Charts on residential and nonresidential construction presented to the Associated General Contractors of America (AGC) Financial Issues Forum June 19, 2014
I try my best to define this topic as much easy as possible. This is actually a basic research may be contradictory even Hope others can also get benefit form my work.
Charts on residential and nonresidential construction presented to the Associated General Contractors of America (AGC) Financial Issues Forum June 19, 2014
I try my best to define this topic as much easy as possible. This is actually a basic research may be contradictory even Hope others can also get benefit form my work.
This analysis focuses on measures much beyond PE ratios. And, it concludes that the Stock Market is actually really cheap vs. bonds. But, it appears quite overvalued when focusing on inflation measures.
Can Treasury Inflation Protected Securities predict Inflation?Gaetan Lion
We look at the spread between Treasuries and TIPS to figure out how effective such observations were in predicting actual inflation several years down the road.
Through this slide I try hard to explain it in as simple as possible, so you guys easily understand what IL-SM curve is & its derivation graphically & mathematically, and I hope you guys no need to open you books after you go through with it.
http://pwc.to/1cpYR81
En octobre, les décideurs de partout dans le monde se sont réunis à Washington DC pour faire le bilan des perspectives économiques mondiales. Pour la première fois depuis 2010, le pronostic d’une reprise soutenue pour les économies développées devrait être positif.
This analysis focuses on measures much beyond PE ratios. And, it concludes that the Stock Market is actually really cheap vs. bonds. But, it appears quite overvalued when focusing on inflation measures.
Can Treasury Inflation Protected Securities predict Inflation?Gaetan Lion
We look at the spread between Treasuries and TIPS to figure out how effective such observations were in predicting actual inflation several years down the road.
Through this slide I try hard to explain it in as simple as possible, so you guys easily understand what IL-SM curve is & its derivation graphically & mathematically, and I hope you guys no need to open you books after you go through with it.
http://pwc.to/1cpYR81
En octobre, les décideurs de partout dans le monde se sont réunis à Washington DC pour faire le bilan des perspectives économiques mondiales. Pour la première fois depuis 2010, le pronostic d’une reprise soutenue pour les économies développées devrait être positif.
In this note I address the issue of where we are in the US business cycle and what comes next.
My bottom line is that the pieces are falling into place for a (mild) recession sometime in the middle of 2020.
The approach that I take is to line up the current expansion (which this month became the second longest ever) with the 7 other post-1960 expansions.
Module 2 Lecture - GDP A Measure Of Total Production And Income (.docxkendalfarrier
Module 2 Lecture - GDP: A Measure Of Total Production And Income (cont'd)-2
Using Real GDP To Measure Our Standard of Living
A common method for measuring the standard of living in the US involves comparing per capita Real GDP (that’ total gross domestic product divided by the population) from the present to some pre-determined past year. Table 14.4, pg. 383 shows the per person Real GDP from 2016 as compared to the per person Real GDP in 1960. From looking at that table it appears the US has done very well, increasing from $17,217 per person in 1960 to $56,200 per person in 2016. This isn’t the whole story, however.
Potential GDP
Potential GDP is the level of real GDP per person when all factors of production (land, labor, capital, entrepreneurship) have been utilized to their fullest. This would be the productivity goal of the US economy. If some of these factors are underutilized or underemployed, real GDP is producing below its potential. Think of the loss of productivity with high rates of unemployment or large tracts of land that are used only occasionally (football and baseball stadiums or public schools which lie vacant during 3 months of vacations). The only way to get a true look at how well the US economy is doing is to look at its production levels in past years and compare them with the full potential of production of which the US economy is capable. Figure 14.3, pg. 384 which I’ve reproduced for you below, gives us a pretty clear picture of how well the US has achieved its production potential since 1960.
Follow the red line upward from 1960 and especially note how much it lies above or below the black line. The red line represents Real GDP per person. The black line represents the actual potential of productive output per person.
Note that the red line rises above potential from 1960 to about 1970. The potential productive output per person was actually rising at about 2.8% per year.
Since 1970 the red line is either on or well below the black line (except for two minor rises). Real GDP per person has fallen below the economy’s potential. It actually shows a growth rate of only about 1.9% a year since 1970. This means that the economy’s POTENTIAL GDP has fallen well below that of 1960.
If the economy had kept up with the 1960’s rate of growth in production it would mean a cumulative effect (from 1960 to 2013) of about $406,000 added income/productivity per person. This is one of the more compelling reasons behind the emphasis on not just per capita (per person) Real GDP but also its comparison to POTENTIAL GDP. Wouldn’t it have been nice to have an extra $400,000 over a span of 40 years (1970-2010)? That’s an extra $10,000 a year; serious added income.
Why Does Real GDP (levels of production and income) Increase Positively Some Years And Then Become Negative In Other Years?
The Business Cycle: this is a term used to describe fluctuations in economic activity. In a sense, it measures the economic growth during the expa.
Sacramento's population projections for the State of California are already 1.4 million too high only 3 years into the forecast by 2023. The reason is Sacramento's unrealistic migration assumption. This analysis tests in detail how and why this projection went so wrong.
This study analyzes the temperature history of 24 American cities going back to 1895. Using a LOESS model, it forecasts prospective temperature increases over the next 40 years and out to 2100. And, it compares the 2100 forecast with the NOAA model(s). This comparison uncovers serious deficiencies within the NOAA model(s), as it does not fit the historical data well; and it does not differentiate much forecasts between various cities.
Compact Letter Display (CLD). How it worksGaetan Lion
Compact Letter Display (CLD) renders ANOVA & Tukey HSD testing a lot easier to interpret. It readily ranks and differentiate the tested variables. With CLD you can readily identify the variables that are statistically dissimilar vs. the ones that are similar.
This study compares the benefits and the funding for CalPERS pensions vs. Social Security. It also looks in more detail on the financial burden of CalPERS pensions on the Marin Municipal Water District.
This presentation includes two explanatory models to attempt to predict recessions. The first one is a logistic regression. The second one is a deep neural network (DNN). Both use the same set of independent variables: the velocity of money, inflation, the yield curve, and the stock market. As usual, the DNN fits the historical data a bit better than the simpler logistic regression. But, when it comes to testing or predicting, both models are pretty much even.
Objective:
Studying trends in US inequality along several social dimensions including education, ethnicity, percentiles, and work status. We don’t explore gender because it is not disaggregated within the mentioned data that focuses on families (fairly similar to households).
Data source:
US Government Survey of Consumer Finance (SCF) data. The SCF aggregates financial data on US families every three years. And, it discloses a time series from 1989 to 2019.
The model development two objectives are:
1) To explain home prices using demographic explanatory variables; and
2) To benchmark the accuracy of OLS regressions vs. DNN models.
For home prices, we used county level data from Zillow. For the explanatory variables, we used data from GEOFRED.
This analysis focuses on population aging, population age categories in % (age pyramids), and overall population growth. It looks at various geographic units (countries, continents, regions, World) from 1950 to the Present (2019 & 2020). And, it looks at projections out to 2100.
Africa is an outlier to the overall global aging; its population growth (historical & projected) is far faster than for other major regions.
We are going to analyze several of the major cryptocurrencies as an asset class. And, we are going to address several related questions:
Do they provide diversification benefits relative to the stock market (S&P 500)?
How do their diversification benefits compare with Gold’s diversification benefit vs. the stock market?
Do cryptocurrencies provide diversification benefits when you really need it… during market downturns?
Are cryptocurrencies truly “digital Gold”? Do they behave in a similar way given that their supply is constrained (supposedly in a similar way as Gold is)?
We will test whether :
a) Sequential Deep Neural Networks (DNNs) can predict the stock market (S&P 500) better than OLS regression;
b) DNNs using smooth Rectified Linear activation functions perform better than the ones using Sigmoid (Logit) activation functions.
The relationship between the Stock Market and Interest RatesGaetan Lion
This is a study of the relationship between the Stock Market and Interest Rates. We review how the Stock Market has reacted when interest rates rise. We also factor the influence of other macroeconomics variables.
This is a study using historical data and forecasts of life expectancy for several countries. The data and forecasts come from the UN - Population Division. While the historical data is most interesting, the forecasts are highly optimistic as they project a linear trend way into the future. Meanwhile, those forecasts should have followed a much more realistic logarithmic curve reflecting slower increase in life expectancy as the life expectancy rises.
Will Stock Markets survive in 200 years?Gaetan Lion
This study uncovers 11 international stock markets that are already running into existing and prospective demographic and economic growth constraints. This study evaluates their respective fragile long term viability and the implications this has for the investors in such countries.
This study answers three questions:
1) Does it make a difference whether you standardize your variables before running your model or standardize the regression coefficients after you run your model?
2) Does the scale of the respective original non-standardized variables affect the resulting standardized coefficients?
3) Does using non-standardized variables vs. standardized variables have an impact when conducting regularization (Ridge Regression, LASSO)?
This analysis compares his track record vs. Manning, Montana, Marino, Brees, Favre, and Elway. At the end of this analysis, it makes extensive use of the binomial distribution to figure out how much of their respective track records are due to randomness vs. skills.
Regularization why you should avoid themGaetan Lion
Regularization models are supposed to reduce model over-fitting and improve forecasting accuracy. Very often they do just the opposite: increase model under-fitting, and decrease model forecasting accuracy. This study explains how Regularization models often fail, and how to resolve model issues with far simpler and more robust methods.
This study reviews the increasing prevalence of 3-shot points within the NBA. It also compares the record of the 5 top players in NBA history in 3-pt shots. It also considers how many good years left Curry may have.
Japan vs. US comparison on numerous dimensionsGaetan Lion
This study compares Japan vs. the US on numerous dimensions including demographics (including health and education), and economics (including monetary and fiscal policies). This is to observe when Japan and the US trends are likely to converge over time.
Climate change model forecast global temperature out to 2100Gaetan Lion
This study is leveraging a VAR model introduced in an earlier presentation to forecast global temperature out to 2100, and assess how likely are we to keep such temperatures at or under the + 1.5 degree Celsius threshold.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
6. Unemployment rate drops in first quarter of expansion The two lines are very much parallel. But, there is a large near constant difference between the two.
7. Unemployment rate drops in second quarter of expansion In the 1973+ period, the unemployment rate remained much higher throughout the next two years of the expansion than during the last four quarters of the recession in the 1953+ and 1956+ periods.
8. Unemployment rate keeps on rising for a while… See how abruptly unemployment has risen in the most recent 2008 recession. And, it has kept on rising during the first two quarters of the current expansion.
9. GDP (quarterly annualized rates) The yellow line denotes the first quarter of the expansion (E1). The green cells indicate intermittent GDP growth embedded within recessionary periods. The red cells indicate when GDP growth briefly fell below 0% during the expansion period. The three indicators at the bottom indicate the recession length in # of quarters, the average change in GDP during the recessionary period and during the expansion.
10. Regression Model to forecast the current expansion The dependent variable is the average GDP growth rate during the eight quarters of the expansion. We ran a stepwise regression using many variables. The optimal regression model used just three independent variables and two dummy variables. They are: X1 the recession length in quarters, X2 GDP in first quarter of expansion (GDP E1), X3 average unemployment in first two quarters of expansion (Avg. U E1E2). The dummy variables are X4 recessions that occurred after 1970 and X5 recessions that occurred before 1950. The model forecasts an average GDP growth of 5.1% for the eight quarters of the current expansion. This is high for a post 1970 expansion.
11. Regression Statistics & Parameters See how the average unemployment in the first two quarters of the expansion (Avg. U E1E2) has a strong positive regression coefficient. That is probably a combination of the V shape recovery and the unemployment lag phenomena. This model fit was excellent with an R Square close to 1 and a standard error of 0.2%. We could see that on the previous slide.
12.
13. Appendix: Data on the relevant recession and expansion periods with analytical comments reflecting the economic performance of the time
14. 1948 an Outlier with a spectacular recovery Green zone: unemployment rate in the last quarter before the recession started. Blue zone: unemployment rate during the recession. Yellow zone: unemployment rate during the first eight quarters of the recovery or expansion. Red zone: recession period showing quarterly GDP on annualized basis. Note the recession period includes quarters of weak growth (not just negative) and it includes quarters of strong growth intertwined between quarters of negative growth (double dip situation). We are showing change in annual CPI as a matter of curiosity as we did not use this variable in our models. CPI measures had very low correlation with GDP growth.
15. 1953 & 1956 a true double dip… The 1953+ ensuing expansion lasted only six quarters before it dovetail into the 1956 recession.
16. 1960 & 1969 early jobless recoveries In both cases, unemployment remained moderate during the recession and rather high during the expansion vs what it was during the recession.
17. 1973 & 1980 Stagflation periods Same observation regarding unemployment as on the previous slide. Those two periods experienced stagflation related to two major oil shocks when OPEC rose oil prices by a high multiple. See the very high inflation rates during the recessions.
18. 1990 & 2000 The U shaped recoveries The recessions were short and mild. But, the ensuing recoveries were also mild meaning weak.
19. 2008 will it be a V, U, or L recovery? The data reflects what was available at the time of this analysis. The recession was certainly severe enough to justify a V shape recovery with strong growth. And, the regression model suggests that too. However, if unemployment remains high and if the CPI remains flat for a few more quarters we may have an L situation (severe recession with a weak recovery).