber, M. (2022). Monetary policy communications and their effects on household inflation expectations. Journal of Political Economy, 130, 49–67.
Commission, E. (2016). The Joint Harmonised EU Programme of Business and Consumer Survey. User Guide. Technical Report. European Commission, Directorate-General for Economic and Financial Affairs.
Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal of Statistical Software, 27(2), 1–43. https://doi.org/10.18637/jss.v027.i02
D’Acunto, F., Hoang, D., Paloviita, M., Weber, M. (2019). Cognitive Abilities and Inflation Expectations. AEA Papers and Proceedings, 562–566. https://doi.org/10.1257/pandp.20191050
Dellas, H., Gibson, H. D., Hall, S. G., & Tavlas, G. S. (2018). The macroeconomic and fiscal implications of inflation forecast errors. Journal of Economic Dynamics and Control, 93, 203–217. https://doi.org/10.1016/j.jedc.2018.01.030. Monetary and Fiscal Policy Stabilization amid a Debt Crisis.
Dovern, J., Fritsche, U., & Slacalek, J. (2012). Disagreement among forecasters in G7 countries. The Review of Economics and Statistics, 94(4), 1081–1096.
Ehrmann, M., Eijffinger, S., & Fratzscher, M. (2012). The role of central bank transparency for guiding private sector forecasts. The Scandinavian Journal of Economics. https://doi.org/10.1111/j.1467-9442.2012.01706.x
Ehrmann, M., & Wabitsch, A. (2022). Central bank communication with non-experts—A road to nowhere? Journal of Monetary Economics, 127, 69–85. https://doi.org/10.1016/j.jmoneco.2022.02.003
European Uncertainty Index: Methodology (2023). https://www.policyuncertainty.com/methodology.html
Gali, J. (2008). Monetary Policy, Inflation, and the Business Cycle, an Introduction to the New Keynesian Framework. Princeton: Princeton University Press.
Gamber, E. N., Liebner, J. P., & Smith, J. K. (2015). The distribution of inflation forecast errors. Journal of Policy Modeling, 37(1), 47–64. https://doi.org/10.1016/j.jpolmod.2015.01.002
Geraats, P.M. (2014). Monetary Policy Transparency. CESifo Working Paper Series 4611
Gerberding, C. (2001). The information content of survey data on expected price development for monetary policy. Deutsche Bundesbank Discussion Paper 9
Glas, A., & Drechsel, K. (2021). Conditional macroeconomic forecasts: Disagreement, revisions and forecast errors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3861829
Grabowski, W., & Stawasz-Grabowska, E. (2021). How have the European central bank’s monetary policies been affecting financial markets in CEE-3 countries? Eurasian Economic Review, 11, 43–83. https://doi.org/10.1007/s40822-020-00160-3
Gülşen, E., & Kara, H. (2019). Measuring inflation uncertainty in turkey. Central Bank Review, 19(2), 33–43. https://doi.org/10.1016/j.cbrev.2019.06.003
Hansen, S., & McMahon, M. (2016). Shocking language: Understanding the macroeconomic effects of central bank communication. Journal of International Economics, 99, 114–133. https://doi.org/10.1016/j.jinteco.2015.12.008
Holden, K., & Peel, D. A. (1990). On testing for unbiasedness and efficiency of forecasts. The Manchester School, 58(2), 120–127. https://doi.org/10.1111/j.1467-9957.1990.tb00413.x
Hope, O.-H., & Tony, K. (2005). The association between macroeconomic uncertainty and analysts’ forecast accuracy. Journal of International Accounting Research, 4, 23–38. https://doi.org/10.2308/jiar.2005.4.1.23
Hubert, P. (2015). Do central bank forecasts influence private agents forecasting performance vs. signals? Journal of Money, Credit and Banking, 4(47), 771–789. https://doi.org/10.1111/jmcb.12227
Hubert, P. (2015). The influence and policy signaling role of FOMC forecasts. Oxford Bulletin of Economics and Statistics, 5(75), 655–680. https://doi.org/10.1111/obes.12093
Index, E.U. (2023). Europe Monthly Index. https://www.policyuncertainty.com/europe_monthly.html
Istrefi, K., & Piloiu, A. (2014). Economic policy uncertainty and inflation expectations. Banque de France Document de Travail
Kim, I., & Kim, Y. S. (2019). Inattentive agents and inflation forecast error dynamics: A Bayesian DSGE approach. Journal of Macroeconomics, 62, 103139. https://doi.org/10.1016/j.jmacro.2019.103139
Lieb, L., & Schuffels, J. (2022). Inflation expectations and consumer spending: The role of household balance sheets. Empirical Economics, 63, 2479–2512. https://doi.org/10.1162/rest_a_01118
Long, J.A. (2020). Panelr: Regression Models and Utilities for Repeated Measures and Panel Data. R package version 0.7.3. https://cran.r-project.org/package=panelr
Łyziak, T. (2014). Inflation expectations in Poland, 2001–2013. Measurement and macroeconomic testing. NBP Working Paper, 178, 1–36. https://doi.org/10.2139/ssrn.2646519
Łyziak, T. (2013). Formation of inflation expectations by different economic agents. Eastern European Economics, 51, 5–33. https://doi.org/10.2753/EEE0012-8775510601
Łyziak, T., & Mackiewicz-Łyziak, J. (2014). Do consumers in Europe anticipate future inflation? Has it changed since the beginning of the financial crisis. Eastern European Economics, 52, 5–32. https://doi.org/10.2753/EEE0012-8775520301
Mankiw, N. G., & Reis, R. (2002). Sticky information versus sticky prices: A proposal to replace the New Keynesian Phillips Curve. The Quarterly Journal of Economics, 17, 1295–1328. https://doi.org/10.1162/003355302320935034
Minot, N. (2014). Food price volatility in sub-Saharan Africa: Has it really increased? Food Policy, 45, 45–56. https://doi.org/10.1016/j.foodpol.2013.12.008
Mital, P., Goetschalckx, M., & Huang, E. (2015). Robust material handling system design with standard deviation, variance and downside risk as risk measures. International Journal of Production Economics, 170, 815–824. https://doi.org/10.1016/j.ijpe.2015.02.003
Nasir, M. A., Huynh, T. L. D., & Vo, X. V. (2020). Exchange rate pass-through & management of inflation expectations in a small open inflation targeting economy. International Review of Economics & Finance, 69, 178–188. https://doi.org/10.1016/j.iref.2020.04.010
Nolte, I., Nolte, S., & Pohlmeier, W. (2019). What determines forecasters’ forecasting errors? International Journal of Forecasting, 35(1), 11–24. https://doi.org/10.1016/j.ijforecast.2018.07.007. Special Section: Supply Chain Forecasting.
Picault, M., & Renault, T. (2017). Words are not all created equal: A new measure of ECB communication. Journal of International Money and Finance, 79, 136–156. https://doi.org/10.1016/j.jimonfin.2017.09.005
Pierdzioch, C., Gupta, R. (2017). Uncertainty and forecasts of U.S. recessions. Working Papers, University of Pretoria, Department of Economics. https://doi.org/10.5018/economics-ejournal.ja.2016-27.
Reif, M. (2021). Macroeconomic uncertainty and forecasting macroeconomic aggregates. Studies in Nonlinear Dynamics & Econometrics Journal of Business & Economic Statistics, 25(2), 20190073. https://doi.org/10.1515/snde-2019-0073
Reis, R. (2006). Inattentive consumers. Journal of Monetary Economics, 56, 1295–1328. https://doi.org/10.1016/j.jmoneco.2006.03.001
Segnon, M., Gupta, R., Bekiros, S., & Wohar, M.E. (2018). Forecasting US GNP growth: The role of uncertainty. Journal of Forecasting, 37(5), 541–559. https://doi.org/10.1002/for.2517
Szyszko, M., Rutkowska, A., & Kliber, A. (2020). Inflation expectations after financial crisis: Are consumers more forward-looking? Economic Research-Ekonomska Istraživanja, 33, 1052–1072. https://doi.org/10.1080/1331677X.2019.1595083
Szyszko, M., Rutkowska, A., & Kliber, A. (2022). Do words affect expectations? The effect of central banks communication on consumer inflation expectations. Quarterly Review of Economics and Finance, 86, 221–229. https://doi.org/10.1016/j.qref.2022.07.009
Szyszko, M., Rutkowska, A., & Pietrzak, M. (2020). When all we have is not enough: A search for the optimal method of quantifying inflation expectations. Economic Research-Ekonomska Istraživanja, 36(1), 977–996. https://doi.org/10.1080/1331677X.2022.2081231
Wander, B. H., & D’Vari, R. (2003). The limitations of standard deviation as a measure of bond portfolio risk. The Journal of Wealth Management, 6(3), 35–38. https://doi.org/10.3905/jwm.2003.320488
Woodford, M. (2003). Interest and Prices. Foundations of a Theory of Monetary Policy. Princeton University Press.
Woodford, M. (2013). Macroeconomic analysis without the rational expectations hypothesis. Annual Review of Economics, 5(1), 303–346. https://doi.org/10.1146/annurev-economics-080511-110857
Zhao, Y. (2022). Internal consistency of household inflation expectations: Point forecasts vs. density forecasts. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2022.08.008