Do you think I have forgotten about you?
I was about to tell you I had not spent a day without thinking about you.
But just now, I changed me mind. I came to realize what I want and need.
Do you think I tell people things?
So just one drink, tell me how happy you are there, and then you may go.
And now, bring it on. Bring me that horizon.
In their article, Allcott and Gentzkow (2017) present a theoretical and empirical framework for examining the economics of fake news, focusing on its role in the context of social media during the 2016 United States of America (US) presidential election. It discusses fake news and voting behavior in the presidential election by investigating various aspects of fake news during the election period, including who generates and believes in fake news, the economic model of fake news, and the factors influencing individuals’ capacity to differentiate between authentic and fabricated news.
Additionally, the study examines the polarization of beliefs concerning fake news and identifies the factors associated with ideologically aligned interpretations. It develops research questions aligned with its objectives, including who generates and believes in fake news, how is fake news distinct from biased or tilted media, how to comprehend the economic model of fake news, how much fake news the typical voter sees in the run-up to the 2016 election, and what individual characteristics predict correct beliefs regarding headlines.
Theoretically, social media platforms may be especially conducive to fake news for several reasons (Allcott & Gentzkow, 2017). Firstly, there is almost no entry cost to access social media. The fixed costs of entering the social media market and creating content are extremely small, implying an increase in the relative profitability of the small-scale, short-term strategies fake news producers often employ and a decline in the relative significance of establishing a long-term reputation for quality. Secondly, the display format of social media can make it difficult to verdict the accuracy of an article.
The study employs a combination of research methods, encompassing primary data using survey research and secondary data from various related website scraping, presented in terms of descriptive statistics and linear regression to address research questions. The analysis scope focuses on fake news articles with political implications for the 2016 US presidential elections. The primary data from an online survey conducted post-election through the SurveyMonkey platform is employed to gather information about individuals’ exposure to fake news, their political beliefs, as well as their demographic characteristics, political affiliations, consumption of election-related news, and their recollection and belief in news headlines.
The online sample is reweighted to ensure it aligns with the characteristics of the nationwide adult population. The secondary data of fake news stories are collected from relevant websites, including Snopes, PolitiFact, and BuzzSumo, to build a database of fake news articles. The findings of the analysis are presented using descriptive statistics and a narrative explanation. Additionally, linear regression analysis examines the inference about true versus false news headlines and the associated determinants.
The main findings of the study suggest that while social media referrals play a minor role in driving traffic to mainstream news websites, it has a significantly larger role for fake news websites, and indicate that trust in information obtained through social media is lower when compared to trust in traditional news sources. Additionally, the research confirms that fake news was extensively disseminated and predominantly favored Donald Trump. Furthermore, the typical US adult likely encountered one or more news stories in the months leading up to the election, with a notable emphasis on pro-Trump articles. The study also underscores the positive relationships between education, age, total media consumption, and the accuracy of beliefs regarding the veracity of headlines. Moreover, individuals were more inclined to share fake news if it confirmed their pre-existing beliefs and if they were actively engaged in politics.
While the article provides rigorous analysis and the acknowledgment of its limitations that can be valuable in developing future research on the same subject, it could be advantageous to explicitly incorporate the concept of confirmation bias in decision-making heuristics and biases within the analysis to make it more comprehensive. Confirmation bias represents the inherent tendency to notice, concentrate on, and attribute greater credibility to evidence that aligns with individuals’ pre-existing beliefs.
The propensity of individuals to seek out information that confirms their convictions while disregarding contradictory information is indirectly corroborated by the article’s finding that those with segregated social networks are notably more inclined to trust ideologically aligned articles. This implies that they are less likely to encounter information from their social peer that challenges their existing views. Overall, the article could be a good reminder to carefully filter every information received particularly through social media.
Reference
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-236.
An anomaly in economics occurs when an empirical outcome is challenging to justify, or when it requires unrealistic assumptions for its explanation using the underlying theories. In simpler terms, it pertains to a real-life scenario that contradicts or departs from the conventional theory.
The anomalies discussed by Kahneman et al. (1991) are the endowment effect and status quo bias, which demonstrate a lopsided value in which the pain of losing something (disutility) is stronger than the joy of gaining the equivalent item (utility), known as loss aversion. In general, these anomalies of the endowment effect and status quo bias demonstrating the loss aversion pose a challenge when trying to account for them within the framework of the rational choice model that assumes agents possess stable, clearly defined preferences and consistently make rational choices of maximizing utility in markets that eventually achieve equilibrium.
In particular, the core consequence of the endowment effect anomaly is an amplified reluctance to part with one’s owned items, underscoring that discomfort associated with perceived losses is more pronounced than the regret of missing out on potential gains. Therefore, viewpoints of fairness are significantly influenced by whether the situation is framed as a gain deduction or an actual loss, indicating that individuals do not consistently maintain stable and well-defined preferences.
Moreover, a status quo bias emerges when individuals are pronouncedly inclined to stick with the existing state (status quo), primarily because the drawbacks of departing from it seem more substantial. When an alternative is designated as the new status quo, it tends to gain considerable acclamation, and the perceived disadvantages of making a change appear to outweigh the benefits. Such instances contrast the traditional assumption of maintaining a consistent preference order since the preference order is contingent on the current reference point.
Additionally, loss aversion suggests that individuals react more strongly to the aspect in which they perceive losses concerning their reference point. While the conventional theory of rational choice assumes that utility primarily stems from the level of wealth rather than changes relative to a reference point, it is essential to recognize that utility is contingent on changes, not absolute levels of wealth. Consequently, a specific difference between two choices will exert a more significant influence when it is perceived as a distinction between two disadvantages relative to the reference point rather than two advantages.
The Prospect Theory’s three cognitive principles offer potential explanations for various anomalies, including the evaluation based on a neutral reference point, diminishing sensitivity, and loss aversion. Firstly, assessments are made relative to a reference point that typically aligns with the status quo but can represent one’s expected outcome or perceived entitlement. Gains are situations where outcomes exceed the reference point, while losses occur when they fall below it. Secondly, the principle of diminishing sensitivity can be applied to the evaluation of changes in wealth, clarifying that distinctions become increasingly challenging to discern as individuals move farther from their reference point. For example, the subjective distinction between $1,000 and $1,100 is much smaller than that between $200 and $300.
Lastly, the loss aversion principle can explain the different risk aversion attitudes towards gains and losses, suggesting that the response to losses is more salient than the response to corresponding gains. Hence, when directly compared or weighted against each other, the extent of utility loss associated with losses surpasses the utility increase associated with gains. The last two principles can result in risk aversion for gains and risk seeking for losses. Overall, at least two aspects of the article can be appreciated: the use of colloquial examples and relevant literature to explain anomalies and the contribution to economic rational choice theory by filling gaps in the literature stemming from unrealistic or impractical assumptions.
References
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193-206.
Many theories, inventions, innovations, and technologies arise from human curiosity and the search for information and truth. However, while curiosity is part of human natural characteristics that might lead to information gathering, it might bring drawbacks if the collected information is not filtered. Information overload and the negative impact of social media are essential subtopics on issues regarding information in the digital era. While it is better to know more about something relevant to an individual’s life and goals, the less one knows about things unrelated to personal matters might reduce unnecessary drama or negative emotions, which in turn might result in better peace of mind.
The information overload phenomenon has been known by many different names, including information overabundance, infobesity, infoglut, data smog, information pollution, information fatigue, social media fatigue, social media overload, information anxiety, library anxiety, infostress, infoxication, reading overload, communication overload, cognitive overload, information violence, and information assault (Bawden & Robinson, 2020). There is no single generally accepted definition, but it can best be understood as that situation that arises when there is so much relevant and potentially useful information available that it becomes a hindrance rather than a help. Its essential nature has not changed with changing technology, though its causes and proposed solutions have changed much.
Specifically, technological advancement has brought about information that is easily accessible, resulting in information overabundance that could lead to a paradox. Orman (2016) discussed this topic and suggested that the paradox lies in such a way that people know more, whereas they understand less. The paradox is due to at least three main factors. Firstly, the “substitution problem” emerges from prioritizing quantity over quality, sometimes compromising information quality. Secondly, information serves as an agent of change, making it non-neutral concerning the physical world. However, using the information to exploit new opportunities leads to the obsolescence of existing information and subsequent information loss. Thirdly, information’s competitive use to deceive others diminishes their knowledge, underscoring its potential to manipulate and control their behavior.
Therefore, effective strategies to evade information overload, including the negative impact of social media, encompass filtering, withdrawal, queuing, and adopting a ‘satisficing’ approach (Bawden & Robinson, 2020). Enhanced design of information systems, adept management of personal information, and the cultivation of digital and media literacies also contribute to mitigation. A potential solution to information overload might be pursuing a mindful equilibrium while assimilating information and striving for comprehension.
Moreover, one channel through which individuals can access information is social media platforms like TikTok, Instagram, and Facebook. However, these platforms can yield negative consequences due to the lack of user discretion. Users could control the accounts they choose to follow or connect with, thus receiving updates from those selected accounts. Consequently, online communities create a platform for discussions encompassing health issues, challenges in daily life, and adverse events. This could lead to stigmatization reduction, a heightened sense of belonging, and increased perceived emotional support (Hong & Kim, 2020). Social media also offers possibilities to bolster users’ mental well-being by facilitating social connections and peer support (Naslund et al., 2020). Moreover, during the COVID-19 pandemic, friendships, positive social interactions, and humor shared on social media were found to alleviate stress (Marciano et al., 2022).
Social media (TikTok, Instagram, and Facebook) are cancer – there is a lot of useful content, but junk contents are much plenty, and it could be hypothetically correlated with mental health.
G. Wirjawan (2023)
Conversely, various studies have highlighted the potential adverse impacts of utilizing social media on mental well-being. Some problems related to social media include dissatisfaction with one’s body image (Harriger et al., 2023), an increase in the likelihood of addiction and involvement in cyberbullying (Naslund et al., 2020), and a negative influence on mood (Valkenburg, 2022). Furthermore, overindulgence in social media could be attributed to intensified feelings of loneliness, the fear of missing out, and diminished subjective well-being and life satisfaction (Valkenburg, 2022). Users prone to becoming addicted to social media often report experiencing symptoms of depression and reduced self-esteem (Bányai et al., 2017).
Braghieri et al. (2022) utilized the gradual introduction of Facebook in various US colleges to measure the effect of social media on mental well-being. Their findings indicated that the introduction of Facebook to a college setting had an adverse influence on students’ mental health. The primary mechanism behind this effect is through the emergence of unfavorable social comparisons. Overall, the outcomes imply that social media might contribute, at least partially, to the recent decline in mental health among adolescents and young adults. Additionally, a systematic review has identified a connection between social media envy and exaggerated levels of anxiety and depression among individuals (Karim et al., 2020).
So, the less we know, the better? The answer might depend on the relevance of the information and how one filters, processes, and uses the information. As for social media usage, if there is one thing that could be inferred is that it might be better to use it mindfully, considerately select which accounts to follow, stop intervening in others’ lives, and start managing one’s matters by utilizing substantial, credible, and pertinent information. All information is valuable, but not every information is relevant.
References
Bányai, F., Zsila, Á., Király, O., Maraz, A., Elekes, Z., Griffiths, M. D., & Demetrovics, Z. (2017). Problematic social media use: Results from a large-scale nationally representative adolescent sample. PloS one, 12(1), e0169839.
Bawden, D., and Robinson, L. (2020). Information Overload: An Overview. In: Oxford Encyclopedia of Political Decision Making. Oxford: Oxford University Press.
Braghieri, L., Levy, R. E., & Makarin, A. (2022). Social media and mental health. American Economic Review, 112(11), 3660-3693.
Harriger, J. A., Thompson, J. K., & Tiggemann, M. (2023). TikTok, TikTok, the time is now: Future directions in social media and body image. Body Image, 44, 222-226.
Hong, H., & Kim, H. J. (2020). Antecedents and consequences of information overload in the COVID-19 pandemic. International journal of environmental research and public health, 17(24), 9305.
Karim, F., Oyewande, A. A., Abdalla, L. F., Chaudhry Ehsanullah, R., & Khan, S. (2020). Social Media Use and Its Connection to Mental Health: A Systematic Review. Cureus, 12(6), e8627.
Marciano, L., Ostroumova, M., Schulz, P. J., & Camerini, A. L. (2022). Digital media use and adolescents’ mental health during the COVID-19 pandemic: a systematic review and meta-analysis. Frontiers in public health, 9, 793868.
Naslund, J. A., Bondre, A., Torous, J., & Aschbrenner, K. A. (2020). Social media and mental health: benefits, risks, and opportunities for research and practice. Journal of technology in behavioral science, 5, 245-257.
Orman, L. V. (2016). Information Overload Paradox: Drowning in Information, Starving for Knowledge. Seattle, WA: Create Space Independent Publishing. Valkenburg, P. M. (2022). Social media use and well-being: What we know and what we need to know. Current Opinion in Psychology, 45, 101294.
A succinct summary of a certain period in photos and insightful quotes (according to the author’s opinion)
Individuals face a range of options each day. Some of them would result in actions, and some would be sacrificed, creating opportunity costs. Those actions also have outcomes and consequences that might change individuals’ paths. While people would act rationally and make considerations most of the time when choosing between options, there exist cases where they listen to their hearts even when the rational calculation seems convoluted and involve many aspects. At those moments, they could not mute the sounds in their heart. There are times when the options are hard to choose, and individuals might have to struggle with themselves before finally making up their minds. The challenge would be whether it is worth enough to choose the options and take the actions, considering the arising costs when taking the actions and the opportunity cost that one might lose due to selecting the option.
Commune with your own heart upon your bed, and be still.
– Psalm
It would be worth all of the time, opportunity costs, and pain, eventually. When you have your objectives that need the degree, have the passion for it, and have the seriousness, earnestness, and perseverance. It is indeed not easy, but it is rewarding.
– AK
It is worth it to keep on fighting and going all the way until it is done, no matter what.
– Tyler Joseph
In the midst of winter, I found there was, within me, an invincible summer
– Albert Camus
What you hear in your heart, let it echo this time, don’t suppress it.
What you’ve forged so far, don’t consider it worthless.
Only you know all your sacrifices for the things you love.
Ask yourself, how much you’re willing to change your life.
Various trials and things that make you doubt,
turn them into sparks to strengthen your determination.
– Hindia
In his article, Hamilton (2017) conveyed a critique of the Hodrick-Prescott (HP) filter, arguing against its utilization due to several inherent issues. He emphasizes that the filter’s sensitivity to the smoothing parameter selection, its tendency to generate misleading cycles, and its failure to accurately represent the actual underlying trend are significant drawbacks. While I partially concur with Hamilton’s viewpoint, it is essential to note that both filters offer distinct perspectives on the cyclical characteristics of the data, and it might be interesting to rethink certain aspects of Hamilton’s arguments.
Firstly, it is argued that the HP filter leads to a series of misleading dynamic associations, which lack a foundation in the underlying data-generating process. The underlying critique of the HP filter revolves around its tendency to impose dynamic patterns that are not connected to how the data is generated. While one might accept the notion of a random walk on a trend, relying solely on asymptotic statistics may not always yield definitive results. Nonetheless, it is worth noting that such series rarely emerge, allowing the HP-detrended series to demonstrate reliable forecasting capabilities (Dritsaki & Dritsaki, 2022).
Secondly, the critique argues that the HP filter yields significantly different filtered values at the end of the sample compared to the middle, leading to spurious dynamics. However, this bias may not be a concern when detrending targets specific business cycle events and is not used for real-time analysis or macroeconomic forecasts (Dritsaki & Dritsaki, 2022). Despite these shortcomings, the HP filter can still be useful with two adjustments, a lower smoothing parameter and rescaling of the extracted cyclical component (Wolf et al., 2020). However, this point of critique is relevant to the filter’s capability to create cycles where they do not exist. Previous studies (Nelson & Kang, 1981; Cogley & Nason, 1995) have shown that linear detrending of a random walk time series can induce spurious periodicity and complex dynamic properties in cyclical components that seemingly do not exist.
Thirdly, it is argued that when statistically formalizing the problem, the smoothing parameter (𝜆) values vastly differ from common practice. Hamilton contends that for quarterly data, a 𝜆 value below 1600 results in the last component of the trend-cycle decomposition being considered white noise. He proposes estimating 𝜆 using the maximum likelihood method and advocates setting the highest value for the 𝜆 coefficient to address excessive flexibility in the trend line. Nonetheless, Schuler (2018) demonstrates that the Hamilton regression filter possesses some drawbacks in common with the Hodrick-Prescott filter, such as the cancellation of two-year cycles and the amplification of longer cycles than typical business cycles, leading to inconsistencies with typical business cycle facts recognized by the National Bureau of Economic Research Studies (NBER) Business Cycle Dating Committee.
As an alternative to the Hodrick-Prescott (HP) filter, Hamilton proposes a robust detrending approach by regressing the variable at date t+h on the four most recent values as of date t. This approach is deemed superior to the HP filter as it avoids spurious dynamic relations and dynamics, providing more stable estimates of the underlying trend. Business cycle information could be extracted directly from time series using suitably selected forecasting OLS error from an autoregression model. Moreover, the approach offers greater flexibility by involving additional variables in the regression as necessary (Dritsaki & Dritsaki, 2022).
Nevertheless, the proposed alternative filter by Hamilton faces similar criticisms as the HP filter, including the presence of filter-induced dynamics in estimated cycles and the arbitrary nature of a key parameter choice (Moura, 2022). Moreover, the Hamilton approach’s estimated trends inherently lag the data, raising doubts about its claimed superiority over the HP filter in practice. Recent empirical research also shows that the HP filter outperforms Hamilton’s filter in dynamic forecasting, with significantly smaller cycle volatilities (Dritsaki and Dritsaki, 2022). The HP or Hamilton filter would inherently produce distinct estimates of the cyclical component. However, this issue becomes less significant when relating stationary economic models to non-stationary data, as comparisons between filtered real-world data and model-filtered series are feasible (Burnside, 1998).
In conclusion, both filters offer distinct perspectives on the cyclical properties of the data, with no clear superiority. As Hodrick (2020) proposes, future research could focus on developing simultaneous multivariate econometric models that apply filters for decomposing trends and cyclical components present in economic data, influencing the development of business cycles.
References
Burnside, C. (1998). Detrending and business cycle facts: A comment. Journal of Monetary Economics, 41(3), 513-532.
Cogley, T., & Nason, J. M. (1995). Effects of the Hodrick-Prescott filter on trend and difference stationary time series: Implications for business cycle research. Journal of Economic Dynamics and Control, 19(1-2): 253-278.
Dritsaki, M., & Dritsaki, C. (2022). Comparison of HP Filter and the Hamilton’s Regression. Mathematics, 10(8):1237.
Hamilton, J. (2017, June 22). Why you should never use the Hodrick-Prescott filter. Centre for Economic Policy Research. https://cepr.org/voxeu/columns/why-you-should-never-use-hodrick-prescott-filter.
Hamilton, J. (2018). Why you should never use the Hodrick-Prescott filter. Review of Economics and Statistics, 100(5), 831-843.
Hodrick, R. J. (2020). An Exploration of Trend-Cycle Decomposition Methodologies in Simulated Data. National Bureau of Economic Research, Working Paper No. 26750.
Moura, A. (2022). Why you should never use the Hodrick-Prescott filter: Comment. MPRA Paper, No.114922
Nelson, C., & Kang, H. (1981). Spurious Periodicity in Inappropriately Detrended Time Series. Econometrica, 49, 741–751.
Schuler, Y. S. (2018). On the Cyclical Properties of Hamilton’s Regression Filter. Deutsche Bundesbank Discussion Paper 03/2018.
Wolf, E., Mokinski, F., & Schüler, Y. (2020). On Adjusting the One-Sided Hodrick–Prescott Filter. Deutsche Bundesbank Discussion Paper No. 11/2020.
A discussion was running through my mind while I was waiting for the pick-up, thinking about a conversation with a friend lately. He mentioned being pragmatic in the context of marriage, meaning a marriage established not because of pure and true love, but rather due to other reasons, such as late age, matchmaking, etc. A couple of months ago, a friend of mine also talked about a romantic relationship that proceed to marriage because of the “easiness” factor, taking the example of a person that married his wife only after a relatively short time of dating and felt that the relationship was not complicated before deciding to get married. However, when one gets married because of other reasons instead of true love, this might raise an issue when a person appears in his life and find out he is in love with her, potentially making an affair or other relationship conflicts. Hence, the question might be whether it is worth waiting for the right person that we truly love or rather continuing our relationship in terms of marriage with another person that comes into our life that seems easy and not complicated even though we might not really love this person (assuming we aim to be in marriage sometime).
The root of the problem is not the waiting per se. It is the uncertainty of how long should we wait until we meet the right person. Just like waiting for the pick-up, we know that the pick-up will come eventually, but what time will it exactly arrive is still bewildering. A lot of reasons might make the pick-up comes late, maybe it is because of the traffic. Perhaps it is due to some technical issue, or probably it is simply not the time yet. This is a challenge to our patience in terms of waiting. We might think of choosing other transportation options as available. Nonetheless, it is only the pick-up that might be the best choice for many reasons. Should we wait? or should we make a trade-off with other transportation? There might even be another option: we could walk along our journey to the predetermined destination and take that pick-up along our way to the destination. However, we might arrive at our destination without taking the pick-up at all, or there might be many risks and challenges along the way that might be difficult for us to walk by ourselves so we need to take the pick-up.
One probability is that we might have met our significant others but have not realized it yet, or even have denied it. Should we wait, then? At this point, I guess so. There is time for us to make a move, and there is time to be patient and wait. It might be biased since I personally emphasize the value of passion and intimacy in a relationship, just like I value substantial and essential parts of a job. What is the point of doing something that we do not really enjoy only because of certain reasons? While sometimes we should be realistic and make a consideration, it might not be worth the time to do things that we do not really enjoy for most of our time. What is the point of being with someone for the rest of one’s life without having true love, passion, and intimacy with her? While there might be costs to this principle, for example, it might take time before meeting or finally getting into a serious relationship with a significant other that we truly love, not to mention the personal need of someone who could accompany us, and the social pressure, it might be worth the waiting.
As a closure, this waiting stuff reminds me of a conversation with my professor. I was asking whether it was worth it for her to pursue a doctoral degree with all of the time, opportunity costs, and “pain” as many people taking the degree said. And she said yes, it was worth it, eventually (of course, assuming we have our objectives that need the degree, have the passion for it, and have the seriousness, earnestness, and perseverance). It is indeed not easy, but it is requital.
In their article, Tenreyro and Thwaites (2013) conduct research to explore the impact of monetary policy on real and nominal variables at various business cycle stages. They aim to determine whether the effects of monetary policy are symmetrical or asymmetrical throughout the business cycle and identify the sources of any asymmetry observed. In my view, considering these objectives, using the impulse response functions (IRFs) in the study is quite persuasive since it is combined with other methods to complement its limitation in answering the study’s objectives.
The authors employ IRFs to analyze the dynamic response of a variable system to a specific shock. These functions capture the average effect of the shock on the system’s variables based on the state of the economy when the shock occurs, encompassing its impact on future changes. By utilizing IRFs, the authors estimate how real and nominal variables respond to monetary policy shocks, facilitating the examination of response magnitude and timing. Additionally, IRFs allow them to study variations in these responses between economic expansions and recessions.
Despite their usefulness, IRFs have some limitations. First is their assumption of linearity and exogenous shocks, which may not always hold in real-world economic systems. Their effectiveness depends on the underlying model used to generate them, and different models can yield varying IRFs. Non-linearities or structural breaks, which are significant characteristics of variable relationships, are not accounted for in IRFs, lacking a comprehensive depiction of variables’ interconnectedness. Additionally, in cases where the data-generating process cannot be adequately approximated by a vector autoregression (VAR) process, IRFs derived from the model may lead to biased and misleading results. Therefore, it is essential to consider these limitations when interpreting the implications of IRFs in economic analysis.
While IRFs are valuable for studying the dynamic impact of shocks on variable systems, they may not be the sole or optimal approach for answering the questions of the study. Hence, it is essential to complement them with other analytical tools. In this case, it is my view that Tenreyro and Thwaites have effectively utilized a combination of methods to extend the IRFs’ analysis. To complement IRFs, the authors incorporate the local projection method (Jordà, 2005), and combine it with the smooth transition regression method (Granger & Terasvirta, 1994). This adaptation allows IRFs to be influenced by the state of the business cycle, offering a more comprehensive perspective on the effects of shocks. Moreover, this combined methodology improves the estimation of shocks and reduces susceptibility to measurement errors.
The article clearly explains that the local projection method offers several advantages for studying the impact variation of shocks over time. One key advantage is their focus on the state of the economy at the time of the shock’s occurrence, lending flexibility to accommodate a panel structure and reducing sensitivity to misspecification. Furthermore, the combination method of smooth transition regression local projection model (STLPM) effectively handles non-linearities, which are weaknesses of IRFs, and estimates the impulse response of real and nominal variables to monetary policy shocks during different stages of the business cycle. In contrast, IRFs assume a constant state of the economy when the shock hits, which becomes problematic when dealing with shocks that lead to significant real effects. Estimating the transition between different economic regimes caused by the policy shock in a regime-switching VAR model involves numerous modeling choices that can be prone to errors and controversies. By employing a regime-switching local projection model, researchers avoid the need to make assumptions about how the economy transitions between regimes, which is particularly beneficial when studying the effects of fiscal consolidations or other impactful policy shocks.
Nevertheless, it should be noted that employing the local projection IRFs method tends to exhibit increased bias and variance. As a result, the confidence intervals for impulse responses are generally less accurate and wider on average compared to appropriately designed intervals based on VAR models (Kilian & Kim, 2009). Hence, in the case of a finite sample in the subsamples data in the analysis, it could hypothetically result in a wider confidence interval of IRFs. It means that using confidence intervals for IRFs in the study could shed more information about IRFs’ estimation accuracy.
Moreover, it is imperative to consider that the article’s analysis focused on the United States as an industrialized and developed country. As a result, the applicability and generalizability of the findings to emerging economies may raise some questions. Thus, future research could explore these aspects in more detail to understand how the effects of monetary policy may vary in different economic contexts.
References
Granger, C., & Terasvirta, T. (1994). Modelling nonlinear economic relationships. International Journal of Forecasting, 10(1):169–171.
Jordà, Ò. (2005). Estimation and Inference of Impulse Responses by Local Projections. American Economic Review, 95 (1): 161-182.
Kilan, L., & Kim, Y. J. (2009). Do Local Projections Solve the Bias Problem in Impulse Response Inference?. CEPR Discussion Paper Series 7266. Tenreyro, S., & Thwaites, G. (2013). Pushing on a string: US monetary policy is less powerful in recessions. CEP Discussion Paper 1218.
The autoregressive moving average (ARMA) is a common technique in time series analysis. In my view, a time series example that can be suitable with the ARMA model but need to be revisited is inflation. The model commonly used in inflation data modeling might associate with the relatedness of the variable with its past values. The ARMA model can be used to forecast inflation, and its simple structure that only requires current and past inflation data might offer several advantages. For example, the ARMA (p, q) model forecasts inflation at a given period by linearly projecting the inflation from the previous period to the period in question (autoregressive or AR part) and incorporating white noise from the current period to a certain number of periods back (moving average or MA part). Moreover, the ARMA model used for short-term forecasting tends to perform better on average compared to medium-term forecasting (Stovicek, 2007).
However, there are some considerations before applying the ARMA model to the inflation case. First, determining the model specification can be challenging since the ARMA model might lack a theoretical foundation. Although economic theory suggests that factors like money supply, nominal appreciation, and output gaps influence inflation, the ARMA model might not explicitly incorporate these insights. Moreover, one study found that the US CPI inflation is effectively represented by an unobserved components model with time-varying volatility in both the transitory and trend equations, implying the need to update the ARMA framework to incorporate a time-varying second moment (Stock and Watson, 2007).
Second, it is worth noting that ARMA can only be applied if the stationarity assumption holds. In the absence of stationarity in inflation data, it may be more appropriate to choose ARIMA to induce stationarity. Third, in cases where inflation occurs seasonally due to special events like Christmas or other holidays, other models might outperform ARMA. For example, previous literature suggests that SARIMA could be a better choice in such scenarios (Davidescu et al., 2021). SARIMA models offer advantages over ARIMA models when dealing with data that exhibits strong seasonal patterns, such as higher prices that can be expected during certain months due to holidays or seasonal demand. SARIMA models can capture this effect and adjust the forecasts accordingly, and can also handle multiple seasonal cycles, such as weekly, monthly, and yearly patterns.
Fourth, monetary policy interventions can also influence inflation, leading to structural shocks or breaks. It might be pivotal to consider the sample period and subset the data if necessary to ensure there are no obvious structural breaks, particularly in the case of developed economies. Inflation in advanced economies might be primarily determined by the monetary policy stance of the central bank, such as the Federal Reserve in the case of the US. Additionally, a shift in the monetary policy regime during the sample period might also indicate structural breaks in the data.
Lastly, as central banks target a specific inflation rate, an inflation series should be stationary with a long-run mean centered at the target rate. In most cases, inflation series are highly persistent due to economic reasons. Therefore, before applying Box-Jenkins forecasting techniques, inflation series are typically differenced again. In other words, forecasting is usually conducted for inflation growth rather than inflation levels. To summarize, it might be beneficial to fit various models and compare them using appropriate model selection criteria to determine the best model for inflation forecasting purposes.
References
Davidescu, A. A., Apostu, S. A., & Stoica, L. A. (2021). Socioeconomic effects of COVID-19 pandemic: exploring uncertainty in the forecast of the Romanian unemployment rate for the period 2020–2023. Sustainability, 13(13), 7078.
Stock, J. H., & Watson, M. W. (2007). Why has US inflation become harder to forecast?. Journal of Money, Credit and Banking, 39, 3-33.
Stoviček, K. (2007). Forecasting with ARMA Models: The case of Slovenian inflation. Bank of Slovenia.
In their paper, Coibion and Gorodnichenko (2011) argue that in the absence of additional significant economic shocks, the monetary policy reversal is likely to be gradual and provide robust evidence that policy inertia is a more likely source of the persistence in interest rates than the persistent shocks hypothesis. The author mostly agrees with their arguments for some reasons.
First, using the Taylor rule as an analytical framework is appropriate for modeling the endogenous response of monetary policymakers to economic fluctuations. Coibion and Gorodnichenko then provide a novelty and account for significant factors that affect the decision-making process by extending the classic Taylor rule to incorporate both the output gap and the output growth rate. They apply formulas that incorporate interest rate smoothing to the Taylor rule. By doing so, they find high levels of interest smoothing, indicating the presence of policy inertia and suggesting that interest rate adjustments occur gradually over time. Moreover, to explore the possibility of persistent shocks contributing to serial correlation, they assume that the errors in the baseline formula, the Taylor rule, are serially correlated. They compare the fitted values of the Taylor rule under both the policy inertia and persistent shocks interpretations and find that the fitted values for the two interpretations are essentially indistinguishable, indicating that the observed serial correlation can be attributed to policy inertia rather than persistent shocks.
Second, from a technical point of view, Coibion and Gorodnichenko (2011) address the issue of serial correlation in the error terms of the estimated Taylor rule which can lead to an overestimation of the degree of policy inertia. Hence, they provide rigorous evidence using various methods back and validate their findings and arguments. For instance, one important finding is the presence of significant policy inertia, characterized by interest rate smoothing and gradual adjustments in response to economic conditions. This evidence suggests that historical policy changes can be accounted for by interest smoothing to a significant extent, reducing the level of serial correlation in the residuals. Additionally, they explore the response of monetary policy to expected output growth, finding that adjusting for the response to expected output growth in the next quarter leads to more accurate estimates of the persistence of monetary policy shocks. These findings contribute to a better understanding of the determinants of interest rate dynamics and provide valuable insights into the behavior of the central bank.
Third, they argue that central bankers are inclined to adjust interest rates gradually and incrementally, moving them closer to their desired levels through a series of steps rather than making immediate changes as suggested by the baseline Taylor rule. This is also reaching close to the policy-making in practice where central banks typically adjust interest rates on a gradual basis. For example, the interest rate set by the central bank of Indonesia in September 2022 was 4.25%. The interest rate then gradually rose to the level of 4.75% in October 2022, 5.25% in November 2022, 5.50% in December 2022, and 5.75% in January to date (as of June 2023). The central bank made these adjustments as a response to global economic conditions and the rise in the U.S. interest rates. Finally, their points have also considered alternative factors, such as financial market variables and real-time forecast revisions, reflecting sound econometric methodology. By examining their impact on interest rate persistence and finding limited significance, the researchers demonstrate the robustness of their analysis and strengthen the case for policy inertia.
Lastly, in the author’s view, the article suggests that monetary policy can be both forward-looking and backward-looking and that the degree of policy inertia can depend on the specific formulation and the degree of interest rate smoothing in the central bank’s reaction function. If the central bank is highly responsive to past deviations of inflation from its target, then it may be slow to adjust its policy rate in response to new information about the economy, and it can be considered backward-looking. Conversely, if the central bank is more responsive to expected future deviations of inflation from its target, then it may be slow to adjust its policy rate in response to changes in the current economic environment, and it can be considered forward-looking.
References
Coibion, O., & Gorodnichenko, Y. (2011). Why are target interest rate changes so persistent?. NBER Working Papers 16707
Gorodnichenko, Y., & Coibion, O. (2011, January 28). How inertial is monetary policy? implications for the Fed’s exit strategy. CEPR. https://cepr.org/voxeu/columns/how-inertial-monetary-policy-implications-feds-exit-strategy
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