Federal Reserve Economic Data

OECD Data Filters: REF_AREA: CAN MEASURE: CC UNIT_MEASURE: XDC_USD ACTIVITY: _Z ADJUSTMENT: _Z TRANSFORMATION: _Z FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

The Figure For January, 1926 Is The Per Share Value Weighted By Outstanding Shares. Source: F.R. Macaulay, "Some Theoretical Problems Suggested By The Movements Of Interest Rates, Bond Yields, And Stock Prices In The United States Since 1856 (NBER, 1938), Pp.A142-A161; F.R. Macaulay, "The Smoothing Of Time Series, " (NBER, 1931). This NBER data series m11005a appears on the NBER website in Chapter 11 at http://www.nber.org/databases/macrohistory/contents/chapter11.html. NBER Indicator: m11005a

OECD Data Filters: REF_AREA: CHN MEASURE: CCRE UNIT_MEASURE: IX ACTIVITY: _Z ADJUSTMENT: _Z TRANSFORMATION: _Z TIME_HORIZ: _Z METHODOLOGY: N FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

OECD Descriptor ID: CPGRLE01 OECD unit ID: PC OECD country ID: CAN All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

Observations for the current and future years are projections. The IMF provides these series as part of their Regional Economic Outlook (REO) reports. These reports discuss recent economic developments and prospects for countries in various regions. They also address economic policy developments that have affected economic performance in their regions and provide country-specific data and analysis. For more information, please see the Regional Economic Outlook (https://www.imf.org/en/publications/reo) publications. Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available here (http://www.imf.org/external/terms.htm).

OECD Data Filters: REF_AREA: NZL MEASURE: UNE_LF UNIT_MEASURE: PT_LF_SUB TRANSFORMATION: _Z ADJUSTMENT: Y SEX: _T AGE: Y_GE15 ACTIVITY: _Z FREQ: Q All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

This series was constructed by the Bank of England as part of the Three Centuries of Macroeconomic Data project by combining data from a number of academic and official sources. For more information, please refer to the Three Centuries spreadsheet at https://www.bankofengland.co.uk/statistics/research-datasets. Users are advised to check the underlying assumptions behind this series in the relevant worksheets of the spreadsheet. In many cases alternative assumptions might be appropriate. Users are permitted to reproduce this series in their own work as it represents Bank calculations and manipulations of underlying series that are the copyright of the Bank of England provided that underlying sources are cited appropriately. For appropriate citation please see the Three Centuries spreadsheet for guidance and a list of the underlying sources.

This time series is an interpretation of Organisation of Economic Development (OECD) Composite Leading Indicators: Reference Turning Points and Component Series data, which can be found at http://www.oecd.org/std/leading-indicators/oecdcompositeleadingindicatorsreferenceturningpointsandcomponentseries.htm. The OECD identifies months of turning points without designating a date within the month that turning points occurred. The dummy variable adopts an arbitrary convention that the turning point occurred at a specific date within the month. The arbitrary convention does not reflect any judgment on this issue by the OECD. Our time series is composed of dummy variables that represent periods of expansion and recession. A value of 1 is a recessionary period, while a value of 0 is an expansionary period. For this time series, the recession begins on the 15th day of the month of the peak and ends on the 15th day of the month of the trough. This time series is a disaggregation of the monthly series. For more options on recession shading, see the note and links below. The recession shading data that we provide initially comes from the source as a list of dates that are either an economic peak or trough. We interpret dates into recession shading data using one of three arbitrary methods. All of our recession shading data is available using all three interpretations. The period between a peak and trough is always shaded as a recession. The peak and trough are collectively extrema. Depending on the application, the extrema, both individually and collectively, may be included in the recession period in whole or in part. In situations where a portion of a period is included in the recession, the whole period is deemed to be included in the recession period. The first interpretation, known as the midpoint method, is to show a recession from the midpoint of the peak through the midpoint of the trough for monthly and quarterly data. For daily data, the recession begins on the 15th of the month of the peak and ends on the 15th of the month of the trough. Daily data is a disaggregation of monthly data. For monthly and quarterly data, the entire peak and trough periods are included in the recession shading. This method shows the maximum number of periods as a recession for monthly and quarterly data. The Federal Reserve Bank of St. Louis uses this method in its own publications. The midpoint method is used for this series. The second interpretation, known as the trough method, is to show a recession from the period following the peak through the trough (i.e. the peak is not included in the recession shading, but the trough is). For daily data, the recession begins on the first day of the first month following the peak and ends on the last day of the month of the trough. Daily data is a disaggregation of monthly data. The trough method is used when displaying data on FRED graphs. A version of this time series represented using the trough method can be found at: https://fred.stlouisfed.org/series/GBRRECD The third interpretation, known as the peak method, is to show a recession from the period of the peak to the trough (i.e. the peak is included in the recession shading, but the trough is not). For daily data, the recession begins on the first day of the month of the peak and ends on the last day of the month preceding the trough. Daily data is a disaggregation of monthly data. A version of this time series represented using the peak method can be found at: https://fred.stlouisfed.org/series/GBRRECDP The OECD CLI system is based on the "growth cycle" approach, where business cycles and turning points are measured and identified in the deviation-from-trend series. The main reference series used in the OECD CLI system for the majority of countries is industrial production (IIP) covering all industry sectors excluding construction. This series is used because of its cyclical sensitivity and monthly availability, while the broad based Gross Domestic Product (GDP) is used to supplement the IIP series for identification of the final reference turning points in the growth cycle. Zones aggregates of the CLIs and the reference series are calculated as weighted averages of the corresponding zone member series (i.e. CLIs and IIPs). Up to December 2008 the turning points chronologies shown for regional/zone area aggregates or individual countries are determined by the rules established by the National Bureau of Economic Research (NBER) in the United States, which have been formalized and incorporated in a computer routine (Bry and Boschan) and included in the Phase-Average Trend (PAT) de-trending procedure. Starting from December 2008 the turning point detection algorithm is decoupled from the de-trending procedure, and is a simplified version of the original Bry and Boschan routine. (The routine parses local minima and maxima in the cycle series and applies censor rules to guarantee alternating peaks and troughs, as well as phase and cycle length constraints.) The components of the CLI are time series which exhibit leading relationship with the reference series (IIP) at turning points. Country CLIs are compiled by combining de-trended smoothed and normalized components. The component series for each country are selected based on various criteria such as economic significance; cyclical behavior; data quality; timeliness and availability. OECD data should be cited as follows: OECD Composite Leading Indicators, "Composite Leading Indicators: Reference Turning Points and Component Series", http://www.oecd.org/std/leading-indicators/oecdcompositeleadingindicatorsreferenceturningpointsandcomponentseries.htm (Accessed on date)

This time series is an interpretation of Organisation of Economic Development (OECD) Composite Leading Indicators: Reference Turning Points and Component Series data, which can be found at http://www.oecd.org/std/leading-indicators/oecdcompositeleadingindicatorsreferenceturningpointsandcomponentseries.htm. The OECD identifies months of turning points without designating a date within the month that turning points occurred. The dummy variable adopts an arbitrary convention that the turning point occurred at a specific date within the month. The arbitrary convention does not reflect any judgment on this issue by the OECD. Our time series is composed of dummy variables that represent periods of expansion and recession. A value of 1 is a recessionary period, while a value of 0 is an expansionary period. For this time series, the recession begins on the 15th day of the month of the peak and ends on the 15th day of the month of the trough. This time series is a disaggregation of the monthly series. For more options on recession shading, see the note and links below. The recession shading data that we provide initially comes from the source as a list of dates that are either an economic peak or trough. We interpret dates into recession shading data using one of three arbitrary methods. All of our recession shading data is available using all three interpretations. The period between a peak and trough is always shaded as a recession. The peak and trough are collectively extrema. Depending on the application, the extrema, both individually and collectively, may be included in the recession period in whole or in part. In situations where a portion of a period is included in the recession, the whole period is deemed to be included in the recession period. The first interpretation, known as the midpoint method, is to show a recession from the midpoint of the peak through the midpoint of the trough for monthly and quarterly data. For daily data, the recession begins on the 15th of the month of the peak and ends on the 15th of the month of the trough. Daily data is a disaggregation of monthly data. For monthly and quarterly data, the entire peak and trough periods are included in the recession shading. This method shows the maximum number of periods as a recession for monthly and quarterly data. The Federal Reserve Bank of St. Louis uses this method in its own publications. The midpoint method is used for this series. The second interpretation, known as the trough method, is to show a recession from the period following the peak through the trough (i.e. the peak is not included in the recession shading, but the trough is). For daily data, the recession begins on the first day of the first month following the peak and ends on the last day of the month of the trough. Daily data is a disaggregation of monthly data. The trough method is used when displaying data on FRED graphs. A version of this time series represented using the trough method can be found at: https://fred.stlouisfed.org/series/USARECD The third interpretation, known as the peak method, is to show a recession from the period of the peak to the trough (i.e. the peak is included in the recession shading, but the trough is not). For daily data, the recession begins on the first day of the month of the peak and ends on the last day of the month preceding the trough. Daily data is a disaggregation of monthly data. A version of this time series represented using the peak method can be found at: https://fred.stlouisfed.org/series/USARECDP The OECD CLI system is based on the "growth cycle" approach, where business cycles and turning points are measured and identified in the deviation-from-trend series. The main reference series used in the OECD CLI system for the majority of countries is industrial production (IIP) covering all industry sectors excluding construction. This series is used because of its cyclical sensitivity and monthly availability, while the broad based Gross Domestic Product (GDP) is used to supplement the IIP series for identification of the final reference turning points in the growth cycle. Zones aggregates of the CLIs and the reference series are calculated as weighted averages of the corresponding zone member series (i.e. CLIs and IIPs). Up to December 2008 the turning points chronologies shown for regional/zone area aggregates or individual countries are determined by the rules established by the National Bureau of Economic Research (NBER) in the United States, which have been formalized and incorporated in a computer routine (Bry and Boschan) and included in the Phase-Average Trend (PAT) de-trending procedure. Starting from December 2008 the turning point detection algorithm is decoupled from the de-trending procedure, and is a simplified version of the original Bry and Boschan routine. (The routine parses local minima and maxima in the cycle series and applies censor rules to guarantee alternating peaks and troughs, as well as phase and cycle length constraints.) The components of the CLI are time series which exhibit leading relationship with the reference series (IIP) at turning points. Country CLIs are compiled by combining de-trended smoothed and normalized components. The component series for each country are selected based on various criteria such as economic significance; cyclical behavior; data quality; timeliness and availability. OECD data should be cited as follows: OECD Composite Leading Indicators, "Composite Leading Indicators: Reference Turning Points and Component Series", http://www.oecd.org/std/leading-indicators/oecdcompositeleadingindicatorsreferenceturningpointsandcomponentseries.htm (Accessed on date)

OECD Data Filters: REF_AREA: DEU MEASURE: IRSTCI UNIT_MEASURE: PA ACTIVITY: _Z ADJUSTMENT: _Z TRANSFORMATION: _Z FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

OECD Data Filters: REF_AREA: HUN MEASURE: CPI UNIT_MEASURE: PD METHODOLOGY: N EXPENDITURE: _T ADJUSTMENT: N TRANSFORMATION: GOY FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

OECD Descriptor ID: NAEXCP01 OECD unit ID: USD OECD country ID: USA All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

Data Are Smoothed By Macaulay'S Forty-Three Term Graduation (See Measuring Business Cycles By Burns And Mitchell, Chapter Eight). Source: F.R. Macaulay, The Movements Of Interest Rates, Bond Yields, And Stock Prices, NBER No. 33, 1938, Table 21, Col. 2. This NBER data series m13001a appears on the NBER website in Chapter 13 at http://www.nber.org/databases/macrohistory/contents/chapter13.html. NBER Indicator: m13001a

This Series Was Calculated By NBER By Adding The Number Of Beehive And Byproduct Coke Ovens. Source: NBER This NBER data series a01163a appears on the NBER website in Chapter 1 at http://www.nber.org/databases/macrohistory/contents/chapter01.html. NBER Indicator: a01163a

After June, 1913, The United States Department Of Commerce And Labor (One Of The Collectors Of The Data Presented Here) Is Named The Department Of Commerce. After April, 1941, The Bureau Of Foreign And Domestic Is Replaced As The Collector Of Data By The Bureau Of The Census. The November, 1872 Figure Was Misprinted In The Source To Read 50.45. Here It Is Given As 50.2. Beginning With July, 1950, Military Grand-Aid Shipments Have Been Eliminated From Export Figures; Other Government Shipments, Such As Various Types Of Civilian Aid Are Included In Exports. Certain Procedural Changes Resulted In A Higher Carry-Over Into The Succeeding Month Respectively In January 1955, April 1955, And January 1957. This Affects The Comparability From Month To Month In The Months Mentioned. The Ammounts Are $15-$25 Millions In January 1955, $20-$25 Millions In April 1955, And Approximately $30 Millions In January 1957 (See Foreign Trade Statistics Notes, April And June 1955, And March 1957 As Well As Ft 900, April 1955 And Ft 900-E, January 1958. Accelerated Vessel Loading Took Place In September 1959 In Anticipation Of The Longshoremen'S Strike. Merchandise Loaded In September On Vessels Departing October 1St Or Later Is Included In The October Figures. The Data For January And February, 1963 Reflect The Dock Strike Effect Shown In Increased Shipments Prior To The Start Of The Strike. December 1964 Through April 1965 Data Were Influenced By A Dock Strike At East Coast And Gulf Coast Ports (See Ft 900-E, November 24, 1965). Ft 900-E And Ft 900-I Have Been Combined Into One Report, Ft 900, Export And Merchandise Trade, Beginning In May, 1968. Source: 1866-1909 Data Are From Monthly Sumary Of Commerce And Finance, December, 1910 Supplement "Calendar Year Statements, " 1910, Pp. 1120-1126. 1910-June, 1914 Data Are From Successive Issues Of Monthly Summary Of Commerce And Finance. Beginning With July, 1914 Entries, The Data Are From Monthly Summary Of Foreign Commerce, Successive Issues. 1942-1944 Data Are From Foreign Commerce And Navigation; Beginning With 1945, The Data Are From Monthly Summary Of Foreign Commerce Of The United States (Issued Quarterly Beginning In April, 1951. Beginning With 1946 Data, See Summary Report Ft 900-E, Total Export Trade. Revised Back Figures May Be Found In World Trade Information Service Statistical Reports, Part 3, Total Export And Import Trade Of The United States (Through The 1962 Issues); United States Department Of Commerce, Overseas Business (Bureau Of International Commerce) 1962-1965 Issues; Bureau Of The Census, Highlights Of United States Export And Import Trade (Ft 990), Monthly, Beginning In 1967. Scattered Revisions For 1946-1963 Are From 1963 Issue Of Foreign Commerce And Navigation (Preprint Table 2/21/66). This NBER data series m07023 appears on the NBER website in Chapter 7 at http://www.nber.org/databases/macrohistory/contents/chapter07.html. NBER Indicator: m07023

The count of active single-family and condo/townhome listings for a given market during the specified month (excludes pending listings). With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of active single-family and condo/townhome listings for a given market during the specified month (excludes pending listings). With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

Indeed calculates the index change in seasonally-adjusted job postings since February 1, 2020, the pre-pandemic baseline. Indeed seasonally adjusts each series based on historical patterns in 2017, 2018, and 2019. Each series, including the national trend, occupational sectors, and sub-national geographies, is seasonally adjusted separately. Indeed switched to this new methodology in December 2022 and now reports all historical data using this new methodology. Historical numbers have been revised and may differ significantly from originally reported values. The new methodology applies a detrended seasonal adjustment factor to the index change in job postings. For more information, see Frequently Asked Questions (https://www.hiringlab.org/indeed-data-faq/) regarding Indeed Data. Copyrighted: Pre-approval required. Contact Indeed to request permission to use the data at their contact information provided here (https://github.com/hiring-lab/data#readme). End Users are excluded of any warranty and liability on the part of Indeed for the accuracy of the Indeed Data. End Users will refrain from any external distribution of Indeed Data except in oral or written presentations, provided that such portions or derivations are incidental to and supportive of such presentations and, provided further that the End Users shall not distribute or disseminate in such presentations any amount of Indeed Data which could cause such presentations to be susceptible to use substantially as a source of, or substitute for Indeed Data. End Users agree to credit Indeed as the source and owner of the Indeed Data when making it available to third parties in any permissible manner as well as in internal use. End Users agree to not sell or otherwise provide the Indeed Data obtained from Licensee to third parties.

Copyright, 2016, Automatic Data Processing, Inc. ("ADP").

OECD Descriptor ID: NAEXCP02 OECD unit ID: CNY OECD country ID: CHN All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

OECD Descriptor ID: NAEXCP02 OECD unit ID: TRY OECD country ID: TUR All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

Notes regarding this series can be found in International Financial Statistics Yearbooks produced by the International Monetary Fund (IMF). We have requested these publications from the IMF. Notes on this series will populate once they become available. Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available at http://www.imf.org/external/terms.htm.

Copyright, 2016, OECD. Reprinted with permission. All OECD data should be cited as follows: OECD (2010), OECD National Accounts Statistics, http://dx.doi.org/10.1787/na-data-en, (accessed on date)

No Quotation For August, 1910. Figure For July, 1916, Interpolated By War Industries Board. Data For 1916-1918 Checked With The U.S. War Industries Board, Price Bulletin No. 15, P. 11, Which Is The Original Source For These Years Average Price Per Pound, F.O.B. Southeastern Mills. Page 58 Of Statistical Bulletin No. 24, Usda Also Has A Price Series For Crude, S.E. But Gives An Average Of The High And Low Price Per Pound Each Month. Telephone Conversation With An Oils Specialist At Oil, Paint, And Drug Reporter, On December 5, 1938, Revealed: A) Crude Cottonseed Oil Is And Always Has Been Sold In Tanks, The B.A.E. Note To The Contrary (Agricultural Statistics, 1938, P. 116) Notwithstanding. The B.A.E. Error Probably Arose Because Of The Manner In Which Prices Of Crude And Refined Oil Are Quoted Near Each Other. Refined Oil Was Sold In Tanks At Times And In Barrels At Other Times; B) The Oil, Paint, And Drug Reporter Has Used Slightly Different Wording In Describing The Market At Different Times, But Actually No Change In Market Quoted Has Taken Place. F.O.B. Mills, Southeastern, And Immediate Southeastern, All Refer To Spot Sales At The Southeastern Mills. "January, 1929-July, 1930 Quoted In Barrels," A Footnote In The Source Is Wrong According To Oil, Paint, And Drug Reports Office And By Inspection Of This Original Source. Interpolated Data Used For June-July, 1926, July, 1927, June- August, 1928, June-August, 1929, And July-August, 1931. Less Than 10 Quotations For Month Of December, 1931. Other Quotations Were Bids. Source: Yearbook Of Agriculture, 1930 (For Data 1909-1928) P. 695, Agricultural Statistics, 1938 (For 1929-1938) P. 116, Bureau Of Agricultural Economics: Prices 1916-1918 From War Industries Board Price Bulletin No. 15. Prices For 1919-1928 Are Averages Of Weekly Quotations In The "Oil, Paint, And Drug Reporter." From August, 1928 Through July, 1934, Prices Are Averages Of Daily Quotations In The "Oil, Paint, And Drug Reporter" Except For The Period October, 1932-June, 1933, When The"Oil, Paint, And Drug Reporter" Published No Prices For Crude Cottonseed Oil At South- Eastern Mills. For That Period,(10/1932-06/1933), The "New York Journal Commerce" Daily Prices Were Averaged And Used. For August, 1934 Through 1937, Prices Are Averages Of Quotations For Saturday During The Month-- Quotations Given In The "Oil, Paint, And Drug Reporter." This NBER data series m04081ba appears on the NBER website in Chapter 4 at http://www.nber.org/databases/macrohistory/contents/chapter04.html. NBER Indicator: m04081ba

Source Code: Q:MY:N:368 Coverage includes all types of new and existing dwellings in the whole country. For more information, please see https://www.bis.org/statistics/pp_detailed.htm. Any use of the series shall be cited as follows: "Sources: National sources, BIS Residential Property Price database, http://www.bis.org/statistics/pp.htm." Copyright, 2016, Bank for International Settlements (BIS). Terms and conditions of use are available at http://www.bis.org/terms_conditions.htm#Copyright_and_Permissions.

Nominal effective exchange rates are calculated as geometric weighted averages of bilateral exchange rates. Copyright, 2016, Bank for International Settlements (BIS). Terms and conditions of use are available at http://www.bis.org/terms_conditions.htm#Copyright_and_Permissions.

OECD Descriptor ID: CPALTT01 OECD unit ID: PC OECD country ID: FRA All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

OECD Descriptor ID: CPALTT01 OECD unit ID: PC OECD country ID: ITA All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

Yield to maturity on accrued principal. Treasury Inflation-Protected Securities, or TIPS, are securities whose principal is tied to the Consumer Price Index (CPI). The principal increases with inflation and decreases with deflation. When the security matures, the U.S. Treasury pays the original or adjusted principal, whichever is greater. Copyright, 2016, Haver Analytics. Reprinted with permission.

The observations for the Dow Jones Utility Average represent the daily index value at market close. The market typically closes at 4 PM ET, except for holidays when it sometimes closes early. Originally made up of 18 utility stocks, the Dow Jones Transportation Average provides a view of the 15 different utility companies in US stock market. For more information, please visit Dow Jones Utility Average (http://us.spindices.com/indices/equity/dow-jones-utility-average). Copyright © 2016, S&P Dow Jones Indices LLC. All rights reserved. Reproduction of Dow Jones Utility Average in any form is prohibited except with the prior written permission of S&P Dow Jones Indices LLC (S&P). S&P does not guarantee the accuracy, adequacy, completeness or availability of any information and is not responsible for any errors or omissions, regardless of the cause or for the results obtained from the use of such information. S&P DISCLAIMS ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE. In no event shall S&P be liable for any direct, indirect, special or consequential damages, costs, expenses, legal fees, or losses (including lost income or lost profit and opportunity costs) in connection with subscriber's or others' use of Dow Jones Utility Average. Permission to reproduce this series can be requested from index_services@spdji.com. More contact details are available here (http://us.spindices.com/contact-us), including phone numbers for all regional offices.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The Harmonized Index of Consumer Prices category Transport (07) is a classification of durable goods, nondurable goods, semi-durable goods, services, and energy. This category contains Purchase of Vehicles (07.1), Operation of Personal Transport Equipment (07.2), and Transport Services (07.3) categories and all subcategories therein. Information provided in the notes pertaining to HICP classifications can be found from the source here (https://ec.europa.eu/eurostat/cache/metadata/en/prc_hicp_esms.htm). Copyright, European Union, 1995-2016 (https://ec.europa.eu/info/legal-notice_en#copyright).

The count of active single-family and condo/townhome listings for a given market during the specified month (excludes pending listings). With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

OECD Data Filters: REF_AREA: USA MEASURE: EMP_WAP UNIT_MEASURE: PT_WAP_SUB TRANSFORMATION: _Z ADJUSTMENT: Y SEX: _T AGE: Y55T64 ACTIVITY: _Z FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

OECD Data Filters: REF_AREA: CZE MEASURE: IR3TIB UNIT_MEASURE: PA ACTIVITY: _Z ADJUSTMENT: _Z TRANSFORMATION: _Z FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

Copyright, 2016, Automatic Data Processing, Inc. ("ADP").

Notes regarding this series can be found in International Financial Statistics Yearbooks produced by the International Monetary Fund (IMF). We have requested these publications from the IMF. Notes on this series will populate once they become available. Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available at http://www.imf.org/external/terms.htm.

Data Represent Prime Bankers' Acceptances, Ninety Days. Data For 1918-1950 Are Averages Of Weekly Prevailing Rates Calculated On The Basis Of Frequency Of A Single Rate Or A Range Of Rates Occurring During The Month. Data For 1951-1965 Are Averages Of Daily Quotations Computed By The Federal Reserve Board (See Bulletin Of November 1954). Revisions Prior To 1954 Are From The Survey Of Current Business 1955 Statistical Supplement, P. 82. Source: Board Of Governors Of The Federal Reserve System, Data For 1917-1941: Banking And Monetary Statistics. Data For 1942-1965: Federal Reserve Bulletin. This NBER data series m13007 appears on the NBER website in Chapter 13 at http://www.nber.org/databases/macrohistory/contents/chapter13.html. NBER Indicator: m13007

Observations for the current and future years are projections. The IMF provides these series as part of their Regional Economic Outlook (REO) reports. These reports discuss recent economic developments and prospects for countries in various regions. They also address economic policy developments that have affected economic performance in their regions and provide country-specific data and analysis. For more information, please see the Regional Economic Outlook (https://www.imf.org/en/publications/reo) publications. Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available here (http://www.imf.org/external/terms.htm).

Series Is Presented Here As Two Variables--(1)-- Original Data, 1842-1944 (2)--Original Data, 1938-1952. "Statistics Of Wheat Prices For The Period 1841-1870 Were Taken From Newspapers Of That Period, Namely The American, The Democrat, And The Tribune; From The Annual Review Of The Trade And Commerce Of Chicago, Published By The Tribune, And All Found In The Archives Of The Chicago Historical Society. The Statistics For The Period 1871-1922 Are All From The Annual Reports Published By The Chicago Board Of Trade." (J.E. Boyle In Source). Average Monthly Price Is Computed (NBER) By Averaging Monthly High And Low. In All Cases The Effort Was Made To Use That Grade Of Wheat In Which The Most Transactions Were Had. On This Basis, Therefore, These Grades Were Used. (All Cash Prices.): 1841-57, Spring Wheat; 1858-59, Standard Spring; 1860-63, No. 2 Spring; 1864-1870, No. 1 Spring; 1871-1897, No. 2 Spring; 1898-1904,"Regular" Wheat (Deliverable On Contracts); 1905-1918, No. 2 Red Winter; 1919-1920, No. 2 Northern, 1921-1922, No. 2 Red Northern. Future Trading In Wheat At The Chicago Board Of Trade Ceased August 25, 1917, And Was Resumed July 15, 1920. The Price Of Cash Wheat Was Fixed By The Government Beginning In September, 1917. Government Control Of Cash Wheat Ceased On June 30, 1920. Beginning In 1883, The Basic Cash Price Of Wheat Is That Spot Price Of Such Wheat As Is Being Delivered On Chicago Future Contracts, Or Is Expected To Be Delivered On Them, Adjusted For Any Premium Or Discount Applicable On Delivery. Listing Of Prices Was Discontinued In The Wheat Studies For May, 1944, Vol Xx, No. 5. June And July Prices Are From "World Grain Review And Outlook, 1945," P.281. Source: James E. Boyle, Chicago Wheat Prices For Eighty-One Years, Pp. 69-71, For 1841-1882; Food Research Institute, Stanford University, "Wheat Studies, " November, 1934, P.118 And The Following December Issues For 1883-1944. This NBER data series m04001a appears on the NBER website in Chapter 4 at http://www.nber.org/databases/macrohistory/contents/chapter04.html. NBER Indicator: m04001a

This series appears in Table 18. Source Code: Q:CN:3P:J:1:1:A:A:TO1:A:A:A:A:A:I Nationality refers to the ultimate obligor, as opposed to the immediate borrower on a residence basis, and is linked to the consolidation of assets and liabilities for related entities. Information on a nationality basis is useful to analyse potential support that might be available from the parent company and to understand links between borrowers in different countries and sectors. For example, the debts of a Cayman Islands subsidiary of a Brazilian bank may be guaranteed by the parent bank. Consistent with the approach taken in the international banking statistics, the BIS bases the nationality of an issuer on the residency of its controlling parent, regardless of any intermediate owners. (December 2012, BIS Quarterly Review, https://www.bis.org/publ/qtrpdf/r_qt1212h.pdf) Copyright, 2016, Bank for International Settlements (BIS). Terms and conditions of use are available at http://www.bis.org/terms_conditions.htm#Copyright_and_Permissions.

The median listing price in a given market during the specified month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The median listing price in a given market during the specified month. With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

The count of active single-family and condo/townhome listings for a given market during the specified month (excludes pending listings). With the release of its September 2022 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology updates and improves the calculation of time on market and improves handling of duplicate listings. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since October 2022 will not be directly comparable with previous data releases (files downloaded before October 2022) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/). With the release of its November 2021 housing trends report, Realtor.com® incorporated a new and improved methodology for capturing and reporting housing inventory trends and metrics. The new methodology uses the latest and most accurate data mapping of listing statuses to yield a cleaner and more consistent measurement of active listings at both the national and local level. The methodology has also been adjusted to better account for missing data in some fields including square footage. Most areas across the country will see minor changes with a smaller handful of areas seeing larger updates. As a result of these changes, the data released since December 2021 will not be directly comparable with previous data releases (files downloaded before December 2021) and Realtor.com® economics blog posts. However, future data releases, including historical data, will consistently apply the new methodology. More details are available at the source's Real Estate Data Library (https://www.realtor.com/research/data/).

OECD Data Filters: REF_AREA: TUR MEASURE: PRVM UNIT_MEASURE: IX ACTIVITY: BTE ADJUSTMENT: Y TRANSFORMATION: _Z TIME_HORIZ: _Z METHODOLOGY: N FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

OECD Descriptor ID: CCUSSP01 OECD unit ID: XDR OECD country ID: SDR All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

Earnings Is Refered To As Earnings Price Ratio In The Source. Source: Cowles Commission For Research In Economics, Monograph No. 3, Common Stock Indexes, 1939, Table R-1, P. 404-405. This NBER data series a13049 appears on the NBER website in Chapter 13 at http://www.nber.org/databases/macrohistory/contents/chapter13.html. NBER Indicator: a13049

Notes regarding this series can be found in International Financial Statistics Yearbooks produced by the International Monetary Fund (IMF). We have requested these publications from the IMF. Notes on this series will populate once they become available. Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available at http://www.imf.org/external/terms.htm.

Future Prices Paid for Raw Materials reports the likely direction of prices paid for raw materials for the manufacturing sector over the six months ahead for the state of Texas. Survey responses are used to calculate an index for each indicator. Each index is calculated by subtracting the percentage of respondents reporting a decrease from the percentage reporting an increase. When the share of firms reporting an increase exceeds the share of firms reporting a decrease, the index will be greater than zero, suggesting the indicator has increased over the prior month. If the share of firms reporting a decrease exceeds the share reporting an increase, the index will be below zero, suggesting the indicator has decreased over the prior month. An index will be zero when the number of firms reporting an increase is equal to the number of firms reporting a decrease. For further information regarding the Texas Manufacturing Outlook Survey release from the Federal Reserve Bank of Dallas visit: https://www.dallasfed.org/research/surveys/tmos.aspx#tab-reports.

M1 comprises notes and coins in circulation outside banking corporations and demand and savings deposits of households, nonfinancial corporations, local governments, securities companies, Tanshi companies, and some other financial corporations such as securities finance companies with banking corporations in national currency. The banking corporations surveyed comprise the BOJ, domestically licensed banks (excluding Japan Post Bank), foreign-owned banks in Japan, Shinkin Central Bank, Shinkin banks, Norinchukin Bank, and Shoko Chukin Bank. Beginning in April 2003, M1 comprises notes and coins in circulation outside depository corporations and demand and savings deposits of households, nonfinancial corporations, local governments, and some other financial corporations such as securities finance companies with depository corporations in national currency. The depository corporations surveyed comprise the above-mentioned "banking corporations", Japan Post Bank, Shinkumi Federation Bank, Shinkumi banks, Rokinren Bank, labor banks, Prefectural Credit Federations of Agricultural Cooperatives, agricultural cooperatives, Prefectural Credit Federations of Fishery Cooperatives, and fishery coops. Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available at http://www.imf.org/external/terms.htm.

OECD descriptor ID: LFWA64TT OECD unit ID: STSA OECD country ID: EU27 All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database),http://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission.

OECD Data Filters: REF_AREA: USA MEASURE: WAP UNIT_MEASURE: PS TRANSFORMATION: _Z ADJUSTMENT: Y SEX: M AGE: Y25T54 ACTIVITY: _Z FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

OECD Data Filters: REF_AREA: ISR MEASURE: CPI UNIT_MEASURE: PD METHODOLOGY: N EXPENDITURE: _T ADJUSTMENT: N TRANSFORMATION: GOY FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

Series Is Presented Here As Four Variables-- (1)--Original Data, 1890-1918 (2)--Original Data, 1914-1928 (3)-- Original Data, 1926-1951 (4)--Original Data, 1947-1958. Specifications From 1947 On Changed To Price Quoted For "Standard Newsprint, Rolls, Contract, Delivered To Principal Points." Price Quoted Per Ton, Here Converted To Price Per Pound. Average Of Weekly Quotations Prior To 1952, Mid-Month Quotation Thereafter. In February, 1956, There Was A Slight Change In The Series, Code # 09-32-01.01; See Bls Bulletin No. 1214, P. 280, Footnote 5. Source: Bls, Prices An Price Relatives For Individual Commodities In The Revisied Index, 1947-1950, Group 9: Pulp, Paper, And Allied Products (November, 1952) For 1947-1950; Monthly "Wholesale Prices And Price Indexes", And Survey Of Current Business, 1955 And 1957 Statistical Supplements, Thereafter; See Also Bls, Bulletin No. 1214 For 1954-1956. This NBER data series m04093d appears on the NBER website in Chapter 4 at http://www.nber.org/databases/macrohistory/contents/chapter04.html. NBER Indicator: m04093d

OECD Data Filters: REF_AREA: AUT MEASURE: CPI UNIT_MEASURE: PD METHODOLOGY: N EXPENDITURE: _T ADJUSTMENT: N TRANSFORMATION: GOY FREQ: M All OECD data should be cited as follows: OECD (year), (dataset name), (data source) DOI or https://data-explorer.oecd.org/ (https://data-explorer.oecd.org/). (accessed on (date)).

OECD Descriptor ID: ODCNPI03 OECD unit ID: PC OECD country ID: GBR All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

OECD Descriptor ID: LRUNTTTT OECD unit ID: PC OECD country ID: CAN All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

OECD Descriptor ID: MABMM301 OECD unit ID: NOK OECD country ID: NOR All OECD data should be cited as follows: OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database), https://dx.doi.org/10.1787/data-00052-en (Accessed on date) Copyright, 2016, OECD. Reprinted with permission

Value represents the benchmark prices which are representative of the global market. They are determined by the largest exporter of a given commodity. Prices are period averages in nominal U.S. dollars. Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available at http://www.imf.org/external/terms.htm.