hope.

The Capital Asset Pricing Model: Theory and Evidence

The Capital Asset Pricing Model (CAPM) is based on the assumptions of complete agreement regarding return distributions and risk-free borrowing and lending, implying that all investors perceive the same investment opportunities and maintain similar portfolios of risky assets, but the allocation of risk-free assets could vary depending on the risk tolerance of each investor. They also combine these portfolios with risk-free borrowing or lending. Consequently, the expected return on assets uncorrelated with the market’s return should be equivalent to the risk-free rate, meaning that an asset’s expected excess return should equal the sum of the risk-free asset return and the product of its market risk (beta) and the market risk premium. The CAPM predicts a linear relationship between a portfolio’s expected return and market beta. Moreover, for the asset market to reach equilibrium, the market portfolio must be the minimum variance portfolio, and therefore, the market portfolio would be mean-variance efficient.

Early empirical studies refute the Sharpe-Lintner version of the Capital Asset Pricing Model, indicating a positive but too-flat relationship between beta and average returns (Black et al., 1972). The risk-free rate surpassed the average risk-free rate, and the coefficient on beta was lower than the average excess market return. Early investigations involving cross-sectional analysis (Fama & MacBeth, 1973) and time series regression tests (Gibbons, 1982) of the CAPM implied that standard market proxies tend to lie along the minimum variance frontier, meaning that the model’s prediction that suggests the premium per unit of beta should be the expected market return minus the risk-free interest rate was consistently rejected. More recent evidence indicated that a substantial portion of the expected return variation is unrelated to market beta. For instance, high debt-equity ratios led to returns that were disproportionately high compared to their market betas (Bhandari, 1988). Meanwhile, stocks with high book-to-market equity ratios exhibited high average returns not fully explained by their betas (Rosenberg et al., 1985). As time progressed beyond the early empirical works on the CAPM, the relationship between average return and beta for common stocks became even flatter (Lakonishok & Shapiro, 1986). On the other hand, factors including size, earnings-price, debt-equity ratios, and book-to-market ratios have been shown to contribute to the explanation of expected stock returns alongside market beta (Fama & French, 1992). Different price ratios, driven by a common factor in prices, offered similar insights into expected returns (Fama & French, 1996).

Several potential reasons could explain the disparity between the predictions of the CAPM and the empirical evidence. Firstly, from a behavioral perspective, when sorting firms based on book-to-market ratios, investors tend to overreact to favorable and adverse situations and extrapolate past performance. This overreaction was evident in stock prices that became overly inflated for growth firms and excessively deflated for distressed firms (DeBondt & Thaler, 1987; Lakonishok et al., 1994). Secondly, the CAPM, as an asset pricing model, is overly simplistic and relies on numerous unrealistic assumptions. For instance, the model assumes that investors only consider the mean and variance of one-period portfolio returns, which is a nontrivial simplification. It fails to account for the various factors influencing investors, resulting in market beta that cannot fully capture an asset’s risk, and it falls short of explaining variations in expected returns. Thirdly, there is no clear theoretical consensus on which assets could be exempt from the market portfolio. On the other side, data availability also often limits the assets that could be included, forcing the use of proxies for the market portfolio, which is argued to invalidate tests of the CAPM.

Overall, the article provides a comprehensive discussion of the CAPM, which might be a convenient theory for an early understanding of portfolio theory and asset pricing. Nevertheless, its empirical challenges might make it unsuitable for practical applications.

References
Bhandari, L. C. (1988). Debt/equity ratio and expected common stock returns: Empirical evidence. The journal of finance, 43(2), 507-528.

Black, F., Jensen, M.C., & Myron Scholes. (1972). The Capital Asset Pricing Model: Some Empirical Tests, in Studies in the Theory of Capital Markets. Michael C. Jensen, ed. New York: Praeger, pp. 79–121.

De Bondt, W. F., & Thaler, R. H. (1987). Further evidence on investor overreaction and stock market seasonality. The Journal of Finance, 42(3), 557-581.

Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607-636.

Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. The journal of finance, 51(1), 55-84.

Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. the Journal of Finance, 47(2), 427-465.

Gibbons, M. R. (1982). Multivariate tests of financial models: A new approach. Journal of financial economics, 10(1), 3-27.

Lakonishok, J., & Shapiro, A. C. (1986). Systematic risk, total risk and size as determinants of stock market returns. Journal of Banking & Finance, 10(1), 115-132.

Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. The journal of finance, 49(5), 1541-1578.

Rosenberg, B., Reid, K. and Lanstein, R. (1985) Persuasive Evidence of Market Inefficiency. Journal of Portfolio Management, 11, 9-17.

Embracing The Process

Each individual might have a unique journey and experience to reach a certain level of understanding. In research, a question is raised and will be answered through the appropriate methodology to conclude. Analogously, we might have our questions about particular things that will eventually be revealed, but the process to get to the answers might be different. Why do we need to experience the process which is often painful? This post aims to discuss the meaning of real love based on personal judgment, experience, and intuition, and to explore the importance of getting through the process to infer that meaning.

To begin, the initial question raised was, when an individual is in a relationship, how does one know that his partner in the relationship is the real love with whom he will be in a long-term commitment? While much information can answer the question, it is not until one experiences it himself that he can grasp a full comprehension. The process to obtain the answer involves a substantial amount of time, moments, costs, etc. Several points are worth noting after going through some time in a relationship with someone who is seemingly real love.

The first is regarding the meaning of true love. From the perspective of the person in the relationship, there might not be such a sacrifice when we truly love someone. In other words, we would do anything for this person no matter what (of course, positively and constructively). We do not perceive this manner as a cost or burden for us since this person is basically ourselves, our life, our priority, our “the one that we would not want to lose at all costs”. We are willing to act, to give, and are definitely going to do anything, even if there might be obstacles and challenges. Because her happiness is my happiness, and she is my life. I love her as I love myself. In a broader context, this might also be one of the reasons parents will always provide the best for their offspring, as well as when one is passionate about his pursuit in life.

Second, depending on one’s belief, the observable indicator that someone is our real love is when this person could encourage or lead us to do better in our important aspects. For example, for someone who values spirituality, his true love might be someone who can ‘nudge’ him to be a better version of himself and to be closer to what is expected based on his belief instead of being stuck at some point. Moreover, the one who is for us might be someone who can align with our thoughts and fundamental principles. For instance, if one holds the principle of honesty and integrity, he might not want a partner who is not true to herself, talks inconsistently with her actions, or tells lies to her significant others, such as her family. In the end, what we need is companionship. Someone who can help us, let us help her, someone we can share anything and take good advice from, someone we can truly love.

Third, a process in any context and way might be painful, but it is worth the pain. One good fact to be inferred a lesson from is the process of an eagle. While an eagle might live up to 70 years, it has to get through a painful process after the first 40 years of its lifetime. In its 40th year, an eagle is left with only two options: die or go through a painful process of change that lasts for a certain period (5 to 6 months). The process requires that the eagle fly to a mountaintop and sit alone in its nest, knocking its beak against a rock until it plucks it out. Then, the eagle will wait for a new beak to grow back before it plucks out its talons. Once its new talons grow back, the eagle starts plucking its old, aged feathers. After 5 to 6 months, the eagle takes its flight of rebirth and lives for 30 more years. It has to undergo a painful and durable process to survive and rebirth, but the process is worth the longevity.

Fourth, it might be hard to see and think objectively during a struggling process, and thus, we might need to hold on. To do so, we need something for our cornerstones, which are our commitment to fundamental values and principles. During a relationship that might not be the one for us or after a break up from a relationship, it may be difficult for oneself to take a clear sight since we have a short and finite horizon. There might be temptations to compromise, damaging the commitment to our values. Nevertheless, it would be pointless to trade off our fundamentals with lust during our ignorance. While there are things we could and might need to compromise, our values are none of them. We can observe a person not from his words, but his actions and decisions. Holding on to core values might be a guide leading us to comprehend and accept the process at some point. We need to embrace the process that can be painful because it would make us deeply comprehend a meaning, and recognize and truly appreciate the reward we obtain resulting from the process. In a relationship context, one can understand himself, the true objective of a relationship, and find his real love.

To conclude, there might be phases in understanding the meaning of love and a relationship and the comprehension could still develop over time. The process is represented in the journey that points toward worthy things ahead. So, let our hearts hold fast because this too shall pass.

Dissolution-Resolution

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.

On Social Media and Fake News in the 2016 Election

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.

On Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias

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.

The Less We Know, The Better?

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.

The Messenger (Part 6)

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

A Comment on Why You Should Never Use the Hodrick-Prescott Filter

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.

Is It Worth the Waiting?

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.

Commentary on Monetary policy is Weaker in Recessions

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.